Omni-IML: Towards Unified Interpretable Image Manipulation Localization
Authors: Chenfan Qu
Yiwu Zhong
Fengjun Guo
Lianwen Jin
Venue: ICLR 2026 Poster (2026)
Keywords: document analysis
tampered text detection
vision foundation model
Primary area: foundation or frontier models, including LLMs
TL;DR: The first generalist Image Manipulation Localization (IML) model that unifies interpretable IML on four major IML domains, along with a high-quality dataset constructed by a novel method.

Abstract: Existing Image Manipulation Localization (IML) methods rely heavily on task-specific designs, making them perform well only on the target IML task, while joint training on multiple IML tasks causes significant performance degradation, hindering real applications. To this end, we propose Omni-IML, the first generalist model designed to unify IML across diverse tasks. Specifically, Omni-IML achieves generalization through three key components: (1) a Modal Gate Encoder, which adaptively selects the optimal encoding modality per sample, (2) a Dynamic Weight Decoder, which dynamically adjusts decoder filters to the task at hand, and (3) an Anomaly Enhancement module that leverages box supervision to highlight the tampered regions and facilitate the learning of task-agnostic features. Beyond localization, to support interpretation of the tampered images, we construct Omni-273k, a large high-quality dataset that includes natural language descriptions of tampered artifacts. It is annotated through our automatic, chain-of-thoughts annotation technique. We also design a simple-yet-effective interpretation module to better utilize these descriptive annotations. Our extensive experiments show that our single Omni-IML model achieves state-of-the-art performance across all four major IML tasks, providing a valuable solution for practical deployment and a promising direction of generalist models in image forensics. Our code and dataset are available at https://github.com/qcf-568/OmniIML.

Published as a conference paper at ICLR 2026
OMNI-IML: TOWARDS UNIFIED INTERPRETABLE IMAGE MANIPULATION LOCALIZATION
Chenfan Qu', Yiwu Zhong", Fengjun Guo Lianwen Jin 1A- South China University of Technology, "Peking University,
3 INTSIG Information Co., Ltd,
*INTSIG-SCUT Joint Lab on Document Analysis and Recognition
202221012612 scut. edu. en, eelwjinescut edu. cn
ABSTRACT
Existing Image Manipulation Localization (IML) methods rely heavily on task- specific designs, making them perform well only on the target IML, task, while joint training multiple IML tasks causes significant performance degradation, hindering real applications. To this end, we propose Omni-IML, the first gener- alist model designed to unify IML across diverse tasks. Specifically, Omni-IML achieves generalization through three key components: (1) a Modal Gate En- coder, which adaptively selects the optimal encoding modality per sample, (2) Dynamic Weight Decoder, which dynamically adjusts decoder filters to the task at hand, and (3) an Anomaly Enhancement module that leverages box supervi- sion to highlight the tampered regions and facilitate the learning of task-agnostic features. Beyond localization, to support interpretation of the tampered images, we construct Omni-273k, a large high-quality dataset that includes natural lan- guage descriptions of tampered artifacts. It is annotated through our automatic, chain-of-thouggh annotation technique. We also design simple-yet-effective in- terpretation module to better utilize these descriptive annotations. Our extensive experiments show that our single Omni-IML model achieves state-of-the-ar per- formance across all four major IML tasks, providing a valuable solution for practi- cal deployment and promising direction of generalist models in image forensics. Our code and dataset are available at https:/ggihubbcoo//fff666/////0
INTRODUCTION
Manipulated images can pose serious risks social media security. Despite the progress made in recent years, existing IML models are designed for individual IML tasks on specific image types (e.g., natural image or document) and usually fall short other IML tasks. Consequently, the maintenance costs are high since every IMI. task requires an independent, task-specific IMI. model.
A naive solution is jointly train IML model using the data of all available IML tasks. However, joint training usually leads to an obvious performance degradation on all IML tasks, making the predictions unreliable. For example, HiFiiNet Guo et (2023) suffers from joint training and thus uses two different sets of model parameters for natural image IML and face IML, respectively. There are main reasons why existing IML methods still cannot provide generalization across different IML tasks after joint training:
First, existing IML methods heavily rely on task-dependent architecture designs, training strategies etc. to detect tampering clues. These designs work well for the target IML task, but usually fall short on other IML tasks. For example, edge anomaly enhancement modules Dong et al. (2022); Yu et al.) (2024) and object attention modules Wang et al. (2022a): Li et al. (2024b) have made significant progress identifying forged natural objects. However, they can hardly work well on document images where edge artifacts are less obvious and object features are not distinct. Early frequency-
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Published as a conference paper at ICLR 2026
vision Qu et al. (2023) fusion performs well on document images but has obvious performance degradation on natural images that cover much more noise and diversity.
Second, existing IML methods lack the design to distinguish diverse tampering features across dif- ferent IML tasks. The IML task is challenging since the tampering methods are diverse and produce different subtle tampering cues. It is even harder to handle various IML tasks with unified model. Models can be easily confused when learning to distinguish various tampering features.
To address the above issues, pro- pose Omni-IML, the first generalist model that simultaneously per- form well on multiple major IML tasks, as shown in Figure [ Specif- ically, a Modal Gate Encoder is pro- posed to automatically select the op- timal encoding modality for each in- put sample, based on the characteris- tics of the input image. And Dy- namic Weight Decoder is proposed to adaptively select the optimal decoder filters for each sample, assisting the generalist model better cope with the highly diverse tampering features from multiple image types. These sample-adaptive designs can provide flexibility so that the model can adapt each input sample and achieve generalization. Further, an Anomaly Enhancement is introduced to enhance the features of tampered regions with a novel box supervision design.
Difforent IML tasks
ALL SOTA performance
Omni-IML
Artifact description capability
Tanqurod Segion
snttere
Seruntic Anthacti
Figure 1: The proposed Omni-IML simultaneously achieve state-of-thh-ar art on multiple major IML tasks, with- task-specific and benchmark-specifc fine-tuning.
In addition to merely localizing tampered regions, we also seek to improve model reliability by en- abling the language description of both visual and semantic artifacts of the tampered image. Despite the recent progress in interpretable IML on natural images, there still lacks unified benchmark for interpretable IML on documents, scene text images and uncut deepfake images. The interpretable IML annotation generation pipelines with GPT-4o in previous works Xu et al. (2024); Huang et al. (2024) work well on natural images, but do not work well on documents and scene texts, where a tampered image usually contains multiple tampered regions and the image artifacts less obvi- ous Qu et al.) 2023). There are two main reasons behind this gap: First, previous methods obtain the content, position and artifact descriptions of the tampered object with only single query GPT-4o. When there are multiple tampered objects in one image, GPT-4o is easily distracted and messes up the descriptions for different targets. Second, when the image artifacts less obvious, GPT-4o's response will be less confident and even incorrect, resulting in low-quality annotations.
To this end, we propose a novel chain-of- of-thoughts pipeline, which solves the above issues through step-by-step focused analysis and self-examination. With the proposed method, we generate arti- fact descriptions for forged images from the natural, document, face, scene text IMI. domains, and construct large-scale, comprehensive, high-quality dataset Omni-273k. A novel structured annota- tion format is adopted, which enables more reasonable and in-depth evaluation. To make better use of our Omni-273k, we further introduce yetfffeyetteffective interpretation module that improves model's s artifact description performance through a reference visual prompt.
We validated our methods hundreds of representative IML tasks (e.g., IML certificates, prod- uet photos, artwork, street photos, cards, signs, group photos, receipts, etc.). which can catego- rized into four distinct major IML tasks, including natural image IML, document IML. face IML and scene text IMI,, covering the vast majority of the recent IMI. research. Extensive experi- ments the four major IML tasks show that joint training the existing IML methods on all tasks leads to significant performance degradation and the trained IML models are inadequate to handle multiple IML tasks simultaneously. While Omni-IML can minimize the performance degrada- tion and simultaneously achieve state-of-the art performance across all the four major IML tasks, demonstrating high scalability and effectiveness. Our main contributions are as follows:
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Localization
Coarse pred Posot
Bounding Box Supervision
Interpretation
Tampered Region 1
Module
Module
Detailed
Absolute Position: Ledl iid:
Artifacts
Reintive Pesitien:
Parception
Frequancyy
Rused
Descriptions
"13101007 2*15000"
Artifacts:
Backbone
Anomaly Enhancement
Large Language Modal
sighty the crooading nest neme sic and lat...
Modal
Gata
Dynamic Weight Decoder
Allgaenent Incombtteacy The algrenest "Ditteee"" des masd lecessitinny: treun of serpered repian appais diffrcnt...
Parception
Vision
Figh
Vision Tower
pred Prge
Pred mask
lil Sille
Figure 2: The overall framework of the proposed Omni-IMP
We propose Omni-IMI,, the first IML generalist model that unifies interpretable IMI. across four major domains, including natural, document, face and scene text IML.
Unified IML modeling is achieved by multiple novel effective modules, consisting of Modal Gate Encoder, Anomaly Enhancement, and Dynamic Weight Decoder.
Further, to interpret image artifacts in natural language.
propose novel chain-of-
thoughts annotation technique to automatically construct high-quality dataset Omni-273k. An effective interpretation module is also proposed to better leverage data.
Extensive experiments demonstrate that our single generalist model can simultaneously achieve state-of-the-art results across different major IML tasks.
2 RELATED WORKS
Natural IML aims to identify the tampered regions in daily-life style images. Existing methods leverage object-level attention WWng et al.] (2022)): Li et al. (2024b), edge artifacts Dong et al. 2022 Li al. (023) noise-domain filters Guillaro et al. (2023);
al. (2024a); Zhang al. 2025 for better generalization. These models perform well on natural images, but mostly not well
enough on other image types (e.g. documents), due to the absence of natural objects, edge and noise artifacts in these scenarios Qu et al. (2025a 2026). Although FakeShield Xu et al. (2024) achieves lmage-level forensics on both natural and face images, its pixel-level forgery localization ability still limited to natural images. Therefore, it is not the first IML generalist. Detailed in Appendix B
Document IML aims to localize the forged regions in document images Shao et al. (2024): Dong
et al. (2024), which often contain sensitive information Liu et al. 2023a): Hongliang et al. 2025
Existing methods utilize early fusion vision and frequency features to spot unobvious artifacts Qu
et al. (2023); Chen et al. 2024b). However, the model will be severely distorted in many cases of natural and face images where the frequency features are too noisy Yu et al. (2024).
Face IML aims to localize Al-generated fake faces. Existing methods harness metric learning Guo et al.
(023) or noise filters Liu et al. (2024) for better texture anomaly capturing. MoNFAP Miao 2024 These methods show generalization face IML but suboptimal natural and document images, where the tampered regions are small and the texture anomalies are unobvious.
al.
Scene Text IML aims localize tampered natural text in arbitrary styles and complex back- grounds. Previous works rely on specific noise patterns Wang et al. (202bb) or pre-training on authentic scene images Qu al. (2025b), limiting their generalization across other IML tasks.
METHODOLOGY
As shown in Figure 2 our Omni-IML has a localization module (left) to identify the tampered region, and an interpretation module (right) describe image artifacts in natural language.
The localization module is roughly based on an encoder-decoder architecture. The Modal Gate En- coder consists of: (1) Vision Perception Head extract visual features from the original images;
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Prass Happest
Modal Gate
Anomaly Enhancement Module
1K1
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Figure 3: Modal Gate (left), Anomaly Enhancement (middle) and Dynamic Weight Decoder (right).
(2) a Frequency Perception Head to extract frequency features; (3) Modal Gate to automatically determine the optimal modality for the following encoding process; (4) a backbone model to extract multi-scale high-level features. The Dynamic Weight Decoder adaptively selects the sample- wisse wise optimal decoder filters and outputs the final mask prediction. We also design an Anomaly Enhance- ment module between the encoder and decoder, enhance the features of tampered regions.
The interpretation module is an MLLM. Its input comprises both the original input image and a reference visual prompt constructed by highlighting the predicted mask on the input image.
3.1 MODAL GATE ENCODER
Key Idea. The frequency feature is a double-edged sword for the IML generalist: it can help to detect visually consistent tampering in some cases, but it may also degrade the model performance when the image is highly distorted. As a result, neither pure vision nor vision++rrquency modeling can consistently provide the optimal solution. To achieve general IMI. through flexible encoding modality, we propose the Modal Gate, which automatically determines the optimal encoding modal- ity (friquency+visiin or pure vision) for each input sample. The key idea of our Modal Gate Encoder to automatically identify the optimal modality by analyzing whether the frequency features contain too much noise, and which coarse prediction seems more confident and accurate.
Image Encoding. As shown in Figure we extract vision features Frgb using Vision Perception Head, extract frequency features using Frequency Perception Head, and obtain the fused features Fuseed by fusing Frgb and Freg with a conv-layer. The two perception heads consist of several conv-layers, with the same structure those in previous work Qu et al. (2023). Two coarse mask predictions Prgb and Prueed are obtained from Frgb and Frueed with two conv-layers respectively.
Modal Gate. As shown in the left of Figure 3 Frgb. Ffueeds Pryb and Prused are channel- concatenated and fed into the Modal Gate for optimal modality prediction. The Modal Gate is a binary classifier consisting of several conv-layers to determine whether to use Ffused or Frgb as the encoder input, by observing the noise level and the confidence of F Fjused. Frgb. and their corre- sponding coarse predictions Prob and Pjused- We present more details in the Appendix C.1
3.2 ANOMALY ENHANCEMENT
Different image types from different IML tasks produce different features, joint training brings much more noise to the features and confuses the IMI. model. To tackle this, we propose to enhance the contrast of forged regions, and improve the feature extraction across diverse image domains through introducing a novel box supervision, as shown in the middle of Figure 3 This method harnesses task synergism to advance our IML generalist. We present more details in the Appendix C.2
3.3 DYNAMIC WEIGIT DECODER
Key Idea. Different types of tampered images result in wide range of manipulation clues. For example, forged objects in natural images may have abnormal contrast or edge artifacts Wang et al.
(2022a). tampered text in document images might be visually consistent but has discontinuous DCT
in the frequency domain Qu et al. (2023), fake faces may have unnatural texture Guo et al. (2023). These wide variations in tampering clues further cause large variation of the encoded features of
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tampered regions. Merely using fixed set of filters for the decoder causes it to be confused by the diverse encoded features, especially in the unified training process. To this end, we propose to adaptively select the optimal decoder filters for each input image based on its characteristics. To achieve this, we propose the Dynamic Weight Decoder (DWD), as shown in the right of the F
Method. In the proposed Dynamic Weight Decoder, the low-level input features first fused with the high-level input features in a top-down manner to obtain multi-scale features F1.2.3,4- A global feature vector V, is obtained by average pooling Fi. The multi-scale features are channel dimension reduced and processed by series of Dynamic Weight Filters (DWFs) with different dilation rates. The output features are further processed by two DWFs to obtain the final prediction mask.
Dynamic Weight Filters. As shown in the top-right of Figure to obtain the dynamic filters, we first average the input feature to obtain a current global representation Ve (orange box), then interact V. with the global image vector Vg (blue box) with fully connected layer and identify the optimal dynamic filters Dopt by weighted summation of four common convolutional filters.
=i=1 A, Wi, where a is the sigmoid function, FC is a linear layer, W, is the ith filter in the DWF. Finally, we depthwise convolve the input feature with Dopt and then perform point-wise convolution with 1 x 1 conv-layer to obtain the output.
A; = of (VC((,, Vg)), Dapt = Ei
3.4 INTERPRETATION MODULE
To provide more reliable forgery analysis by describing the image artifacts in natural language, we introduce a simple-yet-effective interpretation module, illustrated on the right of Figure
Existing works Xu et al. (2024) directly input the tampered image into an MLLM to describe the im- age artifacts. However, due the challenging nature of image forensics, the MLLM often misiden- tifies the tampered region especially on multi-target and challenging scenarios (e.g. tampered doc- uments). To address this issue, we propose to draw MLLM's attention the suspect regions by presenting it with the forgery localization mask predicted by the mask decoder. However, directly inputting the binary mask will lead to considerable ambiguity in dense text and dense face images, since adjacent instances may have very similar positions.
To minimize the ambiguity and the difficulty in understanding the predicted tampered regions, we construct visual reference prompt Tee by highlighting the mask predictions mask on the tampered image Imput by pixel-wise weighting: I = (Limpat+ mmae))/2. We concatenate Iref with Impat along the longest side, and feed it into an MLLM for text prediction. In addition to clearly indicating the suspected region, our method can also minimize overfitting and forgetting in MLLM, since it does not change the original MLLM structure.
4 OMNI-273K DATASET
To enable the Omni-IMI for high-quality description of the tampered region, we construct the Omni- 273k dataset, by querying GPT--4o generate textual artifact descriptions for the tampered regions.
4.1 CHAIN-OF- THOUGHTS AUTOMATIC ANNOTATION
Motivation. Existing works generate textual descriptions for forged objects' content, position and artifact clues a forged image via a single prompt Xu et af.) (2024); [Huan et al.] (2024). These methods make progress on single-target and less challenging scenarios such natural object images, but do not work well on tampered documents, scene texts and uncut deepfakes where image artifacts are less obvious and a tampered image usually contains multiple tampered instances.
There are two main reasons for this: First, given the challenging nature of image forensics, accu- rately describing the content, location, and artifact of a tampered instance requires focused analysis. Simultaneous analysis of multiple targets leads to distractions, causing the model to confuse the artifacts among different objects. Second, for challenging samples such as forged documents, the response of GPT-4o is often unconfident and partially incorrect. These problems prevent previous methods from being an adequate solution for unified image forgery analysis.
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Shep Inatancx-wis was Tampared Object Recognition
Step 2: Focused Artificts Description
Step Examination
Method. To solve the above prob- lems and improve annotation qual- ity. We propose novel chain-of- thoughts pipeline, which consists of three steps, as shown in Figure
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Step 1. Instance-wise Tampered Object Recognition. In this step, we accurately recognize the content and position for each tampered re- gion. Given the binary mask anno- tation mask indicating the tampered region for a tampered image hamp. each of the connected components in mask is a tampered in- stance. For text images, the OCR result for each tampered instance is the content, which obtained through an OCR engine. For other images, we highlight the tampered regions in lomp respec- tively to get n highlight masks high We prompt GPT-4o with kamp and high sequentially for each tampered instance to get its content (e.g. "The rightmost basketball on the floor", "The face of the second young man in white shirts"). Along with recognizing the content, we also obtain the position for each tampered instance.
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Figure 4: Our Chain-of Mfain-of-Thhon ofoTuhhghss annotation pipeline.
Step 2. Focused Artifact Description. In this step, we obtain detailed descriptions of image arti- facts for each tampered instance. For each tampered instance, we prompt GPT-4o with Itamp, highh the previously recognized content and position, and an elaborate query (lists the common artifact perspectives) to obtain the detailed descriptions Tdes for low-level visual and high-level semantic artifacts. The full query is shown the Appendix [D.] During this artifact description process, the annotator GPT-4o is focused in two aspects: First, GPT-4o only focuses on one single tampered ob- ject, instead of all objects as in previous works. Second, except for the hamp, high we also provide the previously recognized contents and positions. GPT-4o can thus focus on the task of describing image artifacts, rather than also handling multiple recognition tasks at the same time as in previous works. Consequently, the hallucination is reduced and the annotation quality is improved.
Step 3. Self Examination. In challenging samples where image artifacts are not obvious, the response of GPT-4o can be unconfident and incorrect, while manual filtering is costly. To this end, propose to further improve the annotation quality by guiding GPT-4o to carefully examine and correct its previous response. For each of the tampered objects, we prompt GPT-4o with its previous response Taes, hump. Thigh and the previously recognized contents and positions, then show GPT- 4o an example containing both an unconfident, incorrect answer and the corresponding manually corrected one (shown in the Appendix D.I in detail). With this approach, GPT-4o's 's final response has considcrably improved quality.
Our method differs from previous methods through focused, step-by-step, self-examined analysis that significantly improves annotation quality to human-level in unified IML analysis.
4.2 STRUCTURED ANNOTATION FORMAT
Unlike previous works that used an unstructured string as the annotation format for the artifact description, we adopt a structured JSON format, enabling much more reasonable and detailed model evaluation. Our annotation for each sample is list of n items, where is the number of tampered instances in that image. Each item a dict with four key-value pairs:
Tampered Region, the value is the OCR of the tampered text or the description of the tampered object (e.g.. "A sleeping orange cat").
Absolute Position, the value is the position of the tampered region relative to the entire image (c.g., "Top left of the image").
Relative Position, the value is the OCR of the text line containing the tampered text, or position of the tampered object relative to other objects (e.g., "On the leftmost green table").
Artifacts, the value is the artifact description of the tampered region. It is also JSON diet, its keys are the titles for different artifacts (c.g. "Textural Artifacts", "Semantic Artifacts") and its values are the detailed descriptions under the titles.

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Compared to the unstructured string format used in all previous works Xu et al. (2024): Huang al. 2024), our novel structured JSON annotation offers two major advantages:
Reasonable Evaluation: Different item fields require different appropriate metrics. For example, tampered text recognition and position description are close-ended tasks. These should be evaluated using exact-match metrics, such as OCR accuracy classification accuracy. Conversely, tampered text artifact descriptions are open-ended. They necessitate evaluation with fuzzy matching metrics, like cosine similarity ROUGE. Previous unstructured string annotations, however, only support single metric for all item fields. This often leads to unreasonable evaluation results. For instance, entirely incorrect text recognition or position descriptions might still yield high scores under cosine similarity or ROUGE. In contrast, our structured annotation allows for the application of distinct metries to their respective item fields, thereby producing more accurate and fair evaluation outcomes.
Finer-Grained Analysis: Prior unstructured string annotations permit only very coarse model evaluation, assessing the entire output as single unit. Our structured annotation, conversely, en- ables much finer-grained analysis across specific components (e.g.. content, position, and artifact descriptions). Consequently, our approach can facilitate more in-depth analyses and provide signif- icantly greater insights behind the results.
Table 1: Dataset comparison with MMTD eu al. (024) and SID Huang et al. (022). 'CM', 'SP', "PT" are copy-move, splicing, printing. 'Mauua Num. is the number of real-world manual forgery.
Dataset MMTD
Image Types
Tampering Methods
Target type
Forgery Num.
Manual Num. Structured Label
Nate+Face
CM, SP, AIGC
Single
21. 101
5, 123
X
SID
Nat. Nat.+Face
AIGC:
Single
200, 000
x
CM, SP
Single and
Ours
269, 061
131, 658
+Doc.4.TT.
PT, AIGC
multiple
4.3
DATASET CONSTRUCTION AND HIGHLIGHTS
We apply our CoT pipeline on tampered images from the commonly used datasets across the four IML domains and obtain about 273k samples. Samples and more details are presented in the Ap- pendix E
As shown in Table I our Omni-273k is the first one that simultaneously covers all four major IML domains, significantly outperforming prior works in multiple perspectives:
Large-Scale: Our Omni-273k has the largest scale and 20x more real-world manual forgeries than previous works, making it much more effective in evaluating real-world model performance.
Comprehensive: Ours includes all the four major image domains, the most diverse tampering methods, and contains both single- and multi-target (>> tampered objects in an image) samples.
HighhQuality: Ours is the first include structured annotation format to enable reasonable, fine- grained analysis. Ours is initially constructed and cleaned by our CoT pipeline to ensure quality.
Table Comparison on natural and document IML with pixel-level IoU metric. 'DT": DomTarrer.
Natural IML Task
Document IML Task
Methods
CASIAI
Coverage NISTI6 IMD20 CocoGlide Avg.
Methods MVSS-Net
SACP De-Test DT-FCD DT-SCD
RRU-Net
.330
.165
.080
.169
.223
206
.401
MVSS-Net
.403
.389
.23
.243
.276
.299
RRU-Net
.517
PeC
.410
.340
.067
.115
.333
.855
CFL.Nett
.433
CAT-Net
.684
.238
.238
.406
TIFDM
.76
IF-OSN
.465
.247
.259
.207
.292
MVSS-Net
.430
.410
4400
EVP
.438
.078
.188
.177
.084
.184
PSCC-Net
.170
.160
.190
TruFor
.630
.446
.279
408
Swin-UPer
.700
.410
.10
APSC-Net
.100
.498
525
.679
.392
552
CAT-Net
.710
.600
540
FakeShield
.540
.357
.320
.500
.523
.48
DTD
.828
.749
.691
ViT
.688
.456
.322
.327
.68
SpT
.644
.411
.466
PIM
.122
.188
.255
.340
.327
.398
CAFTB
.617
.402
.435
Ours
.800
.538
.540
.662
.538
.612
Ours
664
.859
.863
.676

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Table Comparison study of models trained specific task and all tasks. Pixel-level IoU metric.
Trained task
Trained on all tasks
Method (#Model domain)
Natural
Document
Scene Text
Face
Natural
Document Scene Text Face Average
CAT-Net (Natural)
462
.89
.097
.122
.437
.501
.530
.874
.583
TruFor (Natural)
485
.054
.079
.098
.453
.485
.542
.898
.595
APSC-Net (Natural)
.552
.047
.067
.076
.511
.607
.558
.901
.644
Via (Natural)
.483
.042
.081
.080
.450
.499
.529
.886
.591
FakeShield (Natural)
.014
.055
.099
.442
.316
.330
.829
TIFDM (Document)
.020
.498
.044
.001
.382
.428
.489
.834
.533
DTD (Document)
.043
.355
.069
.002
.396
.452
.481
.822
.538
Swin-UPer (ext)eText)
0055
.033
.886
.037
.425
.477
.525
.850
.569
UPOCR (extneTTex)
.067
.049
.543
.015
.401
.430
.505
.869
.551
HiFi-Net (Face)
.003
.012
.747
.271
.373
.289
.712
MoNFAP (Face)
.090
.022
.004
.922
.398
.467
.499
.845
.552
Ours (Natural)
.628
.063
.092
.144
Ours (Document)
.061
.774
.087
.002
Ours (ext)eText)
.079
.623
.030
Ours (Face)
.058
.006
.008
.932
Ours (All)
.612
.766
.610
.923
.728
EXPERIMENTS
The localization and interpretation modules of our Omni-IML are trained totally independently. We present more implementation details the Appendix
5.1 COMPARISON STUDY ON FORGERY LOCALIZATION
The comparison results on natural IMI, document IMI,, face IML and scene text IML are shown in T Evidently, our generalist Omni-IML can simultaneously achieve state-of-the-art average performance on each individual task, demonstrating the strong generalization ability. This is be- cause our Omni-IML can adaptively select the optimal input modality and decoder parameters for each sample, effectively producing the best features for IML on different image types. In addition, the Anomaly Enhancement module suppresses feature noise and reduces model confusion in joint training, which further improves our generalist.
To further explore the generalist capability of previous IML methods, we re-train the state-of-the-ar models with their official model code, the same training data and pipeline as ours. The results are shown in the left of Table 3 Table 3 the left part is the performance of the models trained on specific IMI tasks. Evidently, all the models perform well on only one task. For example, TruFor trained on one task (Natural IML) has an IoU score of 0.485 on natural IML task but its IoU score <0.1 other IML tasks. The right part of Table 3 is the performance of models jointly trained on all tasks. The previous IML methods suffer more performance degradation and show a much worse average performance than Omni-IML in joint training. For example, for TIFDM, its IoU on document IML is 0.498 when it's trained on document IML task only, but its IoU on document IML is 0.428 when it's trained all the four IML tasks, which degradation of 7 points. While for our Omni-IML, the IoU degradation brought by joint training is merely 0.8 points (.774 versus 766) on document IML task. This is because the previous methods rely heavily on designs and strategies targeted at one IML task, and such designs and strategies usually do not work as well on other image types (e.g. noise filters, edge enhancement and object-level attention are beneficial for natural images but not for document images). Moreover, the tampering features among diverse image types differ lot from each other. Without a sample-adaptive and noise-suppression design, the previous methods are challenged to simultaneously learn them well. In contrast, our Omni-IML does not rely on task-depnnd design and benefits from the adaptive selection of optimal encoding Omni-IMi demonstrates
modality, decoder parameters, and noise- sriisesuppression. Consequently, strong generalization across different image types and has minimal performance degradation during joint training. Qualitative results for visual comparison are presented in the Appendix Figure 12]
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Table Comparison on Face and SexneText IML with IoU metric. 'O.F.: OpenForensics
Table 5: Ablation of the proposed modules on IML with IoU metric. 'w.o.": without, 'MG':
Modal Gate, 'DWD': Dynamic Weight Decoder,
Face IMI
Task
Scene Text IML Task
'AE: Anomaly Enhancement. 'Avg': Average.
Method
O.F
Methods
T-CC13
OSTF
Avg.
ManTraNe TraNet
.720
DeepLab3
.722
.290
.506
Ablation
Natural Document SceneTTex
Face Avg.
HPFCN
.726
HRNNet2
.731
.295
.513
Baseline
.432
.166
.492
848 .575
MVSS-Net
.701
BEiT-UPer
.709
.276
.493
w.o. MG
.489
.615
.537
.855 .629
CAT-Nett
.832
SegFormer
.778
.302
.540
W.O. MG*
.538
.634
.561
.904
.659
DOAGAN
.732
Swin-UPer
.773
.307
.540
W.O. DWD
.463
.589
.515
.867 .608
HiFi-Net
.749
ConvNeXt
.776
.310
.543
W.O. DW
.542
.728
.568
.912 .688
MoNFAP
.902
UPOCR
.716
.281
.499
W.O. AE
.548
.707
.570
.916 .682
Ours
.923
Ours .763
.458
.610
Ours
.612
.766
.610
.923 .728
Table 6: Fine-grained study of image artifacts interpretation ability. "Text Rec.': tampered text recognition, 'Abs. Pos': absolute position, 'Rel. Pos.': relative position, 'Obj. Rec.': tampered object recognition, 'Desc.': description, 'OCR': OCR accuracy. 'Ace': accuracy. 'MRB': the mean score of ROUGE-L and BLEU, 'D.S.: DeepSeek- VL. 'M.C.': MiniCPM-V2266, 'Int.' : is Interv 'Qw.: Qwen2.5-VL. 'FS.': FakeShield Xu et al. 2024). 'SL.': SIDA Huang et al. (202)).
Document
Scene Text
Face
Natural Image
Abs.. Rel. Artifacts Abs. Rel. Artifacts Obj. Abs. Rel. Artifacts Obj. Abs. Rel. Artifacts
Method Rec. Pos. Pos. Desc. Rec. Pos. Pos. Desc. Rec. Pos. Pos. Desc.
Rec. Pos. Pos. Desc. Avg.
OCR Ace OCR Ace MRBOCR Ace OCR AOCCRcccMMii MRR MRB Acc MRB Ace MRBMRB Acc MRB Acc
Supervised Fine-Tuned Models without our method
D.S.7B .346 .266 461 .199 186 .440 ,496 .512 .552 .100
.792 .926 .626 .851 .344
.223
.702 ,279 806 .272
.497
M.C.sB
.299 .373 384 478 .133 .492 .64 ,546 .578 194
.758 .898 .583 .831 .330
.199
.669 .251 .790
.268
.474
Int.2B .276 .379 339 480 .172 .421 .449 .,73 530 .178 .757 .896 5591 .828 .331
.197 .643 239 .778 .266
.464
Qw.3B .222 .348 .419 .199.197 .489 .451 .553 .572 .202
.738 801 .599 .775 .340
.241 .694 .310 .771 .298
.481
Qw.7B ,312 .381 .429 521 2202 .580 .366 640 .64 .27 .768 .899 .630 .744 .342
.27
.752 .342 .749 .301
.512
F3B .153 .325 .275 407 160 .382 .37 ,299 405 .153
.706 .65 .556 .730 .303 .181 .635 .226
.732. .253
.406
$1.13B .154 .328 .275 411 166 .33 .49 .300 4455 .153 .706 .765 .557 .730 .303 .182 .635 .226 .732
.253
.406
Supervised Fine-Tuned Models with our proposed method (+Ours)
Int.2B .610 .595 .654 .688 .23 .615 .586 .399 .693 .226 .770 .938 .587 .847 .339 .241 .746
.292 .807
.275
.567
Qw.3B .645 .566 .696 .65 .247 .721 .619 .768 681 .243 815 .941 .668 .843 .366 .282 .763
.343
.772
.310 .598
Qw.7B .653 .576 .698 .689 .254 .716 w1w.11222..... .744 .252 .817 .46 .669 862 .381 .265 .660
.328
.788
.305
.603
5.2 ABLATION STUDY ON FORGERY LOCALIZATION
The ablation results are shown in Table "w.o. MG' denotes the model without the Modal Gate and using frequency-vision fused features in encoder, it has an average IoU of .629 which is 9.9 points lower than Omni-IML of .728. This is because the frequency features in some samples are unstable, and without the Modal Gate to filter them out, these features introduce too much noise to the encoder. "w.o. MG denotes the model without Modal Gate and using the pure vision modality, it has 6.9 points lower IoU than Omni-IML. This is because frequency domain modeling is also effective in some cases, especially when the tampered region is visually consistent (e.g. on document images). 'w.o. DWD' represents the model without the Dynamic Weight Decoder, it has 12.0 points lower IoU than Omni-IML. This is because the diversity of tampering features too high for the encoder to learn them well, thus confusing the model, confirming the necessity of the proposed DWD for the generalist model. "w.o. DW' is the model with the DWD structure but the filter weights in the decoder keep all the same for each input, it has 4.0 points lower IoU than Omni-IML, this verifies that the adaptive selection of optimal decoder weights for each sample can reduce confusion in joint training. 'w.o. AE' is the model without the proposed Anomaly Enhancement (AE) module, it has 4.6 points lower loU than OmL. This is because the proposed AE module can enhance the forged regions and suppress noise in the features. The model without any of the proposed modules serves as the "Baseline' model, its IoU is 15.3 points lower than Omni-IML. These results have proven the effectiveness of our proposed methods.

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5.3 EXPERIMENTS ON FORGERY INTERPRETATION
The experiments interpretation task conducted works that use one metric to evaluate the entire output string, we use our structured textual labels to perform fine-grained evaluation. As shown in 6 we use OCR accuracy [Zhang al. (2019) to evaluate the descriptions of tampered content and relative position for text images, and use Mcan of ROUGE-L and BLEU (MRB) for non-text images. The description answers for absolute position (e.g. "Top left") and artifacts title (e.g. "Edge Artifacts") are close-ended, they can be regarded as single-choice and multi-choice tasks respectively, so we use accuracy to evaluate the ratio of matches. The detailed artifact description are open-ended, and again we use the MRB score.
Omni-273k dataset. Unlike previous
By comparing the model fine-tuned with and without our method Table we can learn that our visual prompt improves model's forgery interpretation ability via reducing the misidentification of tampered regions. For example, for the Qwen2.5-VI. scription scores for tampered text, absolute and relative positions are .312, .381, .429 respectively on documents. These low scores mean that the models often incorrectly detect the tampered re- gions. In contrast, the same model fine-tuned with method gets the three much higher scores of .653, .576. .698 respectively. This confirms that our visual prompt assists the model in correctly identifying the tampered region, and consequently the artifact description is much more accurate (e.g. .689 versus 5211). Similar conclusion to other image types such as scene texts and faces.
fine-tuned without our method, its de-
FakeShield and SIDA not highly effective. This is because their frozen SAM and LISA Lai al. (204)) paradigms are challenged in text and multi-target scenarios Xia et al. (202)). addition, their base MLLM LLaVA is relatively outdated with too-small fixed input resolution 336 and poor at recognizing non-English text. In contrast, our method effectively improves various base MLLMs and consistently yields high performance, demonstrating high generality. Robustness evaluation and qualitative comparison are presented in the Appendix Table 13 and Figures 15
16
Image
Ground-Truth
W.D. DWD
W.D.
Figure 5: Qualitative ablation of the proposed modules.
CONCLUSION
We propose Omni-IML, the first generalist model for Image Manipulation Localization (IML). Our generalist model achieves generalizable localization through several novel and effective modules. These include a Modal Gate Encoder, Dynamic Weight Decoder, and an Anomaly Enhancement module. We also introduce an effective interpretation module for better interpretation of image artifacts in natural language. Furthermore, we constructed Omni-273k, large-scale, comprehen- sive, and high-quality dataset. Its textual annotations are initially curated using a novel Chain- of-Thoughts automatic pipeline. This significantly improves annotation quality and reduces man- ual cleaning costs. The dataset's structured annotation also enables reasonable evaluation and in- depth analyses. Extensive experiments on four major IML tasks demonstrate that single model achieves state-of-the-a average performance across all tasks simultancously. We believe that this work offers valuable insights for real-world application and future research in IMI.
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ACKNOWLEDGMENT
This research is supported in part by the National Natural Science Foundation of China (Grant No.:62476093).
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Zhipei Xu, Xuanyu Zhang. Runyi Li, Zecheng Tang, Qing Huang, and Jian Zhang. Fakeshield: Explainable image forgery detection and localization via multi-modal large language models. arXiv preprint arX1v:2410.02761, 2024.
Yuan Yao, Tianyu Yu, Ao Zhang, Chongyi Wang, Junbo Cui, Hongji Zhu, Tianchi Cai, Haoyu Li, Weilin Zhao, Zhihui He, et al. Miniepm-v: gpt-4v level milm on your phone. arXiv preprint arXiv:2408.00 01000, 2024.
Zeqin Yu, Jiangqun Ni, Yuzhen Lin, Haoyi Deng, and Bin Li. Diffforensies: Leveraging diffusion prior to image forgery detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12765-12774, June 2024.
Quan Zhang, Yuxin Qi, Xi Tang, Jinwei Fang, Xi Lin, Ke Zhang, and Chun Yuan. IMDPrompter: Adapting SAM to image manipulation detection by cross-view automated prompt learning. In The Thirteenth International Conference on Learning Representations, 2025. URL https:
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A
APPENDIX CONTENTS
Detailed Comparison with FakeShield
More Model Details
Modal Gate Encoder
Anomaly Enhancement
Dynamic Weight Decoder
D Detailed Chain-of-thought pipeline
D.1
Detailed -hain-of-thought Method
D.2 Quality Evaluation
F
More Details about the Omni-273k Dataset
More Implementation Details
Image Forgery Localization
Image Forgery Interpretation
More Experimental Results
More Ablation Studies on the Modal Gate
DWD Filter Activations
More Forgery Interpretation Results
Robustness Evaluation
Evaluation on Unknown IML Tasks
6.6
Image-level Forgery Detection
Visualization
Visualization of the AE Enhanced Features
Visualization for Module Ablations
Visualization for Forgery Localization
Visualization for Forgery Interpretation
Limitations
B DETAILED COMPARISON WITH FAKESHIELD
Although FakeShield Xu et al. (2024) also seeks to achieve explainable IML, our work differs significantly from FakeShield following:
1. IML Image Domains. Our Omni-IML can effectively localize forged region in pixel-level across the four major IML domains, including natural IMI, document IMI., face IMI. and scene text IML. In contrast, although FakeShield is designed to identify forged natural and face images at lmael- its pixel-level forgery localization capability is still limited domain Xu et al. (2024). In addition, FakeShield cannot identify forged document or scene text images neither at image-level nor pixel-level. Therefore, Fake is not the first pixel-level image manipulation localization generalist. In contrast, ours is.
single natural image
Multi-target Localization Capability. Our Omni-IML adopts a novel paradigm that prompts MILM with the localization model's mask prediction. Therefore, ours is still effective when mul- tiple forged regions exist in an image. In contrast, FakeShield adopts the LISA paradigm Lai et al. 2024), which is well-known to be poor in multi-target scenario Rasheed et al. (2024); Xia et al. 2024 However, multi-target forgeries scenario is common in real-worid, especially for text im- Qu et al. (2023 2025b). This limits the application value of FakeShield.
ages Luo al. 2025):
Generalist Design. Our proposed Modal Gate, Anomaly Enhancement and Dynamic Weight Decoder typically designed to alleviate performance degradation in joint task training, demon- strating high effectiveness for the generalist model (Table contrast, FakeShield merely mixes all training data, without any special design for generalist model.
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4. Performance Gap. Due to the above reasons, our Omni-IML has significantly lower performance degradation in joint training, and achieves significantly higher performance than FakeShield (e.g. 16.4 higher average IoU on natural IML and 24.9 higher average IoU on all tasks, Table 2
5. Structural Flexibility. The localization module and interpretation module in our Omni-IMi are relatively independent. They trained totally independently and are connected only with a mask prediction during inference. Consequently, in practice, one can change the weights of the localiza- tion module quickly adapt different scenarios. In contrast, the localization and interpretation functions in FakeShield are highly coupled, limiting its flexibility.
6. Benchmark Difference. As shown in Table
The MMTD-Set in FakeShield paper contains only natural and face images, and only nat- ural images have pixel-level annotations. In contrast, our Omni-273k benchmark contains natural, document, face, scene text images, and all have pixel-level annotations.
The MMTD-Set contains only single-target scenario, while ours contains both single-target and targe- scenarios.
Our Omni-273k contains 10x more forgeries and 20x more manual (real-world, high- quality) forgeries than the MMTD-Set, making more qualified evaluating real-world model performance.
Our Omni-27 273k adopts structured annotation format to enable reasonable evaluation and in-depth item-wise analyses. In contrast, the MMTDD-St annotation is unstructured. Only metric be used per evaluation, item-wise be produced, and thus cannot provide enough insights behind the results.
C MORE MODEL DETAILS
C.I MODAL GATE ENCODER
Image Encoding. Given an input image X E Rxxx33 and its Y-channel quantization table QT E RSx8 we extract vision features Frgh using Vision Perception Head (VPH), Frgb (X). We obtain frequency features Frreg from the DCT coefficients and quantization tables (QT) of the images using Frequency Perception Head (FPH), Frreq FPH (DCT(X) QT). We use the same VPH and FPH architectures as those proposed in Document Tampering Detector Qu et al. (2023). The Frreg is fused with Fryb by a conv-layer Conv to get the fused features Ffuseds Ffused Conw Frgb. Fireg)- Two coarse binary mask predictions Pryb and Prused are further obtained from Frgh and Ffased with two auxiliary heads Aur H ead respectively, Prgb = Aur H ead (Frgh), Pjused Aur Hl eadg (Ffused), each of the auxiliary heads consists of two conv-layers.
Modal Gate. The input of the proposed Modal Gate has four parts: Frgh. Fjuseds Prob and P Prasedi We repeat Pr Prgb: Pjueed and concatenate them with Frgb- Frused get F Fouts which is then fed into binary classifier for optimal modality prediction. Pets (LSS(Feat) Pmodat Round(a (Patt))) where a is the sigmoid function and Round function rounds up the nearest integer, which means a fixed threshold of 0.5 for our modal gate's binary classification. The classifier CLS consists of several conv-layers, global average pooling layer and a linear layer, and is used to determine whether to use the fused feature Fjused or the pure vision feature the encoder input Frgb by observing the noise level and anomaly significance level of F Fusedds Frgh and their coarse predictions Prob and Pf Jused-
Loss Function. The Modal Gate Encoder is optimized with LMG. the sum of two segmentation losses and one classification loss. CE denotes the cross-entropy loss function, Lm is the ground- truth mask indicating tampered region and L E {0, 1} is the classification label indicating the op- timal modality. Le is obtained by choosing the most accurate coarse prediction. (xx( y) denotes the Intersection over Union between inputs x and y.
LMG = CE Lm) + CE((Prsed Lm) + CE(PPet Lc)
Le [1 LoU Lm) >
Lm) + 0.1)
otherwise
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Conv 1x1
Region
Regression Loss Classification Loss
Fz
Feature Pyramid Network
Proposal
Network
Avg. Pool
Conv
Regression Loss Classification Loss
Conv 1x1
F
Rol Align
Norm
BBox
Head
Norm
Conv
Fb
Features
Avg. Pool
Channel Dimension Concatenation
Learnable querles
Conv 1x1
F3
Purple: both training and inference.
Addition
Black lines: Only
Input Features
Multiplication
exists in training.
Figure 6: The proposed Anomaly Enhancement.
C.2 ANOMALY ENHIANCEMENT
Key Idea. Different image types from different IML tasks produce different features, joint training brings much more noise to the features and confuses IML model. To tackle this, we propose to enhance the contrast of forged regions and improve the feature extraction across diverse image types through including an extra box supervision during training. However, directly training the model with the both detection and segmentation frameworks may also cause task competition for model parameters Heuer et al. (021) and weaken model performance, while directly scaling up the model parameters could alleviate the competition but will increase computation burden. To address this, we propose a novel collaboration module Anomaly Enhancement (AE).
Method. As shown in the Figure 6 for the input features F3 and F3 in ş and T6 input image size, first extract task-agnostic features Fa and F3 with two attention layers that are used to decouple and minimize negative impact from the segmentation supervision. After that, F, and Fb are processed by the detection modules, including two Feature Pyramid Networks (FPNs) [in et al. (017)) and the Faster R- R-CNN's Ren al. (2055) Region Proposal Network (RPN) and box head. The detection modules (black arrows in the Figure O)) are only present during training. Including the two cascaded FPNs reduces parameter competition from the detection framework and discarding them during inference ensures the computation efficiency, successfully addressing the dilemma. The AE module trained in an end-to-end manner with same four loss functions as Faster R-CNN, including the two classification losses and two regression losses for RPN and box head respectively. After training. the Fa and Fú contain positive features enhanced by the detection supervision, we add them to the original features F2 and F3 and fuse them with conv-layer get Fzr and Fy.
The proposed AE effectively achieves task collaboration while keeping the inference cost almost unchanged. With the AE module, the tampered regions in features Fz and F3 be enhanced and the false-positive noise can be reduced. Consequently, our AE module helps to extract better common features and thus benefits the generalist model.
Loss Funetion. The AE module is optimized by bounding box losses as Faster R-CNN Ren et al. (205 from the RPN and Rol-Head. LAE L L ground-truth boxes are the bounding boxes of the mask annotations' connected regions.
Leggression + L Head + L Rol The
C.3 DYNAMIC WEIGHT DECODER
In the proposed Dynamic Weight Decoder, the low-level input features are fused with high-level input features by Pyramid Pooling Module Zhao al. (001)) and Feature Pyramid Network Lin et al. (2017) to obtain multi scale features F2, Fi. A global feature vector Vg is obtained by average pooling F,. The extracted feature is concatenated to the multi-scale features, Feat
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Dynamic Weight Filters (DWF)
PPM
FC
Weightt
Weighe2
Dynamic
Feature
Weighe!
woight
Weightt
Convixt
DWF
V
F2
(D=2)
Pyramid Network
Depthwise
Channel
DWF (D=3)
F3
Attention
Input
Unrput
DWF (D=6)
DWF (D=2)
DWF (D=2)
Conv1xt
F4
Final Pred.
V,
Channel Dimension Concatenation
Global Representation
Dynamic Weight Decoder
Green lines: Global average pooling
DWF (D=2)
Dynamic Weight Filters with dilation
Figure 7: The proposed Dynamic Weight Decoder.
Concat(Fi, Fa). The concatenated features are channel dimension reduced and processed by series of Dynamic Weight Filters with different dilation rates,
Fdec1 = Concatt AFg(Feut), Faw, Feat),
Faw CConcat[DW (Feat Vg) for in (2,
DW F, denotes the proposed DWF with dilation rate n. The final prediction PDWD D is obtained by
PDwD Conv( DW D2 (DW D2(Conw((Fed) dect), Vg),, Vg)), where Conw denotes 1 x conv-layer. The DWD is supervised by minimizing the cross-entropy loss between PDw D and the ground-truth mask Lmd LDWD CE(PpwD) Lm)
Dynamic Weight Filters. As shown in the top-right of Figure 7 to obtain the dynamic filters, we first average pool the input feature to obtain current global representation V. (orange box in Fig- 7. then interact V. with the global image vector V, (blue box in Figure 7 with a fully connected layer and identify the optimal dynamic filters Dopt by weighted summation of four common volutional filters. A; oFC((Ve Vg)), Dops = Σi A, * Wi, a is the sigmoid function, FC linear layer, W, is the ith filter in the Dynamic Weight Filters (DWF). Finally, depthwise convolve the input feature with Dopt and then perform point-wiss convolution with x 1 conv-layer obtain the final output.
D DETAILED CHAIN-OF-THOUGHT PIPELINE
D.I DETAILED CHAIN-OP-THIUGGIN
Step 1. Instance-wise Tampered Object Recognition. In this step. we accurately recognize the content and position for each tampered region. Given the binary mask annotation maask indicating the tampered region for a tampered image Tramps each of the connected components Tmask is tampered instance.
Content recognition and absolute position calculation for text images, For text images, the OCR result for each tampered instance is the content. We cut off the tampered regions of the image Hamp and recognize the text respectively using commercial OCR engine. The absolute position is the position relative the entire image, and is the selected from the nine options: Top left, Top center, Top right, Left side, Center, Right side, Bottom left, Bottom center, Bottom right. We calculate the absolute position for each tampered region based on the position of its centroid.
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Step 1: Instance-wise Tampered Object Recognition
Step Focused Artifacts Description
Step 3: Self Examination
Finee- H.
00::5533
Content:
Tampered object1
"09:55:34"
Response
Final Response
Absolute
Position ...
GPT-4o
GPT-4o
13:08
Relative
Tampered image have
Position
Tec
Highlight muskl 1iig
Low-level
Correct the wrong ones
One-by-one Analyze
artifacts
High-level
Change to
artifacts
confident tone
X 40701
Content: "K
Tampered object2
Response
Response 2
#XE440711"
Final
Absolute
Position
Relative
GPT-4o
GPT-4o
Position
Mask smnotation
Highlight mask2 Fiih
Figure 8: The proposed Chain-of-Thhuught Pipeline.
Content recognition and absolute position calculation for non-text images, For face and natural images, we highlight the tampered regions in Lamp respectively to get m highlight masks hight E [1. m]. We prompt GPT-4o with Hamp and high sequentially for each tampered instance to get its content (e.g. "The face of a smiling young man with black hair and in blue shirts"). We also calculate the absolute position for each tampered region based on the position of its centroid. For natural images in NISTI6 Guan et al. (2019), IMD20 NIDozamoyzassky et al. (2020) and MIML QQu al. (224) datasets where different tampered regions can belong to the same tampered object, instead of processing each region respectively, we directly highlight all the n tampered regions in the same image, and query GPT-4o to sequentially generate the content and absolute position descriptions for all objects.
Relative position for text images. The relative position is the text line containing the tampered text region. We detect and recognize the text lines in the given image Itamps and determine which text line tampered text region belongs based on IoU.
Relative position for non-text images. We query GPT-4o to generate relative position descriptions for non-text images (e.g., "Over the brown squirrel eating nut").
Step Focused Artifact Description. In this step, we aim to obtain detailed descriptions image artifacts for each tampered instance. For each tampered instance, we prompt GPT-4o with 4ampp high, the previously recognized content and position, and an elaborate query (listing the common artifact perspectives) to obtain the detailed descriptions Taes for low-level visual and high-level semantic artifacts. The full query (taking document image example) is shown in Figure 13
Step 3. Self Examination. In challenging samples where image artifacts are not obvious, response of GPT-4o be unconfident and incorrect, while manual filtering is costly. To this end, we propose to further improve the annotation quality by guiding GPT-4o to carefully examine and correct its previous response. For each tampered object, prompt GPT-4o with Tdes Itamp: Thigh and the previously recognized contents and positions, then show it example containing both an unconfident, incorrect answer the corresponding manually corrected one. The full query (taking document image example) is shown in Figure [4
D.2 QUALITY EVALUATION
To evaluate the performance the proposed annotation pipeline. We have volunteers score 500 random samples from each of the four IML image types, with 0 being the lowest quality and be- ing the highest. The average score for the annotations generated with our method is 4.87, which higher than the 2.94 for annotations generated without method. This validates that our pro-
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Table Statistics of the Omni-273k dataset. "Image num." denotes the number of tampered images, "Target num.' denotes the average number of tampered instances in each sample. 'Avg. len.' denotes the average length of the annotation chars in each sample.
Image Domain
Image num.
Target num.
Avg. len.
Natural images
1.481 126366
1.269
1925
Document images
80028
2131
Face images
60689
2.078
1265
Scene text images
1978
2.533
3379
posed pipeline can significantly improve annotation quality and produce flose-to-humal annotation quality.
E MORE DETAILS ABOUT THE OMNI-273K DATASET
Statistics. The statistics of our dataset are shown in Table 7 A representative sample displaying our Omni-273k's structured annotation format is illustrated Figure 18
Train/Test Split. The Omni-273k dataset is constructed by adding forgery interpretation annotations
to existing high-quality datasets. Therefore, the split of training and test sets of Omni-273k follows the conventional split in each image domain. To be specific, 217, 907 images from CASIAv2 Dong
ct al. (2013), MIML Qu et al. (2024), the training sets of Tamper Qu et al. (2023), SACP/Alibaba Security (2020), OpenForensics Le et al. (202T). Tampered- -C13 Wang et al. 2022b) and OSTF
ct al. (2025b) are split as the training set, the rest 55, 869 images are split as the test set.
Manual Cleaning. We manually reviewed and cleaned the forgery interpretation annotations of the Omni-273k test set. The textual annotations of the training set are the output of our CoT annotation pipeline and may contain minimal noise. However, this minimal noise better simulates the noisy real-world scenarios and reflects model robustness.
Effectiveness. Our Omni-273k can help large language models to better interpret image artifacts. The Omni-IML model trained with our Omni-273k can effectively describe the tampered region content, the absolute position, relative position and artifact clues of the forgeries from all the four major image domains (natural image, document, scene text, face), as shown in Figures 15
16 17
F MORE IMPLEMENTATION DETAILS
The localization module and the interpretation module of our Omni-IML are trained totally inde- pendently, without any effect or involvement to each other in training.
F.1 IMAGE FORGERY LOCALIZATION
Model Training Details. The backbone model of our Omni-IML is ConyNeXt-Base Liu et al. (202b)) initialized with its official ADE20k Zhou et al. (2017) pre-trained weights, following previ- ous works Yu et al.] (2024): Qu et al. (2024). The Omni- IML is trained with the cross-entropy loss for 400k iterations, using the Adam W optimizer Loshchilov & Hutter (2017), with batch size of 16 and an input size of 512 x 512. The initial learning rate is set to le-4 and decays to 1e-6 in a linear schedule. We remove the forgery interpretation module when training the forgery localization module. A fixed threshold of 0.5 is used to binarize model predictions during inference. Pixel-level Intersection over Union (loU) and binary Fl are used to evaluate model performance.
More Details about the Training Data. The natural image part of our training data includes: tam- perCOco Kwon et al. (2022), CASIAv2 Dong al. (2013). MIML Qu al. (2024) and COCO Lin et al. (2144). These datasets have 600,000, 5123, 123150 and 118287 images respectively, and the sampling ratio for these datasets is approximately 2:2:2:1. The document image part of our train- ing data includes: SACP[Alibaba Security (020) training set, Doc' Doc'Tamper Qu et al. (202)) training set. These datasets have 1604 and 120,000 images respectively, the sampling ratio is approximately 1:2. The face image part of our training data is the training set of OpenForensics Le et al. 2022 which has 44122 images. The scene text image part of our training data includes the training set of Tampered-IC 13 Wang et al. (2022b), which has 229 images. The total number of the training data 1012515, the sum of the above numbers. The sampling ratio for natural images, documents, face images, scene text images is approximately 10: 5: 2: 20.
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More Details about the Test Data. The test data for pixel-level IML includes CASIAv1 Dong al.
(213)) with 921 images, Coverage Wen et al. (2016) with 100 images, NIST16 Guan et al. (2019)
with 582 images. CocoGlide Guillaro et al. (2023) with 512 images, IMD20 Novozamsky et al.
(2200) with 2,010 images, S AACP/Alibaba Security 2020) test set with 406 images, Doc't Domperr Qu
al. 2023) test set with 30,000 images and minq=75 75 ("minq" is the minimum compression quality
factor), Doc' DocTTappe-FCCC Qu et al. (2023) with 2,000 images and ming=75, DocTamper-SCD Qu al. (2023) with 18,000 images and minq=75, OpenForensics Guo al. (2023) test set with 18,895 images, Tampered-IC13 Wang et al. (2022b) test set with 233 images and OSTF Qu et al. (0225))
test set with 1007 images.
Evaluation Metries. For the DocTammer benchmark, we use its official scripts to evaluate model performance. For other benchmarks, we calculate foreground loU and pixel-level Precision (P), Recall (R), and F1 F1-score (F) for each sample and then compute the average score following previous work Ma et al. (2024) for fair comparison.
Compared Methods, In Table 2 the compared methods include RRU-Net Bi al. (2019), MVSS-
Dong et al. (0022). , PSCC-Net Liu et al. (2022a).
Kwonn et al. (2022). IF-OSN Wu et al.
(022). EVP Liu et al. 2023b), TruFor Guillaro et al. (2023). APSC-Net Qu et al. (2024). , Sparse-
VIT Su et al. (2025) and PIM Kong et al. (2025) for natural IML; CeL-NNet Niloy et al. (2023).
TIFDM Dong et al. (2024), Swin-UPer Liu et al. (202I). DTD Qu et al. (2023). CAFTB Song
et al. (025) for Document IMI; Mantra Wu et al. (2019), 9) HPFCN Li & Huang (2019). DOA-
GAN Islam et al. (,020), , HiFi-Net Guo et al. (2023).
MoNFAP Miao al. (2024) for face IML,
DeepLab3 Chen et al. (2018). HRNetv2 Wang et al.
(2020). BEiT-UPer Bao et al. (2021). Seg-
Former Xie al. (202 UPOCR Peng et (2024) for scene text IML.
F.2 IMAGE FORGERY INTERPRETATION
The multimodal large language model in our interpretation module is LoRA Hu ct al. (2022) fine-tuned 60k iterations with batch-size of 16, a LoRA rank of 64, an AdamW optimizer with learning rate decayed from le-4 to O. We fine-tune our forgery interpretation module after the training of localization model is done. During both the fine-tuning and inference stages of our forgery interpretation module, the input masks are the predictions from the trained forgery localization module.
Hyper-paraanetes.
LLM Query Details. For the interpretation model fine-tuned with our method, the query prompt "The left half of the input image is a tampered face image. The right half of the input image reference image with the suspected tampered region of the left half image highlighted, and the suspected authentic region darkened. Please find the tampered region in the left half of the input image. For each tampered region, please describe its content, absolute position, relative position, and visible artifacts. The description of the visible artifacts should be in JSON format, where the keys are the types of visible artifacts and the values are the corresponding detailed analysis. Always assume that you are observing only the left half of the input image".
For the interpretation model fine-tuned without our method, the query prompt is "The input image is a tampered image. Please find the tampered region in the left half of the input image. For each tampered region, please describe its content, absolute position, relative position, and visible artifacts. The description of the visible artifacts should be in JSON format, where the keys are the types of visible artifacts and the values are the corresponding detailed analysis. Always assume that you are observing only the left half of the input image".
For the pre-trained model on zero-shot testing, the query prompt is "The input image is a tampered image. Please describe the content, absolute position, relative position, and image artifacts of the tampered object(s) in this image. Content is the appearance of the tampered object (e.g. "A white cat sleeping on the table") or OCR of the tampered text. Absolute position is the position relative to the entire image (e.g.. "Top left of the image"). Relative position is the position relative to other objects (e.g. "In front of the women in white shirts") or the text line of the tampered text (e.g. "In the text line: 09:34:50"). Image artifacts can include edge artifacts, texture artifacts, font artifacts, alignment artifacts, lighting anomaly, depth anomaly, color inconsistency, semantic artifacts, etc. If there are multiple tampered objects in this image, please describe the content, absolute position, relative position, and image artifacts for each of them".
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Table 8: Ablation study on whether soft or hard modality switch is better in the Modal Gate. We used the hard one.
Ablation of MG
Natural
Dacument
Scene Text
Face
Average
IoU
F1
IoU
FI
IoU
FI
IoU
FI
loU
F1
Soft
.479
6600
.628
7331
.536
.696
.78
.925
.630
.738
Hard
.612
.678
.766
.813
.610
.749
.923
.957
.728
.799
Table Ablation study on the Modal Gate's another input modality besides pure vision.
Input
Natural
Document
Scene Text
Face
Average
Modality
IoU
F[
IoU
F
IoU
F1
IoU
F1
IoU
F1
None
.338
.625
.634
.142
.561
.712
.904
.947
.639
.756
SRM
.545
.629
.637
.743
.578
.723
.916
.953
.669
.762
Bayar
.498
.599
.603
.722
.544
.700
.906
.948
.638
.742
NP++
.540
.626
.727
.560
.712
.925
.958
.659
.756
DCT
.612
.678
.766
.813
.610
.749
.923
.957
.728
.799
Table 10: Modal Gate selection rate of pure vision features and frequency+vision fused features under four IML tasks.
Features Pure vision
Natural IML
Document IML
Scene Text IML
Face IML
0.94
0.02
0.78
0.97
Fused
0.06
0.98
0.22
0.03
Filter (Position Fig.3) DWF1 (Top Left Fig.3)
DWF2 (Middile Left)
[Bottom Left)
DWF4 (Bottom Center)
DWF5 (Bottom Right)
Natural
Document
Scene Text
1
Figure 9: Dynamic Weight Filters have different weight activation values for different tasks. cell is filter weight activation value.
Each
G MORE EXPERIMENTAL RESULTS
G.1 MORE ABLATION STUDIES THE MODAL GATE
Soft Hard Gate. In Modal Gate, soft gate means that fuse pure vision features Frgb and frequenfryyyency features Fused through adaptive weighting with a channel-spatial attentional module. A hard gate means that gate we used is better. This is because the motivation of our Modal Gate is to thoroughly exclude the distortion from the frequency features when the frequency features are too noisy. Our hard gate can achieve this, whereas the soft gate cannot.
Frgb Fjused- The results in Table 8 show that the hard
Input Modality. We evaluated the performance of our model under different input noise domains of our modal gate encoder, including SRM et al. (2018), Bayar Dong et al. (2022), DCT Qu et al. (203),, and NoisePrint++ (NP++) Guillaro al. (202)). As shown in Table 9 the DCT auxiliary modality we adopted is the best.
Modal Gate Activations. Table 10 shows the selection ratio of RGB/fused features in data from different image domains. Frequency+visil fused features are typically used in text images and pure vision features are mostly used natural and face images.
G.2 DWD FILTER ACTIVATIONS
Figure shows the filter weight activations of the dynamic weight filters in the Dynamic Weight Decoder (DWD). These values are calculated by averaging the activations of each test sample in each task. The results demonstrate that the filters have different activation weights for different tasks. The filter names correspond to Figure
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Table 11: Experiments on the interpretation ability. Coos. paragraph vectors. 'Avg.' denotes average score.
denotes the cosine similarity of the
Method
Cos. Pre-trained models (zcro-shot).
ROUGE-2
ROUGE-L
BLEU
Avg.
GPT-4o
975
.112
.011
.074
.341
VL.3 2B Chen
2024a
.973
.063
.297
Qwen2.5-VL. 3B Bai ct al
2025
.964
.077
.151
.020
.303
Qwen2.5-VI. 7B Bai
2025
.972
.094
.175
.035
.319
Supervise Fine-Tuned (SFT) models.
DeepSeek- VL 7B Lu al. (0224)
.993
.385
.441
.308
.532
MiniCPM-V-22 6 8B] Yao al.
2224
.992
.378
.439
.287
.524
Intern VT.3 2B Chen
2024a
.993
.372
.432
.293
.523
Qwen2.5-VI. 3B Bai
2025
.944
.403
.456
.316
.542
Qwen2.5- 7B Bai al.
2025
.944
.403
.455
.366
.542
Supervise Fine- Tuned (SFT) models with our method.
Ours+IntemVL3 2B
.993
.405
.466
.340
.551
Our=+Owen2.5VVL.3
.995
.424
.568
Ours+QWen2.5-VL 7B
.995
.433
.498
.367
.573
G.3 MORE FORGERY INTERPRETATION RESULTS
In Table we directly evaluate models with their whole output string for each sample using word-vectors cosine similarity Mikolov et al. (2017), ROUGE and BLEU, following the previous works Xu et al. (2024); Huang et al. (2024). The compared MLLMs include GPT-4o, DeepSeek- VL-7B Lu et al. 2024). MiniCPMV2.cm 2.B-BBBBBBBB Yao al. (2024), Intern VL3-2B Chen et al. (2024a) and InternVL2288 Qwen2.5-VL-3B Bai et al. (2025) and Owen2.5-VL-78 All the pre-trained models (the top group in Table I show poor interpretation performance (e.g. all BLEU scores less than 0.1), confirming that the pre-trained LLMSs are not naturally adequate to detect and explain image forgery. Fine-tuning the models with dataset is necessary for them to achieve forgery interpre- tation capability. The models fine-tuned with our method (bottom group) consistently outperform those fine-tuned without our method (middle group) under all metrics, confirming the effectiveness of the reference visual prompt in our interpretation module. We have presented the coarse- -gaanee evaluation of our model in Table of our paper, which further validates the conclusion.
G.4 ROBUSTNESS EVALUATION
Forgery Localization. We test the robustness of our localization model by applying image distor- tion to NISTI6 dataset images, following the standard setting in the field of image manipulation localization Li et al. 2023);; Zhou et al. (2023); Li et al. (022bb). We compare the pixel-level AUC performance with other methods, including Mantra-No Mantra-Net Wu et al. (2019), MVSS-Net Dong et al. 2022), SPAN Hu et al. 2020), PSCC-Net Liu et al. (2022a), ObjectFormer Wang et al. (0022a). SAFL Net Sun et (2023), NCL,Zhou et al. (2023), ERMPC
(2023), UnionFormer Li al. (2024b). Table 12 shows that our method exhibits strong robustness to various distortion operations,
significantly outperforming previous works.
Forgery Interpretation Robustness. We test the robustness of our localization model by applying various image distortions the test set. The results are shown in Table 13 which have confirmed the robustness of our method.
G.5 EVALUATION ON UNKNOWN IML. TASKS
Our method is designed to alleviate performance degradation in multi-task joint training, rather than generalize to unseen tasks. Nevertheless, we evaluated the joint-task-traite models' performance unseen IML task, the remote sensing IML al. (2024), with its RSCMQA dataset. The results are shown in Table [4 Our method achieves state-of of performance and demonstrates great potential for generalization unseen IMI. tasks.
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Manipulation localization performance 6 dataset under various distortions. AUC scores are reported, 'Ori' represents no distortion. 'k' represents kernel size of the Gaussian blur and "q" represents quality in JPEG compression.
Table 12:
Resize
Blur
JPEG
Method
Ori
.78x
.25x
k=3
k=15
9=100
q=50
Man Tra-Net Wu al.
2019
.795
.774
.753
.774
.746
.779
.744
MVSS-Net Dong et (2022
.788
.783
.775
.786
.758
.788
.788
SPAN Hu ct al. 2020)
.840
.832
.802
.831
.792
.836
.807
PSCC-Net Liu al. (2022a)
.855
.853
.850
.854
.800
.854
.854
ObjectFormer Wang et al. (2002aa)
.872
.872
.863
.860
.803
.864
.862
Sparse VTT Su et (2025)
888
.84
.669
.881
.877
.886
.881
NCL Zhou et al.
2023)
.912
856
.831
.840
.806
.843
.819
ERMPC et 20223
895
.93
.877
.892
.871
.894
.888
UnionFormer
Li et al.
2024b)
.881
873
.871
.865
.843
.880
.879
Ours
.188
.914
.895
.892
.882
.917
.890
Table 13: Robustness evaluation for the interpretation module. Our model uses Qwen2.5-VLL as its base MLLM. The score in each cell is the average cosine similarity, ROUGE-2, ROUGE-L, and BLEU scores.
Resize
Blur
JPEG
Method
Ori
.78x
.25x
k=3
k=15
q=100
9=50
DeepSeek-VL VL 7B Lu et al. (2024
.532
.529
.310
.527
.514
.528
.517
MiniCPM_V2 V2.6 6 8B Yao al.
(2024
.524
.519
.506
.520
.508
.521
.511
InterVLL 2B Chen
2024a)
.523
.518
.500
.518
.504
.519
.509
Qwen2.5-VL. Bai
.542
.538
.523
.538
.526
.540
.531
Qwen2.5-VL 7B Bar et al.
22025
.542
.539
.526
.539
.530
.541
.533
Ours QwenZ.3-VL. VL3B
3B
.588
.564
.552
.563
.553
.567
.557
Ours Qwen2.5-VI. 7B
.573
.569
.559
.559
.562
.572
.565
Table 14: Image manipulation localization on RSCMQA dataset Zhang et al. (2024) (unknown remote sensing IML task). Pixel-level binary Fl metric is used. 'Baseline' is our baseline model.
Method HiFi-Net MVSS-Net CAT-Net TruFor APSC-Net FakeShield Via DTD Baseline Ours Fl
.103
.133
.222
216
.258
.220
.158
.147
.209
.399
Fake Real Prediction
F. (H/32, W/32)
Global MaxPool 2FC
Input DCT, QT
FPH
(H/16, W/16)
Convix1 MaxPool2x2
Encoder Modal Gate
VPH
F2 (H/8, W/8)
C
Conv3x3 MaxPool2x2
Input image (H, W)
F, (H/4, W/4)
Conv3x3 MaxPool2x2
Encoder's output features
Binary classifier
C
Channel dimension concatenation
Figure 10: The classifier for image-level forgery detection.
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G.6 IMAGE-LEVEL FORGERY DETECTION
The image-level forgery detection is achieved by a binary classifier, as shown in Figure 10 The classifier extracts features from the encoder's output features, with alternating conv-layers and max pooling layers. The classifier performs the final classification with global max pooling and two fully connected layers. Binary cross entropy loss used to optimize the classifier. We test the image-level forgery detection performance of our model with AUC and balanced accuracy. following the same standard setting TruFor Guillaro al. (2023). The results in Table 15 show that the proposed model is also very good at image-level classification and significantly outperforms previous works.
Table 15:
Comparison study on image-level forgery detection. "Acc' represents balanced accuracy. mAACC represents mean AUC, 'mAce' represents mean balanced accuracy.
CASIA1
Columbia
Coverage
CocoGlide
Average
Method
AUC
Acc
AUC
Acc
AUC
Acc
AUC
Acc
mAUC
mAcc
CR-CNN Liu & Zhao 2020)
.670
.535
.755
.628
.553
.510
.389
.533
.642
.552
RRU-Net B1 et al. (2019)
.574
.488
.583
.500
.482
.500
.533
.503
.543
.498
ManTraNNt Wu al. (2019)
.644
.500
.810
.500
.760
.500
.778
.500
.748
.500
SPAN Hu et al. (0220)
.480
.487
.999
.951
.670
.605
.475
.491
.656
.634
CAT-Net Kwon et al. (2022
.942
.838
.977
.803
.680
.635
.667
.580
.817
.714
IF-OSN Wu et al. (2022
.735
.635
.882
.522
.557
.510
.611
.567
.696
.559
MVSS-Net Dong al.
(2022)
.932
.808
.984
.667
.733
.545
.654
.536
.826
.639
PSCC-Net Liu et al.
2022a
.869
.683
.300
.508
.657
.550
.777
.661
.651
.601
TruFor Guillaro a..
(2023
.916
.813
.96
.984
.770
.680
.752
.639
.859
.779
OmnI-IMI (Ours)
.950
.891
.9%6
.967
.800
.750
.890
.785
.909
.848
H MORE ANALYSES
H.1 ABLATION FOR MODEL BACKBONE
We experimented with a lightweight backbone. By replacing ConvNeXt-Base with ConvNeXt- Small, we created model that is significantly smaller and faster while achieving comparable per- formance (Table 17 demonstrating the improved trade-off.
H.2 FREDBACK FROM EXPLANATION TO LOCALIZATION FOR ERROR CORRECTION
We used the model's s textual output (object content and position) guide second-stage refinement. Specifically, we fed the explanations for natural/face images into an open-vocabulary segmentation model (DINO-X Ren et al. (2025)) and the explanations for document/scene-ter images into OCR tool (OCRSpace from GitHub) to generate refined localization masks. The results in Table[[8] show consistent improvement in localization accuracy. This confirms that feedback from explana- tion can refine localization.
H.3 ABLATION STUDY OF THE
SAMPLING RATIO
The sampling ratio represents how many times images from each domain are repeated when con- structing the online training dataset. During training, each batch is sampled randomly from this dataset. Our sampling ratio is empirically derived from two principles: task difficulty and image resolution. First, we assign a higher sampling weight to more challenging domains (e.g., scene- text). Second, we account for resolution, as larger images (like those in the scene-text domain) yield more potential training crops. This principled approach, which directly addresses the small dataset size by considering available training area, validated by our ablation study. The results in Table confirm ratio is near-optimal and effectively balances against overfitting and underfitting.
H.4 COMPARISON WITH NON-CoT PIPELINE IN ANOMALY DESCRIPTION ANNOTATION
For a fair quantitative comparison, we randomly selected 400 test samples from each domain and generated fampered-region explanations using the previous non-CoT approach (as in FakeShield
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al. 2024)) We then compared these non-CoT outputs with recorded CoT responses, eval- uating both against our manually cleaned ground-truth using the same accuracy and mean-of- ROUGEL-BLEU metries for anomaly description. The results, shown in the Table 20 clearly demonstrate the advantages of our CoT reasoning, particularly in multi-object scenarios, such as text and face images.
H.5 ON THE NOVELTY THE MODAL GATE
Our Modal Gate is fundamentally different from existing fusion mechanisms in two key aspects: Binary Hard Gate: Unlike prior work using soft attention for feature fusion, our module employs binary hard gate. This decisive mechanism proves significantly more effective, as validated by our ablation study Table Ieadiction-Inforaae contrast, our Modal Gate makes its decision by also considering the confidence of coarse predictions derived from each modality. This allows the gate to assess the reliability each feature set for the specific input, leading to a more intelligent and effective fusion. The ablation study in Table 21 confirms this design choice yields superior performance.
Gating: Conventional gates fuse features directly. In
H.6 ON THE NOVELTY OF THE DYNAMIC WEIGHTT DECODER
Our design overcomes key limitation of standard dynamic convolutions. While conventional meth- ods generate weights using only local features from their receptive field, our Dynamic Weight De- coder incorporates additional global context vector (V, in Figure (7]. This vector, which sum- marizes the entire feature map. informs the decoder about the global image type (e.g., natural vs. document). As confirmed by Table 22 this global awareness enables the generation of more special- ized and effective dynamic weights, a clear departure from prior methods.
H.7 BEHAVIOR ON MISSED DETECTIONS
The explanation module is not strictly bound by the localization output. It uses the mask as a prompt, but the MLLM can override or correct this prompt based on the full image context. We provide qualitative example of this error-correction capability in Figure 20
H.8 SPLITTING THE IMAGE INTO TAMPERED OBJECTS FROM THE BINARY MASK
This is mostly achieved by comnected-componentt analysis Bailey & Johnston (007)) as demon-
strated in Figure 19
VISUALIZATION
I.1
VISUALIZATION OF THE AE ENHANCED FEATURES
Figure [ that our proposed Anomaly Enhancement module can enhance the feature contrast between forged region and authentic region.
1.2 VISUALIZATION FOR MODULE ABLATIONS
Figure 5) shows the qualitative ablation of the modules proposed in Omni-IML. 'w.o. MG** denotes the model without Modal Gate and using the pure vision modality: 'w.o. DWD' represents the model without the Dynamic Weight Decoder; 'w.o. DW is the model with the DWD structure but filter weights in the decoder keep all the same for each input; 'w.o. AE' the model without proposed Anomaly Enhancement module; The model without any of the proposed modules serves as the "Baseline' model.
I.3 QUALITATIVE COMPARISON FOR FORGERY LOCALIZATION
The qualitative comparison between RRU-Net Bi et al. 2019). PSCC-Net Liu al. 2002a) MVSS- Net Dong et al. (2022), CAT-Net Kwon et al. (2022). TruFor Guillaro et al. (202)) APSC-Net Qu
al. (2024) is shown in Figure 12 Our proposed method demonstrates strong generalization.
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Table 16: Model complexity of LMMS-V-NNNN Dong al. (2022). Sparse ViT Su 2025). PIM Kong et al. 2025). CAT-Net K won et al. (022). APSC-Net Qu et al. 2024). DTD Qu et al. (202)),
UPOCR Peng al. (024), Shkelh Xu et al. (2024). The Frame Per Second (FPS) is evaluated on 3090 GPU with batchsize of 1.
Model
MVSS-Net
ViT
PIM
CAT-Net
APSC-Net
DTD
UPOCR
Ours
FakeShield
Size
146M
50M
178M 8.6
114M 9.2
144M 7.8
66M
192M
152M
1300M
FPS
20.4
18.7
8.5
7.0
7.1
0.4
1.4
QUALITATIVE COMPARISON FOR FORGERY INTERPRETATION
The model predictions for forgery interpretation are shown in Figures 15 that our proposed dataset and method can considerably advance the model s capability of forgery interpretation in natural language.
16
17
The results validate
Input
Ground
Features
Feaatunes
Pradictions
Predictions AEi
Input
Ground
Features
Features
Pradictlans
Predictions
Figure 11: The proposed Anomaly Enhancement module can enhance the feature contrast between fake and real regions.
Annotation
RRU-Net
PSCC-Net
MVSS-Net
IF-OSN
CAT-Net
TruFor
APSC-Net
Ours
Figure 12: Qualitative comparison between different image manipulation localization models.
J LIMITATIONS
The recent unified models in most fields are relatively large in size Wang et al. (2023), no exception the Omni-IMI. The complexity of Omni-IML.'s localization module is shown in Table [6] The
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Table 17: Ablation study demonstrating that lightweight backbone offers a compelling trade-off for Omni-IML, achieving comparable performance (Pixel-level loU) with significantly reduced size and latency.
Method
Size
FPS
Natural
Document
Text
Face
Average .728
ConyNext-Base 152M
7.1
.612
.766
.610
.923
ConvNeXt-Small 113M
9.3
.590
.745
.587
.919
.710
Table 18: Ablation study on refining localization predictions using the model's textual explanation. Performance is measured in pixel-level IoU.
Method
Natural
Document
Scene Text
Face
Average
Original Localization
.612
.766
.610
.923
.728
Explanation Refinement .636
.774
.641
.925
.744
Table 19: Ablation study of the cross-domain sampling ratio. Performance is measured in pixel- level loU.
Sampling Ratio NNat:Doc.:Faae:S....
Natural
Document
Face
Scene Text
Average
10:5:2:20 (Ours)
.612
.766
.923
.610
.728
10:5:10:20 (More face)
.603
.754
.925
.596
.719
10:5:2:50 (More scene text)
.611
.766
.923
.601
.725
10:5:2:5 (Less scene text)
.614
.764
.923
.549
.712
Table 20: Ablation study of the thain-of-thoughe (CoT) pipeline in anomaly description generation. 'Acc.' denotes accuracy. 'MRB' denotes the mean score of ROUGE-L and BLEU.
Ablation non-CoT
Doc.-Acc.
DoC.MMRB
S.T.AAc..
S.T.MMBB
Nat-AAc..
Nat.MMRB
Face-Ace.
Face-MRB
.673
.85
.511
.797
.82
.870
.693
.924
CoT (Ours) .949
.892
.68
.903
966
.921
.989
.970
Table 21:
Ablation study of the involvement of coarse prediction features in our Modal Gate. Per- formance measured in pixel-level IoU,
Ablation
Natural
Document
Face
Scene Text
Average
Without coarse prediction features
.560
.742
.602
.955
.705
With coarse prediction features
.612
.766
.610
.923
.728
Table 22:
Ablation study of the global vector V, in our Dynamic Weight Decoder. Performance is measured in pixel-level loU.
Ablation
Natural
Document
Face .584
Scene Text
Average .697
Without global vector V With global vector V,
.565
.721
.918
.612
.766
.610
.923
.728
complexity of Omni-IML's interpretation module the same as its base MLLM (e.g. Qwen2.5-VL- 3B). Our single model can address four IML tasks, and its complexity is significantly lower than that of maintaining four IML models for the four IML tasks. Moreover, our model's primary strength is its performance rather than its speed. Further work will be conducted to reduce the size and improve computational efficiency.
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You are expert good ai andlycing lamgered text images. You will be provided with the OCR information of the tampered area and images, first tampered image the yellow box and the authentic areas darkoned. The tampored text reads "5", and located at the top right of the images and Your task is Describe visible details the that have been tampered with. Please consider the visible details caused lampering from these perspectives. You can explain the judgments basis by combining multiple perspeclives::
reference image
the tampered
highlighted
User
tampered may inconsistent with authentic regions, Therefore, edges the tampered test region may be discontinuous and inconsistent with the background.
Edge artifacts. background
inconsistancy. The font the tampered may be slightly different authentic toxt.
thickness,
surrounding
Alignment inconsistency. tampered
inconsistent with surrounding small offset to text
line.
Inconsistant alignment. The tampered text have inconsistent spacing with the surrounding text small offset to the text line. coherence sentence. example, given tampered
Tampered may sentence meat bocks.", the tampered text "meat" makes the sentence "I meat books. incoherent. The tampered text itself may appear misspelled.
Semantic
Texture anomaly. The texture appearance of the tampered text may be different from the surrounding authentic text. For example the tampered text reglon may have difforent levels of resolution, pixelation, sharpness,
compression artifacts.inuut image
there other visible anomalies in the lampered Lext region but not included in the above points, you should describe them. Don't mention image the yellow box in your answer, always assume that only abserving the ingut image
Please the numbers
the that tampered text
10
6.90
90
6.90
User
9.90
9.95
9.90
7.80
0.25
23
20
5.90
0.25
23
20
5.
33
Edge artifacts: The edges the tampered text appear slight blur lack of sharpness compared the surrounding authentic text. This inconsistency may indicate that the background does not seemlessly blend with the edges of the tampered area, leading visible discontinuitis. inconsistency:
GPT4o
seoms slightll surrounding numbers. This variation can draw attention to the tampered text, does not match the uniformity of the
tampered
compared
in style.
3. Alignment incorsistency: The placement of the lampered text "5" seems to be s slightly misaligned compared to the authentic text spacing disrupts overall
above below offset may altoration.
text, making
Texture anomaly:
exhibit differences piselation and resolution when compared the adjacent authentic text. This could manifest lack clarity distinctiveness that is noticesble upon closer inspection,
The texture of the tampered suggesting it
naturally occurring part of the document.
appear match surrounding authentic text. The ilumiiation may look flat or inconsistett, contributing to the perception that this text has been altered rather than being part
Lighting inconsistency: lighting shading tampered
Figure 13: The prompt to query GPT-4o in the step 2.
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You are an expert good at analyzing tampered text images. You will be provided with the OCR information the tampered area and two images, the first the tampered text image and the second the reference image B, with the tampered areas highlighted the yellow and authentic areas darkened. The tampered text reads "5",
User
top right of images. and
You be given analysis
forged errors. Your task to examine each item of the provided analysis and find the inconclusive description (e containing the words inconclative description, please check whether artifacts described obvious. rewrite the description to confident ane without the word "could", "might" or "may". not, please delete the item. Please also delete item the content violates its trll..
usually unsatisfactory
uncertain
possible
"cauld", "might" "may"): J:
For examplee, given input analysis following: Judgement basis Edge
may noticoable discontinuity when compared to surrounding background. this might appear slightly jagged or irregular edge which doesn't seamlessly blend with the rest of the interface elements, indicating tampering.
edges
tampored
2. Testure anomaly: The texture of the tampered text might appear slightly out of focus, overly sharp, or not as smoothly rendered as authentic elements. Since these anomalies would contrast with the naturally rendered surrounding they manipulation.
indicative of
3. Font inconsisteccy: The font used for "BX" could diffeer subtle such as color, brightness, style from the adjacent text. This does match
inconsistency suggest that tampered.
identity
the display, pointing towards being
might have spacing issues neighboring other elements on the same plane, deviating from the uniformity expected in well-designed digital interface. Text incoherence: The tamperad word "ax" does break the coherence contained different grammatical structure that rendered the word unnecessary or incorrectly placed, could create confusion. in examined determine overall coherence.
Alignment inconsistercy: Tampered
be slightly misaligned
sentence. However, the surrounding
this "BX" fits grammatically, but context of surrounding
Lack integration: The overall lack of integration of "BK" with the rest of the elements might be apparent. This could give appearance heing improparly everlaid pasted onto the imago, contracting against the more naturally flowing and cohesive design authentic text.
The expected response should be: Judgement basis
tampered appears slightly focus, naturally rendered surrounding text, can be indicative of manipulation.
enomaly:
anornlly
Font inconsistency: The font usad for "Sx" differs subtle ways inconsistency would suggest that the element does not match visual identity of the rest of the display, pointing towards being tampered.
brightness, style from the
This
Lack integration: The overall lack of integration "8X" with rest elements apparent. This gives it an appearance of being improperly overlaid or pasted ento the image, contrasting against the more naturally flowing and cohesive design of the authentic
There the words "could", "might" obvicus edge artifacts or inconsistent alignment in the tampered text region. Therefore, these two items should be deleted. You should not delete the items just because they contain uncertain words such as "could" or "might". You should examine carefully delete uncertain descriptive
the items trledd "sdge Artifacts' "Alignment inconsistency" there
desoribed anomalies actually Item ensitled "Text incaherence" states that there text incoherence, which violates its title "Text incoherence" and should also
addition, content
input analysis (Providing
GPT-4o response)
RESPOT
900
6.90
1
90
5.90
9.90
9.90
User
86
2.76
7.60
2. 16
7.60
25
23.30
23 20
5 .90
33.
30
04
00
04
tampered "5" different numbers. This variation draws attention to the tampered text, as does not match the uniformity of the authentic text in size and style.
Font inconsistency;
thickness compared the surrounding
GPT4o
2. Alignment Inconsistency: The placerment of the tampered text "s" slightly misaligned compared to the authertic text. This offett creates awkward
that disrupts the overall flow of the making stand out alteration.
3. Texture anomaly: texture of the tampered area exhibits dilferences piselation and resolution when compared the adjacent authentic text. This manifests a lack of darity distinctiveness that noticeable upon closer inspaction, suggesting naturally accurring part the document
Figure 14: The prompt query GPT-4o in the step 3.
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Published as a conference paper at ICLR 2026
12-14 12:15:18
12:15
QEE::: 6:
283
Tampered Region: the text:
Absolute Position:
image.
Qwen2.5VL-77 (Baseline), incorrect
Relative Position: in the text line itzX S: 220314407**
Inconsistency: suggests that the text in this text line tampered with.
tampered differs original text, particularly
thickness. This
Tampered
Absolute Position: Bottom left of image.
Omni-IML (Ours), correct
Relative Position: the text line [[] 2022 12:18:05
Edge Artifacts: The edges the tampered text 2022-03-14 appear less defined compared to the surrounding text. There slight blurriness or irregulerities the boundaries, indicating mismatch with the beckground texture and color, which suggests tampering.
Inconsistency: font the tampered text different in characteristies compared the authentic text. observable
thickness of the characters, which do not match the surrounding text, making the tampered text visually distinct. direction,
Alignment inconsistency: uniformity of the image style.
with surrouncing text, disrupts
Tampered Region: The orange cartoon fox head with black edges.
Absolute Position: Center of the image.
Qwen2.5VL-2 (Baseline), less comprehensive
Relative Position: Over body eating brown squirrel.
Texture Inconsistency: cartoon texture sharply contrasts
brown squirrel background.
smooth, cartzon-llke texture, the squirrel exhibits natural fur testure rich in depth and detail. This dispanity creates visually discordant effect. Lighting Inconsistency:
present surrounding shadows and highlights on the fax fails align with the image's natural light source, resulting an artifictaa look. Additionally onallyy the fax lacks the ground.
lighting
cartoon inconsistent
realistic shadows that would naturally
Color Inconsistency: The cartoon Tox's colors are far more vibrant uniform comgared to the muted, tones the squirrel
background. This heightened saturation causes the fox to appear unnaturally distinct from the subclud color palette of the background.
Tampered Region: The orange cartoon fox head with pointed cars.
Absolute Position: Center
Omni-IML (Ours), comprehensive
Relative Position: Over the brown squirrel eating nut. sharp and lack smooth transition the background. These artifacts indicate that
Edge Artifacts: The edges of the cartoon fox head
cartoon fox heed been Lampered with.
Texture Inconsistency: The texture of the cartoon fox markedly different from that of the brown squirrel and the background, The fox has flat, cartoonish texture,
This inconsistency creates jarring visual Lighting inconsistency: The lighting on the cartoon fax does not match the lighting conditions of the surrounding enviroment. The shadows and highlights the fox de
squirrel natural texture conveys depth
with that would typically occur in relation to the squirrel the ground. Color inconsistency: The color saturation the
the leading unnatural appearance. fox lacks realistic shadowing
vibrant and uniform compared the subdued and varied colors the squirrel and the background. dilference in saturation makes stand out unnaturally against the more muted tones the the
image.
Depth Artifacts: depth
inconsistent
fox positioned
perspective
and scale do not align properly, creating sonse disorientation spatial relationships.
Figure 15: Comparisons in image forgery interpretation.
33

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Published as a conference paper at ICLR 2026
Tampered Region face
with
wearing white Qwen2.5VL. (Baseline), incomprehensive
Absolute Position: Right side of the image.
Relative Position: Adjacent the woman the red shirt with long brown hair
Edge Artifacts: The transitions around the hairline and jow display rough, unnatural edges that fail searnlessly integrate with the surrounding areas.
Texture Inconsistency: appears overly polished, missing
porss blomishes, resulting artificial, plactic
quality.
Lighting Inconsistency: lighting
illumination
scene, creating
Tampered
face of
long blonde hair
white lab
Absolute Position: Center of the image
Relative Position:: Between the woman with short brown hair and another woman with short blonde hair
Edge
boundary between
displays clear evidence tamporing, with gops abrupt trancitions.
Texture Inconsistency: The skin texture alternates between being overly smooth and exhibiting strange, inconsistent patterns in certain spots, suggesting
Lighting inconsistency: lighting independently before being merged.
significantly
edited
Tampered Region
Absolute Position: Right side of the image.
Omni-IML (Ours), comprehensive
Relative Position: the
hair Edge Artifacts: The edges around the hairline and jawline are jagged and do not blend smoothly with the rest the image.
Texture Inconsistency:
skin texture overly smooth and lacks natural face inconsistent with the background, creating a stark contrest that highlights the tampering. High-level Artifacts: The teeth show signs of irregular spacing and size, contributing to the unnatural look of the smile.
or blemishes, giving artificial appearance.
Lighting Inconsistency:: The lighting
Tampered Region
Absolute Position: Left side of the image
Relative Position:
with curly Edge Artifacts: The edges around the face, especially near hairline, show clear signs of manipulation with jagzed and unnatural lines. wrinides, and imperfections typical of real humen skin,
left
center
red top
Texture Inconsistency: The skin texture overly smooth and lacks the natural some areas appearing overly bright while others unnaturally shadowed.
Lighting Inconsistency: The lighting
face inconsistent, High-level Artifacts: The teeth show signs of blurring and lack the sharpness and detail expected real smile.
yourg Absoluta Position: Center the image
Tampered Region
long
Relative Position:
the woman with short blande and with brown hair
Edge Artifacts: The edges around the face, particularly where meets skin, show dlear signs manipulation, with unnatural discantinuities.
Texture inconsistency:
appearing overly Lighting Inconsistency: The lighting the face does not align with the lighting on the hair, indicating that the face and hair were likely edited
texture inconsistent,
showing unnatural patters..
separately combained.
Depth Artifacts: The eyas and mouth appoar overly simplified and lock the intricate details and depth that are characteristic of real human features.
Figure 16: Comparisons in image forgery interpretation.
34

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Published as a conference paper at ICLR 2026
KOVVUME UARIA
1AB
J2 ARTA
COD 03-33 12:00-00
PE 12-4220-02
WANAREE 4
FARANZA
21
F014 8
Tampered Region: the text: 10.50
Absolute Position: Bottom
image.
5B (Baseline), comprehensive
Relative Position: in the text line 10.50 10.50
inconsistency: tampered "10.50
slightlyy blurred appearance, lacking crispness genuine nearby.
discrepency suggests that the text was edited, its texture would typically match the surrounding elements were authentic.
Tampered Ragion: text
image.
Omni-IMI MMn (Ours), comprehensive
Relative Position: the text line 10.50 10.50
Edge Artifacts: The edges lampered "10. 50" appear less delined compared the surrounding text. There slight blurriness
irregularities boundarles, indicating misrnatch with background texture and color, which suggests tampering. Texture Incorsistency:
The texture of the lampered "10.50" text appears somewhal hazy and less sharg than the surrounding authentic texts, indicates manipulation, Font inconsistency: The '10.50" has an abvibusly different font than the surrounding text. Its larger font size and thinner strake indicate that has been tampered with. Alignment Inconsistency: text "10.50" likely tampered with.
integrated
should normally appear
inconsistent sumounding text. misalignment means
biobo
Tampered Region: the text: Modem
Absolute Position:
image.
Qwen2-5VL-7B (Baseline), incomprehensive
Relative Position: in the text line: 3/64 Modem
compared to letters appear heavier or darker, indicating that distinnt font style weight may have been applied during the editing process.
Inconsistency: tampered
Tampered Region the text: Madem
Absolute Position: Top center the
Omni-IML (Ours), comprehensive
Relative Position:
Edge Artifacts: The edges of the tampered text 'Modem' show noticeable discrepancies compared to the suthentic regions. The background tampering not
abrupt changes searllessly blended with the surrounding background.
tampered agpears uniform,
indicating
the tampered text "Modem" displays variation color thickness compared authentic The letters appear to bolder or darker than surrounding text, suggesting that different font style or weight has been used the tampering
Inconsistency:
Alignment Incorsistency: The positioning the lampered text "Modem" misaligned relation the text line belongs to. There noticeable difference spacing and alignment with the surrounding text, making appear somewhat offeet and disrupting the visual flow of
Tampered
Absolute Position: Right side of the image
Relative Position: the line: Robin
Edge Artifacts: The edges surrounding the tampered text "Robin" exhibit irregulartties compared the authentic areas. background immediatel ately adjacent
text does blend seamlessly, leading to noticeable discontinuity that alterations made. This inconsistency in the edges implies lack of proper integration with surrounding elements. Font Inconsistency: The font used for the tampered text "Robin" differs appearance from the surrounding text. It slightly lighter color and different thickness, discrepancy indicates consistent color and weight.
tampered
the toxt, more
Figure 17: Comparisons in image forgery interpretation.
35

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Published as a conference paper at ICLR 2026
Footpath. Ty Burlington giig Greenstead
Footpath Ty Burlington glig Greenstead
"Tampored Ragion": Burlingtan" "Absolute Position": "Center of the Image"
"Relative
Burlington giig"
"Artifacts":
Artifacts": "The edges currounding tampered "Burlington" slight discontinuity compared
surrounding authentic text. The background behind the tampered toxt less consictent with the authentic regions, indicating tampering where the text has been altered replaced."
"Font Inconsistency". " "The font the tampered text "Burlington" displays noticeable differences in color and thickness compared the authertic text. The letters in "Burlington" are darker and thicker resulting mismatch with the style of the text above and below it, The font appears slighlly stretched distorted, which atypical texture of the text Burlington' apoears unnaturally smooth, lacking the subtle pores and minor natural resulting artificial appearance.
surrounding
"Texture inconsistency"":"
distortions
"High-level Artifacts": 'The inclusion of "'Brlligtoo" within the text line Burlligton gie" breaks the flow of the sentence The coherent English, suggesting tampered
additional characters after "Burlington" text disrupts the intended meaning of the sign.'
confusion, they
gig. "Absolute Position": Right side of the image"
"Tampered Region":
"Relative Positiio:: 'In the textline: Burlington
"Artifacts":
"Edge Artifacts": : "The edges around the tampered text "siig" display noticeable incorsistencies. The background texture does blend searnlessly with the surrounding suthentic text, resulting jagged or uneven appearance. This discontinuity at the edges indicates lack of integration with the original signage.",
of "gile" appears slightly lighter and less bold compared to the surrounding text "Burlington" Additionally, the style and thickness of the letters are not uniform text, creating evident discrepancy."
"Font Inconsistency". : "The
tampered text
surrounding
aspects.
"Alignment Inconsisteney" "The positioning of the tampered text "gli"" shows misalignment with the rest of the text line. There an observable spacing irrggulrrty, where does not properly aligned with "Burlingtan": This misalignment detracts from the overall coherence of the text line.!
"Texture Inconsistency': : 'The texture the "pigg locks uniformity: certain regions overly smooth, while others exhibit unnatural patterning"
"High-low lovel Artifacts": 'The tampered text "glie" appears to be misspelling and does not make sonse within the context of the phrase "Burlington gigg.. tampering."
likelyy "Burlingto", which inclusion "ggii" nonsensical incoherent, indicating
"Tampered Region": "The text: Ty"
"Absolute Position": " Top right of the image"
"Relative Position: "In textline: Footpath.Ty" "Artifacts":
Artifacts .....
"Allgnment Inconsistancy" ":
"Texture Inconsistency"
"High-level Artifacts": ......
Figure 18: A representative sample to display our Omni-273k's structured annotation format.
36

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Published as a conference paper at ICLR 2026
"Tampered Region": The text: "pdas", "Absolute Position": Center of the image, "Relative Position": In the textline: "pdas" "Artifacts": {
"Edge Artifacts". : "The edges around the tampered text "pdlas" are inconsistent with background. There noticeable blur or softness edges the letters, suggesting that the with the background. leading discontinuous appearance."
blend seamlessly
pdas
"High-level Artifacts", "The word "pdas" should not be on the signboard because incongruaus with standard traffic sign conventions. Official cirectional signs use specific, universally understand text (like place names or instructions), and "pdas" does not fit any recognized category. suggesting has been digitally added."
Tampered Image
Localization
Netwark
Explanation Network
Empty Mask Prediction (Wrong)
Figure 19: Our explanation module is designed to be robust against localization failures. It uses the localization mask as guide but is not strictly bound by it. For example, if the localization module fails to detect a tampered region (i.c., predicts an empty mask), the explanation module can still independently identify the anomaly and report it, effectively correcting the initial error. In the predicted mask of this figure, white denotes real region, blue denotes tampered region.
37

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Published as a conference paper at ICLR 2026
1
9
9
6
2
4
3
1
9
2
5
2
Tampered Text Face Images
Mask A nnotations
Connected Components
Corresponding Objects
Split Objects
Single Tampered Natural Region
Mask A Anotaaioos
Connected Components
Corresponding Objects
Split Objects
+
1
(1) Request PT-440 to split different objects and group regions of the same object
Multiple Tampered Mask Natural Regions Annotations
Split Objects
(2) Human inspection
Figure 20: To split individual tampered objects, we employed a domain-specinf strategy. For docu- ment, scene-text, and face images, where tampered objects typically consist of single contiguous region, we used commected-componect component analysis on the annotation masks. For natural images, where single object may comprise multiple disconnected regions, prompted GPT-4o group mask fragments into semantic objects, followed by manual verification to ensure accuracy.
38


Reviews:
[{"review_id": "RiJlkhuErH", "reviewer": "Reviewer_91UG", "summary": "This paper introduces an unified model named Omni-IML for the localization and interpretation of image manipulation. Three novel modules are proposed in the Omni-IML, effectively addressing the task conflicts in the unified training. An interpretation module is proposed to improve model’s forgery interpretation accuracy by prompting the multi-modal large language model with the localization model’s prediction. The proposed Omni-IML achieves state-of-the-art average performance across four major image manipulation localization tasks.\nThis paper also contributes the Omni-273k dataset to support better training on image forgery interpretation, it is initially constructed by a chain-of-thought pipeline, effectively reducing human-washing cost. The proposed structured label format enables detailed and reasonable evaluation.", "questions": "See the concerns raised in the 'Weaknesses' section.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 3, "contribution": 4, "strengths": "Unified generalist model across four IML domains\n- A single model achieves strong generalization and SOTA performance on natural, document, face, and scene text tasks without per-task tuning.\n- Addresses fragmentation and lack of generalization in prior task-specific solutions with a well-motivated unified design.\n\nSample-adaptive architecture design\n- Modal Gate Encoder adaptively selects the optimal modality (RGB vs frequency+vision) per sample.\n- Dynamic Weight Decoder introduces per-sample dynamic filters to mitigate joint-training degradation and improve feature fusion of low- and high-level features.\n- Anomaly Enhancement module is cleverly designed to enhance forged-region contrast using box supervision, with empirical support in ablations.\n- Visual prompts assist correct tampered-region identification, yielding more accurate artifact descriptions compared to prior methods.\n\nThe Omni-273k dataset as a substantive contribution\n- Large-scale coverage across four major IML tasks, enabling comprehensive training and evaluation.\n- Structured JSON annotations enable fine-grained, reasonable evaluation and analysis, improving over unstructured labels in prior work.\n- Chain-of-thought automatic annotation pipeline enhances data availability and quality.\n\nExtensive experimental validation\n- Thorough experiments, ablation studies, and comparisons convincingly demonstrate the effectiveness of each proposed module and the unified system.\n- Results consistently verify performance and generalization across diverse tasks.\n\nTechnical soundness and clarity\n- Methodology is technically sound, with coherent component design and integration.\n- Writing and paper structure are clear and easy to understand.", "weaknesses": "- Regarding the Modal Gate, other auxiliary modalities, except for frequency may produce better results. Also, a soft gate weighting fusion may be better than the hard gate in the current version.\n- The model’s parameter size and computational burden are relatively heavy; it includes a multi-modal large model in both training and inference. \n- The font size figure2 and figure3 is too small. It is suggested to simplify the content and enlarge the font size.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VpmITGPlL3", "reviewer": "Reviewer_d5Rc", "summary": "The paper proposes Omni-IML, aiming to unify four major image manipulation localization (IML) tasks—natural images, documents, faces, and scene text—within a single general model. The core architecture comprises three components: a Modal Gate Encoder (sample-adaptive modality selection), a Dynamic Weight Decoder (sample-adaptive decoder filters), and Anomaly Enhancement (box-level supervision to boost contrast of manipulated regions during training). In addition, the paper presents an explanation module that overlays the predicted mask as a visual prompt to an MLLM, and constructs a large-scale dataset with structured text annotations, Omni-273k. Experiments claim single-model SOTA across multiple benchmarks in the four IML categories with minimal degradation under joint training. The authors commit to releasing code and data.", "questions": "1. Can the overall architecture be light-weighted for deployment (e.g., smaller backbones, distillation, pruning)? How does the end-to-end latency compare to methods with similar or larger model sizes (e.g., PIM, UPOCR) under the same input resolution, given that the paper reports FPS only?\n2. Can the localization and explanation modules be jointly trained, and what are the effects? How does the explanation module behave under missed detections? Any attempts at feedback from explanation to localization for error correction?\n3. Can you provide justification for the 10:5:2:20 cross-domain sampling ratio? Given the very small number of scene-text samples, does heavy re-sampling cause overfitting or bias in practice?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. Introduces the first practical general IML model, Omni-IML, addressing the long-standing issue that existing methods suffer significant performance drops under joint training.\n2. Novel and effective architectural design: the Modal Gate Encoder elegantly resolves the “double-edged sword” of frequency features—very helpful for document images but potentially noisy for natural images—by adaptively selecting the optimal modality. The Dynamic Weight Decoder adjusts filters dynamically to cope with diverse manipulation types. The Anomaly Enhancement module leverages box supervision during training to strengthen anomaly cues without adding inference cost, which is a neat design.\n3. Builds a large-scale, high-quality, and comprehensive dataset, Omni-273k, providing stronger foundations for the field, along with a novel automatic annotation pipeline.\n4. Extensive experiments validate the effectiveness of the proposed method.", "weaknesses": "1. The main contribution is performance, but it comes with higher computational cost. As shown in Table 16, Omni-IML (152M parameters) is larger than most methods (e.g., DTD 66M, SparseViT 50M) and its inference speed (7.1 FPS) is slower than most methods. Considering practical deployment, such high computational cost could be a barrier.\n2. The localization and explanation modules are independent. The explanation module simply uses the localization mask as a visual prompt; there is no end-to-end or error-correction feedback mechanism. If the localization module misses regions, the explanation module receives no prompt there, likely leading to explanation failure. This loose coupling limits the system from being a truly end-to-end interpretable solution.\n3. The paper states a cross-domain sampling ratio of 10:5:2:20 for natural, document, face, and scene-text images, respectively. However, the scene-text training set has only 229 images yet receives the highest weight. Such extreme re-sampling may introduce generalization/bias side effects. The paper currently lacks sensitivity analyses or rationale justifying this choice.\n4. Numerous writing and typesetting issues hinder readability. For example, typos in the first line of the intro (“Mmanipulated”, “servious”), the title of Figure 5 (“Anomanly Enhancement”), and “Singe and multiple” in Table 1.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "IE61WxoZjs", "reviewer": "Reviewer_FDt3", "summary": "The paper introduces Omni-IML, a unified model for interpretable image manipulation localization (IML) across four major domains: natural images, documents, faces, and scene texts. It addresses the limitations of task-specific IML models by proposing a generalist architecture with key components: a Modal Gate Encoder for adaptive modality selection (vision-only or vision+frequency), a Dynamic Weight Decoder for sample-adaptive filtering, and an Anomaly Enhancement module using bounding box supervision to highlight tampered regions. Additionally, the authors construct Omni-273k, a large dataset with 273k samples annotated via a chain-of-thoughts (CoT) pipeline using GPT-4o for natural language descriptions of tampering artifacts. An interpretation module based on a multimodal large language model (MLLM) is introduced to generate textual explanations.", "questions": "1.  Why is repeated reasoning (e.g., self-examination) necessary for low-level artifacts like texture inconsistencies, which seem more perceptual than contextual? Could a simpler single-prompt approach suffice for document or scene text domains, and what ablation results support the added complexity?\n2.  How do the Modal Gate and Dynamic Weight Decoder differ substantially from existing gated or dynamic mechanisms (e.g., in multimodal fusion or dynamic convolutions)? Can you provide comparisons to show that these are not just incremental adaptations?\n\n3. In cases where tampering is subtle and low-level (e.g., no strong semantic context), does the visual prompt risk introducing hallucinations in the MLLM? What metrics were used to evaluate this, and how does it perform on edge cases like multi-instance documents?\n\n4. Given the incremental nature of the pipeline, how does Omni-IML handle novel tampering methods not seen in the training datasets, such as those from future diffusion models? Are there cross-domain transfer experiments to validate true generalization?\n\n5. Since the interpretation module is separate from the localization pipeline, how does this design choice impact the model's ability to provide consistent and context-aware artifact descriptions? Would integrating interpretability into the Dynamic Weight Decoder or other core components improve overall performance or coherence?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. Unified Generalist Approach: This is the first work to effectively unify IML across diverse domains (natural, document, face, scene text) in a single model, achieving SOTA results on multiple benchmarks. It provides a practical solution for real-world deployment, reducing the need for multiple specialized models and addressing high maintenance costs.\n\n2. Large-Scale Interpretable Dataset: Omni-273k is a comprehensive, high-quality dataset covering all four IML domains, with structured JSON annotations for tampered content, positions, and artifacts. The CoT annotation pipeline improves quality over prior single-prompt methods, enabling finer-grained evaluation and supporting interpretable forensics.\n\n3. Effective Architectural Innovations for Generalization: Components like the Modal Gate (for modality adaptation) and Dynamic Weight Decoder (for handling diverse tampering clues) enable robust performance across tasks. The Anomaly Enhancement module leverages box supervision to enhance task-agnostic features, and the simple interpretation module (using visual prompts) boosts reliability without altering the MLLM structure.\n\n4. Strong Empirical Validation: Extensive experiments on representative benchmarks show minimal performance degradation from joint training, with Omni-IML outperforming domain-specific SOTAs. The work also highlights interpretability, a underexplored aspect in unified IML.", "weaknesses": "1. The CoT annotation pipeline, while effective, feels incremental and assembled from existing ideas (e.g., step-by-step prompting and self-examination in GPT-4o). It may not be necessary for low-level tampering artifacts (e.g., texture or edge inconsistencies), which are often straightforward and do not require repeated reasoning or context-heavy analysis, potentially overcomplicating the process without strong justification.\n\n2.  Modules like the Modal Gate and Dynamic Weight Decoder rely on well-established techniques (e.g., gating mechanisms for modality selection, dynamic filtering akin to those in dynamic networks). This makes the contributions appear more as combinations of prior art rather than groundbreaking innovations, reducing the perceived technical depth.\n\n3. The focus on low-level artifacts (e.g., font/alignment inconsistencies in documents) might limit generalizability to emerging tampering methods, such as those from advanced generative models. The evaluation could benefit from more diverse, real-world scenarios beyond the curated datasets.\n\n4. While the CoT pipeline is claimed to improve annotation quality, there is insufficient evidence (e.g., human evaluations or comparisons to non-CoT baselines) to demonstrate its necessity, especially for simpler domains where context information is weak.\n\n5. SThe interpretation module operates as a standalone component, not integrated with the localization process (e.g., the Dynamic Weight Decoder). This separation may reduce the model's ability to provide cohesive, context-aware explanations, potentially missing opportunities for joint optimization of localization and interpretability.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "al8Laxoysu", "reviewer": "Reviewer_8wZG", "summary": "This paper presents Omni-IML, a generalist model for Image Manipulation Localization. Its core methodological contributions are threefold: a Modal Gate Encoder for adaptive modality selection, a Dynamic Weight Decoder with task-aware filters, and an Anomaly Enhancement module that uses box supervision to learn task-agnostic features. The proposed single-model solution aims to unify multiple IML tasks, marking a promising step towards generalist models in image forensics.", "questions": "see the weaknesses.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 2, "strengths": "Unified image forgery locazation is an study-worthy problem, and this paper also presents a promising solution for this problem. The paper is well organized and easy to follow.", "weaknesses": "1, My main concern is which is the primary focus of this paper?  a unified framework or interpretable? both hold many pages in methodology, but their connection is low. The explanation module integrates the IML results and finetune a MLLM for explanation text. However, the explanation seems does not benefit the IML accuracy in return, leading to the explanation section is relatively separate from the main focus. To me,  the localziation accuracy  is more important than explanation for IML problem, so it is reasonable and intuitive to promote localizaiton accuracy with explanation guidance. \n\n2, Does the MLLM joinly tune with the localization networks?\n\n3, how is the bbox supervision obtained in anmaly enhancement ?\n\n4, During the dataset construction,  how to split the image to several tamperred objects from the binary mask? was it achieved with regulations or some algorithms?\n\n5, As the localization mask has been given before generating the explanation, with this, the tampered region seem too simple. Besides, the artifacts description  is also too simple and general.\n\n6, the parameter and inference time should be compared.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)