Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection
Authors: Yao Xiao
Weiyan Chen
Jiahao Chen
Zijie Cao
Weijian Deng
Binbin Yang
ZiYi Dong
Xiangyang Ji
Wei Ke
Pengxu Wei
Liang Lin
Venue: ICLR 2026 Poster (2026)
Keywords: AI-generated Image Detection
Perceptual Artifact
Primary area: datasets and benchmarks
TL;DR: We introduce a fine-grained benchmark for interpretable AI-generated image detection and explore detection methods that can effectively leverage perceptual artifact for interpretable and reliable judgments.

Abstract: Current AI-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking interpretable or convincing evidence to substantiate their decisions. This limitation stems from existing AIGI detection benchmarks, which, despite featuring a broad collection of synthetic images, remain restricted in their coverage of artifact diversity and lack detailed, localized annotations. To bridge this gap, we introduce a fine-grained benchmark towards eXplainable AI-Generated image Detection, named X-AIGD, which provides pixel-level, categorized annotations of perceptual artifacts, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals. These comprehensive annotations facilitate fine-grained interpretability evaluation and deeper insight into model decision-making processes. Our extensive investigation using X-AIGD provides several key insights: (1) Existing AIGI detectors demonstrate negligible reliance on perceptual artifacts, even at the most basic distortion level. (2) While AIGI detectors can be trained to identify specific artifacts, they still substantially base their judgment on uninterpretable features. (3) Explicitly aligning model attention with artifact regions can increase the interpretability and generalization of detectors.
The data and code are available at: https://github.com/Coxy7/X-AIGD.

Published as a conference paper at ICLR 2026
UNVEILING PERCEPTUAL ARTIFACTS: A FINE-GRAINED BENCHMARK FOR INTERPRETABLE AI-GENERATED IMAGE DETECTION
Yao Xiao"; Weiyan Chen Jiahao Chen Zijie Cao"*, Weijian Deng', Binbin Yang', Ziyi Dong'. Xiangyang Ji Wei Ke Pengxu Wei Liang Lin 1.2 Sun Yat-sen University, PPngg Cheng Laboratory, 'Xi'an an Jiaotong University.
AAstrrllian National University, TTiinguua University
xi xiaoy99 1mail2...... sysu. edu. cn, weipx3 sysu. edu.cc
ABSTRACT
Current Al-Generated Image (AIGI) detection approaches predominantly rely on binary classification to distinguish real from synthetic images, often lacking in- terpretable or convincing evidence to substantiate their decisions. This limitation stems from existing AIGI detection benchmarks, which, despite featuring a broad collection of synthetic images, remain restricted in their coverage of artifact di- versity and lack detailed, localized annotations. To bridge this gap, we introduce a fine-grained benchmark towards eXplainable Al-Generated image Detection, named X-AIGD, which provides pixcl-level, categorized annotations perceptual artifacts, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals. These comprehensive annotations facilitate fine-grained inter- pretability evaluation and deeper insight into model decision-making processes. Our extensive investigation using X-AIGD provides several key insights: (1) Exist- ing AIGI detectors demonstrate negligible reliance perceptual artifacts, even at the most basic distortion level. (2) While AIGI detectors can be trained to identify specific artifacts, they still substantially base their judgment features. (3) Explicitly aligning model attention with artifact regions can increase the interpretabiliity and generalization of detectors. The data and code available at: https::// www/Cooyy/// AIGD
uninterpretable
INTRODUCTION
As Al-Generated Images (AIGIs) become increasingly indistinguishable from real photographs, there is growing demand for detection methods that are not only accurate but also interpretable (Li
al. 2024a Wen et al. 2025 Gao al., 2025). Traditional detection approaches typically frame
AIGI detection as binary classification task, leveraging handcrafted or learned features (Ojha et al.,
2023 Tan et al. 2024 Koutlis & Papadopoulos 2024| Chen et al. 2024a b). While effective on specific datasets, these methods often struggle to generalize across different image generators and are vulnerable to perturbations in image structure and content. More importantly, their reliance on low-level signals offers limited interpretability, providing only binary decisions without identifying specific visual artifacts for decision making, which is unconvincing for practical applications.
To enhance interpretability. series of research (Huang et al. 2024 Xu al. 2025 Liu al. 2024c
Wen et al. 2025 Gao et al.] (2025) attempts to utillze the visual reasoning capabilities of Multimodal Large Language Models (MLLMs) to generate textual explanations for AIGI detection. However, due to the lack of human annotations of detection clues, these works primarily rely on data annotated by stronger MLLMs such as GPT-4o (OpenAll (2024) for model training. which may be unreliable and lack spatial grounding. Recent works (Ye et al. 2025 Kang et al. (025) have made progress by providing numan-annotanl on perceptual artifact localization (Zhang et al. 22023 Cao et al.] 2024). However, these datasets lack systematic and fine-gradd mne-ggai categorization of artifacts, limiting the depth of interpretability analysis.
datasets with localized artifact annotations, following previous studies
"Equal contribution. t: Corresponding author.

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Cognitive-level
Commonsense
Physics
Counterfactuals
MccDonald's
Objects defying spatial, Temporal, functional, or biolagical commorsense: linguistic
Violation of physical
Violations real-world knowledge, commonsense, Fundomental physical laws
lows, egg. inconsistent
shodows and reflections disobeying optical lows
High-level Semantics
Semantics
Objects with semantic enrors or abnernal structure: incorrect or nonsendical composition of differnt parts of obejct
Semantic deviations due to issues structural integrity composition
Low-level Distortions
Edges & Shapes
NATURAL ELEN
Distortions affecting fundamental visual properties and details
Textures
Color
OFTIET
Symbols
Figure Taxonomy Perceptual Artifacts. Our X-AIGD categorizes perceptual artifacts into three hierarchical levels: 1) low-level distortions, 2) high-level semantics, and 3) cognitive-level counterfactuals. Low-level distortions capture fundamental visual irregularities, such as edge mis- alignments, unnatural textures, and color inconsistencies. High-level semantics address structural and compositional errors that disrupt object integrity and logical arrangement. Cognitive-lever counter- factuals encompass commonsense and physical violations that defy real-world knowledge, such as implausible object relationships or violations of physical laws. Red polygons illustrate the precise localization of these artifacts, supporting fine-grained evaluation of AIGI detection models.
Consequently, the development of robust and interpretable AIGI detection methods remains hindered by the lack of reliable and comprehensive datasets that capture the full perceptual artifacts in AIGIs.
To address these limitations, introduce novel benchmark, named X-AIGD (eXplainable AI- Generated image Detection), to elevate the interpretability and robustness of AIGI detection. X- AIGD features pixel-level annotations that span diverse range of perceptual artifacts, systematically categorized into three levels: low-level distortions (e.g., unnatural textures, warped edges), high-level semantics (e.g., abnormal object structures), and cognitive-level counterfactuals (e.g., violations of commonsense or physical laws), as illustrated in Fig. [ This structured taxonomy captures not only easily observable distortions but also deep semantic inconsistencies and logical violations, offering more comprehensive evaluation of model capabilities. For reliable assessment of interpretability and detection performance, we collect high-quality, diverse fake images generated from the captions of real images by advanced generative models (e.g.. FLUX (Labs 2024) and Stable Diffusion 3.5 (Esser et al. 2024)), ensuring semantic alignment and distribution consistency between fake and real image pairs. As summarized in Tab. [ X-AIGD provides paired real and fake images, along with pixel-level annotations and systematic artifact categorization, making it well-suited to support research on interpretable AIGI detection.
Leveraging the fine-grained annotations and paired real and fake images in X-AIGD, we conduct multi-faceted investigation whether and how AIGI detection models can utilize perceptual artifacts as reliable, interpretable cues. We begin by analyzing existing end-to-end AIGI detectors to assess their reliance on perceptual artifacts. Despite their ability to learn diverse features, it is observed that they commonly bypass perceptual artifacts. This underutilization of human human-interpreete interpretable cues suggests an opportunity for improving detection mechanisms by more effectively incorporating artifact-based reasoning.
The limitations observed in binary classification motivate us investigate whether explicitly detecting perceptual artifacts could enhance both detection performance and interpretabilityy Utilizing the annotated images in X-AIGD, we explore several approaches that adopt artifact detection as an auxiliary task to guide authenticity judgment, including transfer learning and joint training. While these models show promise in identifying certain low-level artifacts, their authenticity predictions still predominantly depend uninterpretable features, and the performance gains from artifact detection are at most marginal. This observation underscores the challenge of aligning detection decisions with transparent, artifact-based reasoning.
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To further explore the potential of artifact-basee reasoning, investigate whether explicitly aligning model attention with artifact regions can enhance the interpretability and generalization of AIGI detectors. By incorporating attention alignment loss during training, we observe improved focus on artifact regions and significantly enhanced performance in cross-dataset evaluations. Further trade-off analysis the penalty of attention outside artifact regions reveals that balancing the learning of perceptual artifacts with other useful features is crucial for optimal performance in practice. Overall, our findings highlight the potential of artifact-aware detection and the complexity of developing AIGI detectors that are both accurate and interpretable, emphasizing the need for continued research into AIGI detection methods that effectively leverage perceptual artifacts.
Our primary contributions are summarized as follows:
We propose X-AIGD, a fine-grained benchmark designed to facilitate in-depth exploration of interpretable AIGI detection, featuring localized, multi-level artifact annotations.
Our analysis reveals that existing AIGI detectors underutilize perceptual artifacts, hindering interpretability and detection robustness. Further, we find that detectors trained with artifact detection as an auxiliary task still rely heavily on uninterpretable features, highlighting the challenge of aligning detection with perceptual cues.
We demonstrate that aligning model attention with artifact regions can enhance both inter- pretability and generalization, underlining the potential of artifact-aware detection methods.
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RELATED WORKS
Al-Generated Image Detection. Existing AIGI detection datasets proposed for the binary classifi-
cation scenario (Zhu et al.) 2023 Bammey 2023 Baraldi et al.) 2024 Chen al. 2024a) typically comprise paired real and fake images and cover a variety of generative models, focusing on the evaluation of the generalization ability of AIGI detectors. Based on these datasets, most previous
studies either adopt end-to-end models (Wang et al. 2020b Ojha et al. 2023 Koutlis & Papadopou los 2024: Chen et al. 2024a Liu al. 2024b). or propose fingerprint- baasepritt-bbeed based methods (Wang et al. 2 Tan et al., 2024. Zhong et al. [2024] Chen et al. (2024b) that rely on certain low-level synthetic traces such as the up- up-sampling artifacts (Tan et al.) 2024). Despite their reported high accuracy, the interpretability of these models is not well-studied due to the lack of datasets with comprehensive and grounded annotations of the visual clues for AIGI detection.
Benchmarks for interpretable AIGI detection. To support the research on interpretable AIGI detection, existing works (Li et al. 2024a Gao et al., 2025 Wen et al. 2025) have primarily focused on MILMs and introduced benchmark datasets with textual annotations providing interpretations of artifacts. However, their interpretability evaluation often involves comparing the models' textual outputs against ground truth text at the image level, lacking grounding to specific anomaly regions. prevents verifying whether the model's interpre-
This undermines the efficacy of the evaluation, tation truly based on relevant visual evidence. LOKI (Ye et al. 2025) and SynthScars (Kang et al.) 2025) provide localized annotations image anomalies based on human-anna annotated bounding boxes or pixel masks. Nevertheless, they treat artifact detection as separate task from AIGI detection with minimal examination of the their synergy. Moreover, the lack of paired real images limits their applicability for developing and evaluating interpretable AIGI detectors.
Perceptual Artifact Localization for Generated Images. Previous studies related to perceptual
artifact localization (Zhang et al. 2023 Cao et al., 2024; Liang et al. 2024 Fang et al. 2024 Wang al. 2025) aim to construct artifact localization models that provide feedback for tuning generative models or regional guidance for repairing generated images. Therefore, their collected datasets mainly focus on specific artifacts that deteriorate the visual quality. In contrast, our X-AIGD aims at the comprehensive evaluation and fine- -rained interpretability analysis of AIGI detectors, thus includes wider range of artifacts that serve detection clues, and provides paired real images.
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Real Images
Artifact Amnotation
Localization
#Category
Image praportonn
PALAYST (Zhang
SynArtfact Cao et
2024
K
Bounding box
13
RichHF-18K Lang et
Point
N/A
0.45
LOKI (image)
(Yeet
2015
Unpaired Bounding box
NIA
FakeRench
(1 N1243
Unpaired
NIA
14
0.30
MMFR-Daasse Gho
2072
Paired
N/A
SymthScars (Kang et al. 2025
X
Pixel mask
0.15
0.02
0.03
(ours)
0.00
[0.00,
0.25) [0:25, 0.50) [0.50,0.1 0.75) [0.75, 1.00] Average confidence score
Table I: Comparison with existing datasets. X- AIGD provides detailed annotations (pixel-level masks and categorized artifact labels), real images paired with the fake ones, supporting fine-gra ane-ggained evaluation. PAL4VST, SynArtifact, and RichHF- 18K are for perceptual artifact localization.
Figure 2: Human assessment of annotation quality. The distribution of average confidence scores assigned by human annotators indicates the overall high quality of artifact annotations.
3 X-AIGD BENCHMARK
3.1 IMAGE COLLECTION
The reliable and comprehensive evaluation of the interpretability of AIGI detectors requires a high- quality and diverse dataset of real and fake images, where reasonable number of perceptual artifact instances can be identified and localized for most fake images. To this end, we collect fake images depicting realistic scenes based on the semantics of real images from various sources. Specifically, we source real images from four existing datasets, and obtain captions of these images with different levels of detail, which serve as diverse set of text prompts for fake image generation. To ensure the quality of the generated images, we use 13 up-to-date text-conditioned image generation models (e.g., PixArt-c (Chen et al.] 2024c), FLUX. 1-dev (Labs) 2024), and SD 3.5 (Esser et al.) 2024)). We also include community fine-tuned models on Civitai (Civ) that aims at improving generation realism. Prompt engineering (Liu Chilton 2022) techniques are applied to further enhance image quality and suppress non-realistic styles following (Baraldi et al.) (2024). In total, collect 4,000 real images and generate 4,000 corresponding fake images from each of the 13 generators, resulting in 52,000 fake images. Additional details provided in Appendix A
Paired real and fake images with semantic alignment are important for assessing models' ability base their judgment on localized perceptual artifacts instead of merely the overall semanties of the images. However, as compared in Tab. [ the real images provided by existing datasets for interpretable AIGI detection (i.e., LOKI (Ye et al. 2025), FakeBench (Li et al. 2024 are not paired with their fake images. Furthermore, datasets proposed for perceptual artifact localization (Zhagg al. 2023; Cao et al. 2024 Liang et al.) a (2224) do not provide real images and therefore are less suitable for the study on interpretable ATGI detection. A more comprehensive comparison of related datasets presented in Tab.
3.2 ANNOTATION OF PERCEPTUAL ARTIFACTS
For reliable interpretability assessment, ground truth annotations should be complete and accurately capture perceptual artifacts of all relevant types perceivable by humans. To this end, we define comprehensive artifact taxonomy comprising 3 levels and 7 specific categories, as shown in Fig. T We recruit 12 human annotators label artifacts in fake images by localizing them with pixel-level polygon masks and assigning the corresponding category according to our taxonomy. To improve annotation completeness, each image undergoes three successive rounds of annotation by different annotators. Additionally, we exclude images flagged as low-quality unrealistic by annotators.
Given the labor-intensive nature of annotation, we select a subset of the fake images collected in Sec. 3.I for detailed annotation. Specifically, the test set comprises 200 fake images for each of the 13 generators, along with their corresponding real images. The training set is constructed similarly using images from subset of 5 generators. Following annotation and filtering, obtain total of 3,035 valid annotated fake samples. To evaluate the annotation quality, we randomly sample a subset of annotated fake samples and ask three independent annotators assign confidence score
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CNNSpot
Cram
+
UnivFD
Forerrrr
DRCT-ComwB
DACT-CLIP
CoDE
Propartion
1.0
1.0
1.0
Authenticity ludgmentt Accuracy
0.25
0.20
0.8
0,20
0.8
0.16
0.8
022
0.15
0.12
0.18
0.10
0.00
0.2
0.05 0.2
0.
0.
(26.3)
13)
(10 E31
1.00 0.0
18.3)
0.00 0.0
0.00
(8..
(62
1s5,
19
0M4 un
08
02.0
125
023
MDPS
(c) Perceptual Artifact Ratio
Figure 3: Accuracy of existing AIGI detectors on AIGIs with different fidelity. The image fidelity is measured by (a) NIQE (Mittal et al. 2012); (b) MDFS (Ni et al. 2024); (c) Perceptual Artifact
Ratio (Zhagg et al. 2023) computed based on our annotations. For these three metries, higher values
indicate lower fidelity.
from 0, 0.5, I to each annotated artifact instance. The distribution of the average confidence scores presented Fig. 2 indicating generally high annotation quality, despite a small proportion of controversial instances due to the subjective nature of the human perception of certain artifacts.
3.3
TASK SPECIFICATION
The X-AIGD dataset is designed for interpretable AIGI detection, task that requires models to provide interpretations for their judgment of image authenticity. Specifically, we focus on grounded interpretations, which provide localized evidence (i.e., artifact regions) and categorical explanations (i.e., artifact types) understandable to humans. Formally, interpretable AIGI detection can be divided into two subtasks: (1) Authenticity Judgment (AJ)): Predict the binary label y {0. 1}, where y = 1 suggests fake image generated by AI, and O indicates the image is real-worke photograph. For this binary classification task, we adopt balanced accuracy (Ace), defined as the average of real-image accuracy and fake-image accuracy, along with precision (P), recall (R), and Fi-score (F1), to assess the model performance. (2) Perceptual Artifact Detection (PAD): the image is predicted to be fake, detect set of perceptual artifact instances. For each detected instance i, predict its region T; and category e E C. The set of categories C' includes 7 types of perceptual artifacts spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals, as illustrated in Fig. [ To assess the models' ability in detecting artifacts of a specific category, we compare the model predictions with ground-truth artifact regions of this category, and calculate the Intersection over Union (IoU), as well as pixel-level precision, recall, and Fy-score (PixP, PixR, and PixF1). ). In addition, we propose alternative instance-level weak-localiizatonn metrics, which are compatible with a wider range of models and are noise tolerant, as discussed in Sec. A.5) To accommodate the evaluations of models unaware of the artifact categories of X-AIGD, we propose a Category-Agnostic PAD setting, which focuses on the existence of perceptual artifacts without considering the categories. Specifically, for each image, we obtain ground-trute binary mask from the union of annotations of all categories, and compute the same metrics PAD based the predicted binary mask of artifact regions.
ASSESSING THE INTERPRETATION OF EXISTING AIGI DETECTORS
Despite the performance of existing AIGI detectors on previous benchmark datasets (Wang et al. 2020b Chen et al., 2024a] Bammey 2023), it is unclear whether they can detect any kind of perceptual artifacts, given that they are not explicitly designed to align their detection clues with human perception. In this section, conduct quantitative and qualitative analyses based on X-AIGD to assess their ability to detect perceptual artifacts.
AIGI detectors. Our interpretability investigation focuses end-to-end expert AIGI detection models, which potentially learn a wide range of features for AIGI detection, including perceptual artifacts. We evaluate the following pre-trained models: CNNSpot (Wang et al. 2020b), Gram-
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Fake Image
FatFormer
DRCT-CLIP
Muiti-task (Classfication)
Multi-task (Segmentation)
Ground Truth
Lovelda
High -evel
Cognitive level
Low-level:
Textures
Shaps&Shapes
High-level:
Semantics
Cognitive-level:
Physics
Figure 4: Qualitative comparison of the interpretations of different models. Columns 2-4 show the interpretation heatmap for existing AIGI detectors (FatFormer (Liu al. 2024b) and DRCT- CLIP (Chen et al. (2024a)) and the classification head of the multi-task model. Column 5 is the artifact segmentation results of the multi-tar model. The last column shows the ground truth annotations.
Net (Liu 2020), UnivFD (Ojha 2023). FatFormer (Liu al. 2024b). DRCT-CLIP (Chen
et al.) 22024)), DRCT-ConvB (Chen et al. 2024a), and CoDE (Baraldi et al. (2024). Further details are presented in Appendix B. In addition, we provide dedicated evaluation of MLLMs and related
discussions in Appendix
Relationship between authenticity judgment accuracy and image fidelity. Intuitively, AIGIs with lower fidelity or more perceptual artifacts should be easier to detect if models effectively leverage these artifacts as cues. explore this, we evaluate existing AIGI detectors on samples with varying fidelity levels from X-AIGD test set, measured by NIQE (Mittal et al.) 2012). MDFS (Ni et al. 2024), and Perceptual Artifact Ratio (PAR) (Zhang et al. 2023). While NIQE and MDFS are no-reference quality metrics, PAR quantifies the proportion of artifact region in images based on annotations. Interestingly, as shown in Fig. 3 the detector accuracy does not have significant correlation with image fidelity. Moreover, models do not consistently perform better AIGIs with visible artifacts (PAR >> 0) compared to those without (PAR = 0), indicating their limited utilization of perceptual cues for detection.
Quantitative evaluation on model interpretations. To evaluate the potential capability of models in detecting various types of perceptual artifacts, quantitative analysis conducted based on the inter- pretation heatmaps for the fake class as model interpretations. We apply Grad-CAM (Selvaraju et al.) 2017) to CNN-based models and Relevance Map (Chefer et 2021 Vision Transformers (Doso- vitskiy et al. 202T). For patch-based models (DRCT and CoDE), heatmaps are generated through sliding-patch approach inspired by Chai et al.) (2020). The heatmaps are binarized with threshold of 0.5 and compared against the ground-truth artifact masks. The results in Category-Agnosse PAD (Tab. 2
indicate weak alignment between model interpretations and human perception.
Qualitative analysis. We visualize the interpretation heatmaps of two representative models, Fat- Former (Liu et al. (0224b) and DRCT-CLIP (Chen et al. 2024a), alongside the ground-truth masks of perceptual artifacts in Fig. 4 It is shown that FatFormmr primarily concentrates background regions with smooth textures, while DRCT-CLIP exhibits more scattered activation pattern with heightened focus edges. These qualitative results indicate that neither model have consistent attention to perceptual artifacts, reflecting limitations in artifact-awar aware detection. Additional examples presented in Fig. T2

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Table 2: Comparison of existing AIGI detectors and the models trained on our dataset on Authenticity Judgment and Category-Agnostte PAD. For existing models, the binarized interpretation heatmaps of the classifiers are used for the evaluation on Category- artifact annotations, models achieve non-trivial performance on Calegory-Agnostic PAD, but transfer learning from PAD to AJ and multi-task learning yield most marginal improvements in AJ.
PAD. Training on our
Authenticity Judgment
Category- PAD
Model
Ace
P
R
Fi
Ioll
PixP
PixR
PixFi
CNNSpot (Wang et al.
2020b
48.6
39.7
5.6
9.8
0.9
7.7
1.0
1.8
Gram-Ner
Liu at
2020
62.7
57.7
95.0
71.8
5.8
16.9
8.1
11.0
UnivED
ha
2023
64.1
1.0
0.4
10.1
0.4
0.8
FatFommr
Lu
$7.3
16.3
0.5
0.5
0.9
DRCT-ComvB
Chen
2024a
82.5
88.4
74.8
81.0
9.0
14.8
18.8
16.6
DRCT-CLIP
Chen a
20248
53.0
52.2
71.4
60.3
0.7
13.7
0.8
1.5
CoDE
Baraldi et al.
2024
76.5
93.1
57.2
70.9
2.9
11.5
3.8
5.7
AJ-only
89.3
96.3
85.8
90.2
7
7
PAD-only
/
/
27.2
40.4
45.4
42.7
Transfer learning (lincar probing)
93.5
73.9
Transfor learning (full fine-tuning)
93.5
91.6
92.5
Multi-task leurning
92.6
92.0
92.3
27.3
36.2
52.5
42.8
Table Fine-graiee Fine-grained evaluation on Perceptual Artifact Detection. The multi-task and single-task (PAD-only) models perform better on low-level artifacts but struggle with high-level ones.
Low-level
High-level Semantics
Cognitivellveel
Textures
Edges&Shapes
Symbols
Color
Commonsense
Physics
PixP
PixR
PixP
PixR
PixP
PixR
PixP
PixR
PixP
FixR
PixP
PixR
PixP
PixR
PAD-only Multi-task
17.7
9.1
32.8
50.1
43.8
54,2
10.9
2.5
19.9
23.5
11.3
2.2
2.9
0.3
14.9
13.6
29.8
85.6
43.3
52.3
8.4
5.4
17.9
25.5
7.4
2,2
34
0.8
5 EXPLORING PERCEPTUAL ARTIFACT DETECTION FOR INTERPRETABLE AIGI DETECTION
Given the limited interpretability of existing AIGI detectors, it is natural ask whether more interpretable models can be developed by explicitly detecting perceptual artifacts to justify their decisions. Although Zhang al. (2023) and Kang et al. (2025) have demonstrated the feasibility of training segmentation models to detect certain types of perceptual artifacts, it remains unexplored whether AIGI detectors can effectively utilize the perceptual artifacts as reliable and interpretable cues for authenticity judgment. To explore this, we employ PAD as an auxiliary task guide authenticity judgment via transfer learning or multi-task learning, and analyze the model behavior on both tasks.
PAD auxiliary task for AJ. To investigate whether PAD can enhance the interpretability and performance AIGI detectors, we explore two approaches: 1) Transfer learning, where segmentation model is first pre- pre-trained on PAD and then used to initialize the backbone of binary classification model for linear probing or full fine-tuning on AJ; 2) Multi-task learning. where single model is jointly trained both tasks. For unified comparison, Transformer (Liu et al. 2021) as the backbone for all models. Details are provided in Appendix B.2
the same Swin
To validate that both the PAD-only model (i.e., the pre-trained model used for transfer learning) and the multi-task model effectively learn from the human annota- tions, we compare their segmentation results with the interpretations of existing AIGI detectors. The PAD performance in Tab. shows that both models achieve significantly better results than existing models, demonstrating their non-trivial ability detect perceptual artifacts. Furthermore, to understand their strengths and weaknesses in detecting different types of perceptual artifacts, we report category-specifie results in Tab. 3 and qualitative visualizations Fig. First, for low-level artifacts, the models perform relatively well in detecting distorted edges, shapes, and symbols, as indicated by the quantitative results. Second, while some high-level artifacts are detected, the models tend to associate certain objects parts with structural errors (e.g., the horses in the second row of Fig, 4). and struggles to differentiate semantically correct instances from incorrect ones. Third, the model exhibits clear limitations in detecting cognitive-leve artifacts, such incorrect reflections (e.g., e. the third row of Fig. 4. which highlights the inherent challenges of traditional vision models reasoning about commonsense and physical consistency. Overall, despite promising results
Fine-grained evaluation PAD.
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for certain artifact types, the models fall short in generalizing across the full spectrum of perceptual artifacts, underscoring the complexity of the PAD task and the gap in interpretability.
Case study: text rendering artifacts. To fur- ther demonstrate the PAD capability of the multi- task model, we conduct case study on text ren- dering artifacts, which are common in AIGIs. According to our artifact taxonomy, text render- ing issues span both low-level distortions and cognitive-level counterfactuals. Distorted let- ters are detectable without understanding word spelling or meaning, whereas legible texts re- quire linguistic and commonsense knowledge to identify spelling mistakes or nonsensical ex- pressions. The qualitative examples in Fig. 5 illustrate that the multi-task model effectively identifies distorted letters in case (a) but pro-
Low-level:
Edges & Shopes
Symbols Cognitive-lvvel:
Commonsense
Nera Fukan Ner Drptinoay The Care
Finger Coral:
Bibeke, ele ginfe udieligud
PROMISE
Hawaili
Monzialowe
Nera Fuean
Finger
For Spptineee
Coral:
Blbck the gmf
PROMISE
Hawaili
Moriinine
Figure 5: PAD results of multi-task model on different types of text rendering artifacts: (a) distorted letters; (b) spelling mistakes; (c) nonsen- sical words; (d) no evident issues.
duces false alarms for legible texts in case (d). As anticipated, struggles with spelling errors in case (b). Interestingly, the model detects some nonsensical words as commonsense violations in case (c), yet the predicted masks are intermingled with low-level artifacts, indicating its limited capacity to differentiate between types of text-related issues.
Analyzing the effect of auxiliary PAD learning on AJ. To investigate whether learning the PAD task can enhance the interpretability and performance of AIGI detectors, we first compare the transfer learning and multi-task models with the single-task baseline (i.e., AJ-only). As suggested by the comparisons in Tab. linear probing after PAD pre-training leads significantly lower recall, indicating that the features learned from PAD alone are insufficient for effective AJ. Full fine-tuning and multi-task learning yield slightly better Fi-scores than the AJ-only model, suggesting a limited positive effect of PAD on AJ. Furthermore, to assess how the multi-task model may depend on the perceptual artifacts for authenticity judgment, we compare the classification Grad-CANe heatmaps and the segmentation outputs in Fig. 4 Despite certain overlaps on low-level artifact regions, the heatmaps show high activations on some background areas of the segmentation. In other words, the model still tends to rely uninterpretable features for authenticity judgment, and the perceptual artifacts detected by the segmentation head are not necessarily the main clues for the model's ' judgment. Additional quantitative evaluation of interpretations is presented in Appendix D.3
IMPROVING AIGI DETECTORS WITH EXPLICIT ATTENTION ALIGNMENT
6.1 ALIGNING MODEL ATTENTION WITH PERCEPTUAL ARTIFACT REGIONS
As discussed in Sec. using PAD as an auxiliary task does not effectively encourage AIGI detectors to focus on perceptual artifacts for authenticity judgment, and the model attention may still be distracted by uninterpretable features. This motivates us to explore a more direct way that regularizes model attention to align with perceptual artifact regions.
To this end, we propose an attention alignment method that can be applied to ViT-based AIGI detectors. Specifically, we employ the Gradient Attention Rollout (Gildenblat) 2020). extension of Attention Rollout (Abnar & Zuidemal 2020) that computes the aggregated attention heatmap Acts E [0, 1] 7l for the classification logits with respect to all h w image patches. This heatmap reflects the model's attention distribution for predicting the image as fake, and is compared with the annotated artifact regions. To match the patch-level attention heatmap, we construct a corresponding
patch-level artifact heatmap Aart E [0, 1|1x" by setting each value (f.))
art as the proportion of pixels belonging any annotated artifact within the corresponding patch. For real images, we set Aart as a zero matrix. Since the computation of Agta is differentiable, we can optimize the mean squared error between the two heatmaps as an auxiliary loss during training. However, restricting the model attention to only artifact regions may hinder the model from learning other useful features that appear
in benign regions (i.e., where A (15)
0). Therefore, we introduce a benign region weight X E [0 art
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Table 4: Ablation study on attention alignment. Artifact- based alignment generally achieves superior AJ perfor- mance to baseline without alignment (3 0) and saliency-
Precision
Recall
based alignment across datasets.
20
Alignment
X-AIGD
Synthbuster
Chamelcon
CommFor
Mask
Acc
Fi
Acc
Fi
Fj
None
85.7
84.3
69.1
55.9
71.4
62.7
69.7
58.2
Saliency
86.6
86.4
71.2
60.7
68.4
58.3
69.2
59.7
Artifact
87.1
87.4
72.1
63.2
71.2
63.5
61.4
13
Fake Image
Ours
04
10
00
02
O8 62
(e) X-IGD
Synchbuster
5.5
0.3
Edges Shapes
Semantics
Chameleon
CommFoo
Figure 6: Comparison of interpretations of baseline and our artifact-alignet model. Our model shows broader attention to potential artifact regions.
Figure 7: Sensitivity analysis on benign region weight X. A moderate À (e.g.. 0.4 or 0.6) yields higher Fj-scores.
to control the penalty on non-artifact features, leading to the following attention alignment loss:
Lalign
1
(A -A0-)))
where W((.)
1,
if ( A
(1)
hew
if A =
(1J)
By adding this regularization term to the standard binary cross-entropy loss LBCE for authenticity judgment, the overall training loss becomes:
C= LBCE + BLaligns
where B > is the weight for the attention alignment loss.
6.2 EXPERIMENTS
Experiment setup. To investigate whether explicit attention alignment can enhance the generalization of AIGI detectors, we finetune DINOv2 (Oquab al.) (023) using the objective defined in (2) on the training set of X-AIGD, and evaluate the model on three AIGI detection datasets with diverse fake image sources in addition the X-AIGD test Synthbuster (Bammey 2023), Community
Forensics (CommFor) (Park & Owens) 2025), and Chameleon (aan et al. 2025). The hyperparameter selection is based on the accuracy on validation set constructed from unannotated fake images generated by the same generators as the training set and the corresponding real images in X-AIGD. Additional details are provided in Appendix B.3
Ablation study. We compare the artifact-based attention alignment method with a baseline that finetunes DINOv2 using only the binary cross-entropy loss (i.e., 3 0). As shown in Tab. 4 the proposed method consistently improves the overall performance across datasets, especially on Fi- scores, demonstrating its effectiveness enhancing model generalization. Furthermore, we conduct an additional ablation that replaces the annotated artifact masks with saliency masks generated by a pre-trained saliency detection model (Qin et al. 2020).. The results indicate that regularizing model attention to salient objects does not yield similar performance improvements, underscoring the effectiveness of leveraging perceptual artifacts for attention alignment. Additional ablation studies presented in Sec. D.4
Qualitative analysis. Fig. 6 visualizes the interpretation heatmaps of the baseline and our artifact- aligned model. Our model shows significantly broader and more intensive attention potential

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artifact regions, such the limbs of humans and animals, and complex structures textures. This indicates that the our method effectively guides the model to focus on more interpretable features.
Discussion on benign region weight X. A sensitivity analysis on À is illustrated in Fig, 7 A larger À discourages the model from utilizing detection clues beyond perceptual artifacts, such as low-level fingerprints or global characteristics. While perceptual artifacts can be more generalizable clues, the difficulty in detecting higher-level artifacts as suggested by Sec. | can lead to lower recall if the model overly relies on them. Therefore, balancing the contribution of perceptual artifacts and uninterpretable features is crucial for practical AIGI detection. In addition, the precision is consistently and significantly higher than the recall across different datasets, which relates to the common challenge identified by Ojha et al. (2023) that AIGI detectors often treat the real class as a sink class that hosts any sample not containing specific fake patterns. However, with moderate values, the gap between precision and recall can be shrunk, indicating more balanced learning of both real and fake classes, and thus better Fi-scores in generalization.
CONCLUSION
To facilitate the research on interpretable and generalizable Al-generated image detection, we intro- duce X-AIGD, a fine-grained benchmark featuring pixel-level, categorized annotations of perceptual artifacts. X-AIGD enables fine-grained analysis of model decisions, uncovering critical insights into existing detection mechanisms. Our comprehensive evaluation reveals that existing AIGI detectors of- ten overlook perceptual artifacts, relying instead on opaque, uninterpretable features. While multi-task learning shows potential for explicit artifact detection, it still struggles to align authenticity judgments with human-interpretaahe cues. Therefore, we propose an attention alignment method that regularizes model to focus artifact regions, which significantly enhances interpretability and cross- datsset generalization. These findings highlight the significance of artifact-aware detection strategies. By setting new standard for interpretability evaluation, X-AIGD aims to drive advancements in robust and explainable AIGI detection methods.
ETHICS STATEMENT
Research Involving Human Subjects or Participants To ensure the ethical conduct of this research, we conduct an internal ethics review to confirm compliance with relevant ethical standards. This study involves human participants recruited to perform data annotation tasks. Prior participating. individuals are thoroughly informed about the purpose and significance of the research, the expected time commitment, and the details of compensation. Annotators are provided with flexible working hours, enabling them to complete tasks at their own convenience. To protect participant privacy, no personally identifiable information is collected beyond what is necessary for payment purposes. The identities of annotators are anonymized in all reporting and analysis. All personal payment-related information is handled confidentially and stored securely.
Data-Related Concerns This study uses real-world image data from publicly available datasets, including the MSCOCO (Lin et al. 2014), the LAION-Aesther (LAION AI 2022). the Conceptual Captions Dataset (Sharma et al.] 2018), and the SA-1B (Kirillov al.) 2023). These datasets curated by academic or non-profit organizations and are widely used for machine learning research under the appropriate licenses. We carefully review the licenses associated with each dataset to ensure compliance with usage restrictions and attribution requirements. Due to the nature of these large-scale web-crawled datasets, obtaining explicit consent from all potentially depicted individuals is not feasible. To mitigate potential ethical risks, we rely on expert models and human inspection to filter out NSFW (Not Safe For Work) content from both real and synthetic images. All usage is strictly for non-commercial academic purposes, aligning with the open science missions of the respective dataset creators. All synthetic images used this study are generated from text prompts using open-source text-to-image models. The inputs are textual descriptions only, and the outputs are not derived from any specific real-world individuals or copyrighted works. Should any rights holder believe that their content has been used in violation of applicable copyright privacy laws, we welcome them to contact us so that we may review and address the matter accordingly.
Societal Impact and Potential Harmful Consequences This work introduces X-AIGD, benchmark for interpretable Al-generated image detection, aiming to improve the transparency and reliability of identifying synthetic images. Although our goal is to enhance detection capabilities, we recognize
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that such tools could be misused to reverse engineer and refine generation techniques, making AI- generated content even more realistic and harder to detect. This poses risks in terms of misinformation, deception, and societal manipulation. We emphasize the importance of responsible use and encourage researchers to consider the dual-use implications of this line of work.
Impact Mitigation Measures We provide comprehensive dataset documentation, adopt ethi- cally responsible open license, ensure secure and prrvacy-rrsserving data practices, and enable full reproducibility by sharing all necessary research artifacts. We also confirm compliance with relevant legal and human rights standards. Our goal is to advance trustworthy, interpretable, and socially responsible research AIGI detection.
AUTHOR CONTRIBUTIONS
Yao Xiao: Conceptualization, Methodology, Software, Validation, Writing Original Draft, Project Administration. Weiyan Chen: Conceptualization, Methodology, Software, Data Curation, Formal Analysis, Investigation, Visualization. Focus on the evaluation of existing models and the attention aligmment method. Jiahao Chen: Conceptualization, Methodology, Software, Data Curation, Formal Analysis, Investigation. Focus on the investigation of MLLM-based interpretable AIGI detection, case study text rendering artifacts, and annotation tool development. Zijie Cao: Conceptualization, Methodology, Software, Data Curation, Formal Analysis, Investigation, Visualization. Focus multi-task models, qualitative result visualization, and annotation process monitoring. Weijian Deng and Binbin Yang: Writing Review & Editing. Ziyi Dong: Conceptualization, Methodology. Xiangyang Ji and Wei Ke: Supervision. Pengxu Wei: Conceptualization, Writing Review & Editing, Supervision, Project Administration. Liang Lin: Supervision.
ACKNOWLEDGMENTS
This work is supported in part by National Natural Science Foundation of China (NSFC) un- der Grant No. 6236222,,, 62376209, 62325605, 62536010, Guangdong Provincial General Fund No. 2024A1515010208, Guangzhou Science and Technology Program Project No.2025A0415465. 2024A04J6365.
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Le Zhuo, Ruoyi Du, Han Xiao, Yangguang Li, Dongyang Liu, Rongje Huang, Wenze Liu, Xiangyang Zhu, Yun Wang, Zhanyu Ma, Xu Luo, Zehan Wang, Kaipeng Zhang, Lirui Zhao, Si Liu, Xiangyu Yue, Wanli Ouyang, Yu Qiao, Hongsheng Li, and Peng Gao. Lumina-next: Making lumina- -2x stronger and faster with next-dit. Advances Neural Information Processing Systems, 37:131278-131315, 2024.
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A ADDITIONAL DETAILS AND DISCUSSIONS OF X-AIGD BENCHMARK
A.1 SOURCES OF REAL AND FAKE IMAGES
Tab. summarize the mal--image sources used to derive caption prompts for fake-image generation. The four sources provide 4,000 real images in total for the benchmark. Tab. 6 giists the 13 generators used synthesize fake images, grouped by architecture. The checkmarks indicate which models contributed to the training and test splits used in our experiments.
Table 5: Overview of real image sources in X-AIGD.
Datasct
Sample Count
SA-IB Kirillov al 2023
2972
Laion-Aesthetic LAAON AT 2022
856
MSCOCO
Lin
2014
133
Conceptual Captions Dutusct Sharma
2018
Table 6: Overview of image generation models used for fake image synthesis in X-AIGD. These
models cover three architectures: diffusion UNets (Rombach et al. 2022). Diffusion Transformers (DiT) (Peebles & Xie 2022). and auto-regressive models. indicates community fine-tuned model on Civitai (Civ).
Generator
Architecture
Training Set
Test set
SD v1.4 Rombach et al.
2022
Diffusion UNet
SD vI 1..----itic--iiiiii (SG 16122
227
2024
Diffusion UNet
SDXL (Podell et al
M22
Diffusion UNet
SDXL-realism--nngin
r3777
2024
Diffusion UNet
PixArt- (Chen et a
20244
DIT
Lumina-Next Ziuo et al. 2024
DIT
SD [Eser et ml. 2024
DiT
HYDIT-v1.2 (La (La al.
21124b
DIT
FLUX.I-dev [Lbs
2024
DiT
FLUX.I-des MUXX.--de-iPhoeePlhoo (Ambaaal
2024a
DIT
SD 3.5-L [Esser al 2024
3D Photo
2024b
DIT
Infinity (Han et
rege-segee
A.2 IMAGE COLLECTION PROCESSES
provide image and its corresponding content description text. Based on image and the provided description, please determine the image was captured real scene (not generated by Al). If this photo taken camera, respond with 'Yes'; otherwise, response with 'No'. Image content description text:
(a) Prompt template used for filtering unrealistic images from CC3M and diion-Asshettcc
provide an image and its corresponding content description text. Based on both the image and the provided description, please infer if the image has been blurred for privacy reasons (such as blurring of faces license plates). the image has been blurred, respond with 'Yes'. image not been blurred, respond with 'No'. Image content description text:
(b) Prompt template used for filtering blurred images in SA-1B.
Figure 8: Prompt templates used for real image filtering with Intern VL2 8B (Chen et al. 2024d)
Real image filtering. To ensure diverse and realistic of real-world images, collect data from four widely-used datasets: COCO (Lin et al., 2014), CC3M (Sharma et al. 2018). Laion- Aesthetic (Schuhmann et al. 2022), and SA-1B (Kirillov et al., 2023). These datasets collectively
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offer broad variety of scenes, subjects, and visual complexities. To maintain a focus on authentic photographic content, we apply a filtering process to exclude non-photographic images. Specifically, we employ Intern VL2 8B (Chen et al., 2024d) to identify and remove unrealistic images (e.g.. cartoons and paintings) from CC3M and Laion-Aesthetic. Additionally, we exclude images from SA-1B that are blurred for privacy reasons. The detailed prompt templates are shown in Fig.
Text prompt collection. We utilize captions that describe the visual content of real images as the text prompts for fake image generation, and these prompts are expected span wide range of complexity levels. To achieve this, we utilize BLIP-2 (Li et al., 2023) and Intern 8B (Chen) al., 2024d) to generate captions for real images. Notably, we employ different prompts to guide these models in producing captions of varying lengths, thereby controlling their descriptive richness and detail. In addition to model-generated captions, we further enrich our dataset by incorporating captions from COCO (Lin et al. 2014) and those used in (Chen et al. (024c) for SA-1B. These sources, respectively, provide abundant examples of low- and high-complexity descriptions. By combining these sources and balaneing the distribution of caption lengths, we construct diverse and evenly distributed set of 4,000 captions, which serve as the text prompts for generating fake images.
Prompt engineering for high-quality image generation. To enhance image quality, we follow the prompt engineering approach in (Baraldi et al.) 2024). Specifically, for the generation of each image, randomly sample set of positive modifiers and attach them to the text prompt. These modifiers include: best pallity masterpiect ultra highres, ultra realistic, HDR, photorealistic, hyper detailed, dynamic righting, and professional 1 righting. Each modifier is sampled independently with a proba- bility of 0.5. In addition, we apply a fixed negative prompt all generators: "abstract, game, 3D style, fantastical, splash art, simple background, blurry background, blurry, Interchangeable Lens, Digital Photo Frame, rough, nsfw, lowres, bad anatomy, bad hands, text, error, missing fingers, extra digit, fewer digits, cropped, worst quality, low quality, normal quality, jpeg artifacts, s signature, watermark, username, blurry". These positive and negative modifiers apply to all the pre-trained generators. For the community fine-tuned models drazzz, 2024 Anibaaal 2024a b), which require specific modifiers to ensure the quality and style of generation, we follow the respective official recommendations.
Safety checking. For responsible data release, we adopt model- based image safety checking for the collected images. Specifically, we an open-source NSFW image classifier (Falcons.at ail 2023) to filter out the unsafe images, and perform manual inspection on the positive results to eliminate the false alarms. Furthermore, during the annotation process, annotators are instructed report any potentially harmful content in the generated images. A total of 12 generated images that do not pass the safety checks are excluded from the dataset.
A.3 ANNOTATION OF PERCEPTUAL ARTIFACTS
Image selection and train-test splitting for the annotated subset. Given the labor-intensive nature of pixel-level annotation, we select a representative subset of the collected fake images to annotate. After the image collection process, we obtain 4,000 real images and 4,000 corresponding fake images from each of the 13 generators. We assign a unique UID for each real image and its corresponding fake images generated from the same prompt, resulting in 4,000 UIDs in total. We split half of the UIDs into the training set and the other half into the test set, ensuring no overlap between the two sets. Then, for both the training and test sets, we select 200 UIDs for each involved generator for annotation. This selection ensures that all generators have 100 overlapping UIDs in the training and test sets, while the remaining 100 UIDs are unique to each generator. This design allows for a comprehensive evaluation of cross-generator generalization and the impact of semantic diversity.
Annotation workflow. To reduce the impact of the unavoidable inter-annotator variation due to the subjective nature of artifact perception, our protocol is designed to prioritize completeness rather than force strict consensus among annotators. Instead of three fully independent passes, we employ a relay-style workflow: each annotator inspects the previous annotations, identifies missed artifacts, and adds any missing artifact regions when needed. including cases where their interpretation differs from earlier annotators. Each image undergoes three rounds of annotation by three different annotators.
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Table Number of annotated artifact instances across all artifact types and generators.
Low-level
High High-leyel
Cognitive-leve
Generator
Total
Textures
Eniees&Shupes
Symbols
Semantics
Commonsense
Physics
I-de Labs 2024
27
5:00
143
16
182
976
FLUX.I-Jev- Wew.---e--PPhePPhoee Amabaall 2024a
599
201
105
1065
HYDIT-v1
Li
21244
618
38
271
1057
Infinity
Han c A021
50
747
82
25
272
49
24
1249
Lumina
PixAAt-O
Chen ni.
161
1200
131
30
358
27
40
1947
SDXXL Podell al 11266
138
109
165
849
SDXL-rcallsti-aggune
2024
335
200
101
817
SD v1.4
Romback 2l
103
1189
94
286
1749
SD v 11.5-reallshhc-vssaa
2024
285
1077
299
366
2128
S13 Esser al 2024
337
1220
327
28
463
79
2408
2024
1041
SD 3.5-L-iPhonncPPicc Ammbaall
2024b
158
478
114
16
114
39
11
930
Total
1737
10310
1998
301
3146
486
307
18285
We intentionally retain all annotations rather than adjudicating them into single canonical mask. A subsequent independent confidence-sor across all annotations. Specifically, three independent annotators assign a confidence score of (no artifact), 0.5 (ambiguous), or (clear artifact) for each annotated artifact instance.
step is employed to measure the annotator certainty
Annotator instructions. To ensure high-quality annotations, we enlist 12 annotators who possess a higher education background and foundational knowledge of AI-generated images. Before commenc- ing annotation, all annotators undergo standardized training. which covers definitions of perceptual artifact categories and proficiency in annotation tool (i.e., Labelme (Contributors) operations. During the annotation process, annotators are required to meticulously inspect each generative image for perceptual artifacts, create precise polygon masks around the identified artifact regions, and categorize these artifacts into their correct classes according to standardized guidelines. Concurrently, annota- tors are instructed to flag out-of-scope images with unrealistic styles and unsafe images containing potentially harmful content.
Annotation statisties. Tab. presents the statistics for annotated artifact instances within X-AIGD, encompassing both the training and test sets. A total of 18,285 artifact instances are annotated, spanning 13 generators and 7 distinct artifact categories (encompassing low-level, high-level, and cognitive-level artifacts).
A.4 COMPARISON WITH RELATED ANNOTATED AIGI DATASETS
Tab. S provides a comprehensive comparison between X-AIGD and existing datasets with artifact annotations proposed for interpretable AIGI detection or perceptual artifact localization.
A.5 EVALUATION METRICS FOR PERCEPTUAL ARTIFACT DETECTION
While this paper reports pixel-level PAD metrics (i.e., IOU, PixP, PixR, and PixFi) following prior works (Zhag et 2023 Kang et al. 2025), argue that strict spatial precision necessarily required for interpretable AIGI detection, where approximate or partial localization is often sufficient for real-world applications.
To this end, we propose alternative instance-level matching metrics that tolerate partial localization. Specifically, a predicted region A with predicted category é is counted as a valid indication (i.e., true positive) of a ground-truth artifact instance with region rý and category Cj if è Cj and Area(f; n )/r AArra(f:) 2 t, where is a constant threshold. This parallels instance matching in object detection while accommodating the inherent fuzziness of artifact boundaries. For each artifact category, we compute the instance-level precision, recall, and Fi-score with threshold t, denoted by P@t. R @t, and F, @t, respectively. These metrics compatible with various forms of localized predictions, including pixel masks, bounding boxes, and points, thereby unifying the evaluation of models with different grounding abilities.
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Table 8: Comprehensive comparison with related annotated datasets.
Fake Images
Artifact Annotation
Dataset
Real Images
#lmage
#Generator
Localization
#Category
#Instance
Annotator
FakeClue
Paired
~5,000
Unknown
N/A
N/A
N/A
MLLM
Wen et al.
2025
FakeBench
MLLM &
Unpaired
3,000
N/A
N/A
Li ml.
2024a
Human
MMFRR Dataset
Paired
60,266
Unknown
N/A
MILM
Gao ct al. 2025
FakeChain hain
MLLM &
Unpaired
23,797
17
N/A
16
N/A
ei al. 2025
Human
PALAVST
N/A
10,168
Pixel mask
N/A
40,841
Human
Zhang et al. 2023
SynArtifact
N/A
1,310
8
Bounding box
13
1.593
Human
Cao et
2024
RichFII
N/A
11,140
Unknown
Point
N/A
82,430
Human
Liang al 2024
LOK] (1mage))
Unpaired
229
10
Bounding box
N/A
687
Human
Ye ct ml. 2025
SymthScars
N/A
12,236
Unknown
Pixel mask
26,566
Human
Kang al. 2025
X-AIGD
3,337 (labeled)
Paired
13
Pixel mask
18,285
Human
(ours)
48,651 (unlabeled)
A.6
MITIGATING THE IMPACT OF LABEL NOISE
Due to the inter-annotator variability of artifact annotations, training or assessing models with the annotated data without consideration of label noise can lead to model overfitting or less reliable evaluation results. To reduce the adverse effect of low-confidence annotations and fuzzy artifact boundaries on downstream use of X-AIGD, we recommend several complementary strategies: (1) exploiting the confidence scores for annotations: filter or re-weight the annotations according to their reliability; (2) training interpretable models with noise-tolerant localization loss; (3) evaluating model explanations via the instance-level geakklocalization metrics proposed in Sec. A.5
B ADDITIONAL EXPERIMENTAL DETAILS
B. ACCESSING INTERPRETATIONS OF AIGI DETECTORS
Code and checkpoints for existing AIGI detectors. We adopt the implementations and checkpoints for CNNSpot (Wang et al. 2020b), Gram-Net (Liu et al. 2020), and UnivFD (Ojha et al. 2023 from the MIGCDeteclBenchman (Zhong al. 2024). For more recently proposed methods, inc uudin FatFormer (Liu et al. 2024b), DRCT-ConvB (Chen et al. 2024a). DRCT-CLIP (Chen et al. 2024a), and CoDE Baraldi et al. 2024), utilize the code and pre-trained models from their official repositories.
Grad-CAM interpretation. To evaluate the interpretability of CNN-like AIGI detectors, including Swin Transformers (Liu et al. [201], we apply Grad-CAM (Selvaraju et al. (017). as implemented in (Gildenblat& & contributors 2021 generate heatmaps that highlight regions associated with the prediction of the "fake" class, In particular, we enable test-time augmentation smoothing (by setting obtain more stable and refined heatmaps; other parameters remain their
aug_smoot wwwottthTuueee default settings. Furthermore, image pre- prepprccssiigg strategies dictate how full-image heatmaps are obtained. For resizing- based models (Wang al., 2020bb Liu al., 2020 Ojha al., 2023 Liu et al. 2024b), heatmaps are directly mapped back to the entire image. In contrast, for patch- patch-based based models (Chen et al. 2024a| Baraldi et al. (2024), heatmaps are generated via a diding-patch approach inspired by (Chai al. (020). Specifically, the model is applied to overlapping image patches (with window size and stride equal to 224), and the resulting per-patch heatmaps are then aggregated to form full-image heatmap.
Relevance Map interpretation. Grad-CAM is not well-suited for Transformer models, as its methodology relies the spatial locality inherent in CNNs, which is fundamentally violated by the global self-attention mechanism in Transformers Therefore, we employ the Relevance Map method (Chefer al. 2021), which is specifically designed for the Transformer architecture, to
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visualize the attention regions of Transformer-basee AIGI detectors (i.e., FatFormer (Liu al. 2024b). DRCT-CLIP (Chen et al. 2024a), CoDE (Baraldi et al. 2024), and the models studied in Sec. 6 All parameters are consistent with the official settings.
B.2 MULTI-TASK MODEL TRAINING
Multi-task model construction. To explore the potential of learning PAD as an auxiliary task for interpretable AIGI detection, we construct multi-task model that utilizes Swin Transformer (Liu et al., (021) as the backbone. The same model architecture is applied to all variants studied in Sec. 5 including the single-task, transfer learning, and multi-task models. Specifically, we adopt the official Swin-B model pre-trained on ADE20K (Zhou et al. 2019). The features extracted by Swin-B are processed through two distinct branches: a binary classification head that consists of global average pooling layer followed by linear classifier with sigmoid activation (with the decision threshold fixed at 0.5), and a semantic segmentation head that employs UPerNet (Wang et al.) (020a) as the main head and is supplemented by auxiliary FCN (Long et al., (015) head during training. Both segmentation heads contain a hidden dimension of 512. During inference, the multi-task model simultancously outputs the probability that the input image is fake and a pixel-level mask indicating various types of perceptual artifacts.
Image augmentations. To enhance the robustness of the multi-task model, we implement com- prehensive image augmentation strategies during training. Given that fake images are typically in lossless PNG format while real images are predominantly JPEG-compressed, we apply random JPEG compression (sampling JPEG quality between 30 and 100) exclusively to fake images. This approach mitigates compression bias and better simulates real-world deployment conditions. For all images, subsequently perform scale-preserving random resizing (with resizing factor ranging from 0.5 to 2.0), random cropping (with a maximum cutout ratio of 0.75), and random flipping (with a 0.5 probability).
Training details. During training, the binary classification head for Authenticity Judgment (AJ) employs Binary Cross- Cross-Entropy loss. For the semantic segmentation heads addressing Perceptual Artifact Detection (PAD). we formulate this as a multi-class segmentation task, which considers one background class and the 7 classes of perceptual artifacts. For this subtask, use weighted Cross- Entropy loss where the class weights are approximately inversely proportional to their pixel-level frequencies in the training set. When combining the multi-task losses, the weights for the binary classification head, the main segmentation head, and the auxiliary segmentation head are set to 1.0, 1.0, and 0.2, respectively. To ensure class balance in AJ, we augment the labeled training sets with additional real images taken from the unlabeled training set (i.e., those with UIDs that are split into the training set but not selected for annotation). We train the model with a batch size of 16 for 80,000 iterations. We utilize the AdamW optimizer (Loshchilov & Hutter 2019) with a base learning rate of 2e-5 for the backbone and 2e-4 for the task heads. A small validation set split from the training set for selecting the best checkpoint. Our code is built upon MMSegmentation (Contributors) 2020).
B.3 ATTENTION ALIGNMENT
Training details. The training setup for the models in Sec. genneally follows that of the multi-task model in Sec. B.2 except that we use DINOv2 (Oquab al. 2023 (ViT-B) as the pre-trained backbone and adopt a lighter fine-tuning recipe with less severe JPEG compression (60-100 quality factor) and no random resizing. The models are trained with a batch size of 8 for 20 epochs. We report the average results of 10 independent repeated experiments with different seeds to ensure the reliability of the results.
Hyperparameter selection. To determine the optimal value for the benign region weight X, we create a validation set by randomly sampling 1,000 UIDs from the unannotated training set and ensuring that they have no overlap with the UIDs of images used for model training. The validation set shares the same generators with the training set, and therefore has 1,000 real images and 5,000 fake images. We train the models with À {0.0. 0.2, 0.4, 0.6, 0.8, 1.0), and select the best À that yields the highest balanced accuracy on the validation set for each independent experiment. The weight of the attention alignment loss is fixed as 0.1 without heavy tuning.
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Table 9: Evaluation of MLLMS' ability to determine the presence of different types of artifacts
in fake image. Image-level Fj -cores are reported for each artifact category.
Model
Textures
Edges Shapes
Symbols
Color
Semantics
Commonsense
Physies
Qwen2.5-VV 5-VL 7B (Bai et 2025
5.0
5.3
49.0
14.6
26.7
11.7
14.3
InternV1.3 8B Zhu et 2025
16.2
38.6
47.2
13.2
39.6
18.7
13.4
LLVa--Next (Mistral (Lu et al.
2024a
32.9
69.1
28.7
9.0
52.6
17.1
12.2
DeepSeck- VL22-VL2---- (W al
2024
32.3
71.1
33.2
7.8
45.2
18.5
17.1
GPT-4o (OpenAl 2024
9.3
59.5
91.7
0.0
40.5
32.3
9.1
Random
29.9
56.7
30.6
9.4
49.1
19.2
12.0
Table 10: Authenticity judgment accuracy (%) of FLK and FakeReasoning on X-AIGD.
Model
Real Acc.
Fake
Avg. Acc.
FakeVI.M (Wen al. 2025
71.3
FakeReusoning Gao 2025
81.2
39.7
60.4
C DISCUSSIONS ON MLLM-BASED INTERPRETABLE AIGI DETECTION
A growing line of work (Gao et al. 2025
Li et al. 2025 Wen et al. 2025 Kang et al. 2025) attempts
to build interpretable AIGI detection models based on MLLMs, leveraging their strong capacity of visual understanding and reasoning and the textual outputs for explanations. Some studies (Kang et 2025 Yang et al. 2025) integrate additional modules into MLLMs to enable grounded interpretations of image defects. In this section, we study the performance of existing MLLMs based on X-AIGD, and reveal the challenges of building MLLM-bbased interpretable AIGI detectors.
C.I EVALUATING TRAIIED MLLMS ON PERCEPTUAL ARTIFACT DETECTION
To investigate whether pre-trained general-purpose MILMS have sufficient ability to detect perceptual artifacts, we separately prompt an MILM to identify each artifact category in a test fake image and return bounding boxes for detected instances, any. We observe that most predicted boxes are highly imprecise, despite the models' general grounding capability other high-level tasks. To obtain meaningful measure, we evaluate only image-level binary predictions: any non-empty bounding box list counts predicting the presence of that artifact type. The image-level Fj Fi-scores across the seven artifact categories are shown in Tab. Across most categories, the MLLMs' predictions do not exceed random guessing, with the notable exception of the Symbols category. This suggests that current MI.LMs may lack the fine-gra fineggraieed perceptual sensitivity required for grounded artifact reasoning, including the proprictary model GPT-4o.
C.2 EVALUATING MILM -BASED INTERPRETABLE AIGI DETECTORS
We conduct a focused evaluation of three open-source representatives of this line of work, namely FakeVLM (Wen al. 2025), FakeReasoning (Gao et al. 2025), and LEGION (Kang al. 2025).
using the X-AIGD benchmark.
Authenticity judgment performance. The textual output of Fake and FakeReasoning comprises a binary authenticity prediction and accompanying explanations. When evaluated on X-AIGD, both models exhibit significant class bias, resulting in relatively low average accuracy, as shown in Tab. TO In addition, the LEGION model trained for artifact localization and explanation produces non-zero artifact mask and at least one artifact description for every input image, including real ones. This behavior can be explained by the fact that this model is trained exclusively fake images and never observes real image samples.
Qualitative assessment of explanations. As exemplified by Figs. 9 and 10 our qualitative analysis of the explanations produced by the three models highlights several recurring issues:
FakeVLM and FakeReasoning: Often present vague ungrounded judgments such as "inconsistent light source", "lacks realistic detail", or "(objects) do not match reality". While some common cues (c.g., "overly smooth", "saturation is too high") are correct in some cases, they frequently appear as false positives. These patterns likely stem from the MLLMs used for generating the annotations for their training data.
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FakeVLM:
take
Explanation: This is fake imagee Although it does not show ainicaus antifacts or dire signs of compostion, there underlying characteristic incorsistencies in its features that suggest it artificially created.
Fasennss real
Explanation: <SUMMARY- well determine the image rest thinn by onalyzing its content and identiyynng forgery uttibuttes from both low levol and heghnllste perspactioes. will then conclatle by stwiing wither 'tii image is real" or "this image is take based on my analysis, <IUUMMARE RY-
CCAPTION the
berind a coffee amiling
holeinng of coffee. She cary, ruslic switing chaltboond machines options, background home kitchon ensronment. </OAPTION>
whiil bicuse
be
sugaesting
<RFASCNIMG- from low-lavel perapective: tectunes the person's
clothing, the wooder background and the soffee equi pmont appear natural
and detailed The edges of objects, such as the penson and the cottee machine, The Lighting on the person and the shadows cast by objects are consistent
Authentichy:
with netural sourow
clewr and sheen, with good monlution color bolance. From high high-lovel perspective: The realatic
deplcts
coffee
image NR -CONCLUSION This Image real. 4CCNCLUSION-
FakeVLM: take
Explanation: This is fokeiimage The image achibitt underlying
characte inconststencles features suggest artifiiaally
Reasanin take
Explanation: -BUMMARY+> will determine the image real or fale by anolyzing content and identitying forgery atributes from both low leval and high level perspectives. will than conclude stating either "this image real -rUUMMARY
thi take based
eCAPTION= The
lakesido with vibrent red posiiioneed
colm, clear watorreficcts surrounding greenery mountains In backpound. sbove pantly cloudy, actiing the trr curquil atmosphare of setting. <CCAPTION
<RFFASININGD From low-level perspactive: The lighting on the bost does not mosch the lighting on the suroundiin enviromennt suggesting inco nconsistoncy. The texture of the water and the bost appears overly smooth and lacks flo floating
Authonticity: take
realistic detail. From high-taval perapecthve: The beat which unusual.
the
visible REASSONNNG
Theratore, image
This image take, </DONCLUSION>
FakeVLM: fake
Explanation: This is take image. The box has an unreatiatic shape and
structure, with nn overly amooth and uniform surface that lacks natural
imperfections The edges and cornera are too facz and lack the slight
ineguul iis tysically found resl-world objects, The lighting and shadows
iPad Pro (11 (11-inch) Smart Folio
inconsistent, unnature that does align with expected lacks the subtll onject.
Light Imporfactions
be present
FakeRoessoing:: roal
Explanation: cSUMMAANY will determine if the image real fwbn
analycing content and idur lifinng fongery iuuiuutee from both low-llonel and high high-lessal perspectives. will then conclude stating wither "hhi imagle is real" +/BUMAARY>
'this image take'
The image
PPP Pro
letters against tackgrounc, Indicatinng is product packaging for an inad cass.
appears to bu new unopenedd with clean, minimalet design <XCAPIIN>>
retl
<REASONING- From low level perspective: The texture the box and the surtace is aspeors natural and cons with resl world materials The odges of the box and the seot smooth and well-defined, typical of real. photnggephh The lighting and shadown on inclesting
consietntt with natura quality
source.The
slear
phoottgraph. From high-lavel perspective: logical, indicading produc Therefore, image real.
.CONCLUSION- This image is real. KICONCLUSION-
Observations:
1. FakeVLM appears to show tendency towards the "fake" class in these challenging cases. In these observed instances, Justifications resort generic templates rather specific visual evidence. For instance, may use broad descriptions like "inconsistencies in features' without explicitly grounding them in the image content.
2. contrast, Fake Reasoning is overly conservative, favoring" "real" predictions. The provided explanations might discuss valid image features (like shadows) but miss the actual artifacts, making reasoning less specific the defects.
Figure 9: Qualitative examples of the judgments and explanations of FL and FakeRea- soning. Images are selected from X-AIGD.
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LEGION
Explanation: Upon esarmnning the image. I have found: woman white cost and hat stanns confidently, with her left tell tilted pupil god, her right upper leit and the pupil entargedd, her left ear's outer rim, eur bone, and earlobe fused, her right ear's outer rim, ear earbone, and earlobe fused, her left hand's fingers following eye
tilned
and abnormally shaped.
Lady's eye :The pupit is enlarged, and the appearance unn unnatural. Lady's right
laft,
pupil anlarged, of the right is tilted upper left, appeerance
Lady's left ear :hh outer ear rim, ear bone and earlobe of the left ear are fused together. Lady's
bone and carlobe of
right
together.
Lady's ' left hand The fingers the hand are twiised and abnormally shaped, which is obviousily different from normal fingors.
Authenticity: take
LEGION
have founct variety delectable treats, including cookess, fruit, bread, vibant bauquet of flowers in the background.
Explanation: Upon examining
arrangeed
Flowers have
following artifacts. right
vase
flowers in the vase on the right side the table are not the same as the flawers in the vase on the left side of the table.
Authenticity: fake
LEGION
Explanation: Upon examining the have found: signpost colorfu the signa bellow outdoor setting,, a appearing distorted and illeible. To elabomae, have found the following artiflccts. bello Illegibe..
DASCAJALES 12
ARE
bellow
ACIOIMES
8-40
LUCA All
Authenticity: real
Observations:
In the demonstrated examples, LEGION appears exhibit signs of hallucination. generates masks explanations these visual masks do always align with the accompanying text. Finally, the model exhibit hyper-sensitivity towards regions: even legible text sometimes misclassifled "distorted" "indecipherable".
images obvious defects exist. Furthermore,
Figure 10: Qualitative examples of the judgments and explanations of LEGION. Images are selected from X-AIGD. The predicted artifact masks are marked on the images with masks.
LEGION: While its binary masks can localize some artifact regions, the generated textual explanations may be inconsistent with the predicted masks. We observe hallucinated object parts (c.g., nonexistent hands) or descriptions of abnormalities that do not correspond to any detected region, suggesting potential overfitting to textual patterns rather than grounded visual evidence.
24

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Published as a conference paper at ICLR 2026
Quantitative evaluation of artifact localization for LEGION. We evaluate the artifact masks produced by LEGION on the Category-Agnooe PAD task, and the results are shown in Tab. [T] Compared with the interpretations of the DINOv2 models trained in Sec. LEGION outperforms vanilla fine-tuned baseline all metrics but remains inferior to our attention-aligameet model trained on annotated data.
Table 11: Quantitative comparison of LEGION's artifact masks and our model interpretations on Category-Agnostic PAD.
Model
IoU
PixP
FixR
PixFi
24.4
1.0
LEGION Kang al. 2025
29.1
11.7
16.7
Ours (Attention Alignment)
14.0
37.2
18.2
24.5
Summary. These findings highlight the importance of X-AIGD. Existing MLLM- -aaded methods may generate explanations that are ungrounded, inconsistent, biased, and they lack access to paired real and generated images with fine-grained human annotations that are essential for reliable interpretability evaluation. X-AIGD addresses these limitations by offering localized and trustworthy supervision, which enables principled analysis of whether models truly attend to perceptual artifacts rather than spurious cues.
C.3 IMPROVING MILMS ON PERCEPTUAL ARTIFACT DETECTION
To investigate whether our annotated data can be exploited improve MILMS ability to detect perceptual artifact, we perform a preliminary experiment on Qwen2.5-VL 7B (Bai et al. (025) using GRPO-based reinforcement fine-tuning (Shao et al. 2024 Liu et al., 2025). Specifically, in each rollout, the model is prompted to predict the authenticity of the input image and provides list of detected artifact instances that are localized by bounding boxes and categorized following the taxonomy of X-AIGD. We adopt the classification reward, loU-based detection reward, and format reward following Visual-RFT (Liu et al. 2025).
To assess the PAD performance of the models, adopt the instance-level metric discussed in Sec. A.5 The Fi @0.5 scores for the pre-trained Qwen2.5-VL. 7B and our fine-tuned model are presented in Tab. First, the pre-trained model shows weak initial competence most artifact types except for the Symbols catagory, aligning with our findings in Sec. C.I As a result, the effectiveness of reinforcement fine-tuning limited: yields notable gains for the Symbols category, while the improvements for other artifact types are at most marginal. Overall, these observations indicate that X-AIGD can improve the perceptual artifact detection ability of MLLMs on certain artifact categories, although broader gains likely require more effective training strategies and complementary supervision such as pseudo-labeles synthetic data as discussed in Sec.
D MORE VISUALIZATIONS AND EXPERIMENTAL RESULTS
D.1 VISUALIZATION OF ANNOTATED ARTIFACTS
Fig. we visualize the annotated artifacts for each category. We provide more examples Fig. || to further demonstrate the artifact categories and our pixel-level annotations.
D.2 COMPARISON OF MODEL INTERPRETATIONS
In Fig. compare the interpretation heatmaps of different models and the segmentation results of the multi-task model against the ground truth artifact regions. We provide more visualization results in Fig. 12
D.3 QUANTITATIVE EVALUATION ON MODEL INTERPRETATIONS.
To quantitatively study the alignment between model interpretations and regions for the models in Sec. [5) and Sec. 6 and validate the effectiveness of our attention alignment method, compare the Sategory-Agnostie PAD performance of these models in Sec. As shown
artifact
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Table 12: PAD performance of Qwen2.5-VL 7B before and after GRPO fine-tuning. Instance-
level F1 Pc-scorss at threshold i = 0.5 (as defined in Sec.A.5) A.5) are reported for each artifact category.
Model
Textures
Edges & Shapes
Symbols
Color
Semanties
Commonsense
Physics
Qwen2.5-VL 7B (Bai al. 2025
1.3
0.3
18.8
3.0
1.3
0.0
0.0
Fine-tuned
12
31.5
1.9
2.5
2.5
Table 13: Quantitative evaluation on model interpretations on Category-Agnostic PAD. The Swin-based models are studied in Sec. while the DINOv2- models are trained in Sec.
Category PixP
Agnostic PAD
Backbone
Method
loU
PixR
PixF1
Al-only
8.8
11.8
25.9
16.2
Swin
Transfer learning (linear probing)
6.3
10.1
14.2
11.8
Transfer learning (full fine tuning)
6.9
15.2
11.3
13.0
Multi-task learning
4.8
11.0
7.8
9.2
Baseline
24.4
0.5
1.0
DINOv2
Attention Aligument
14.0
37.2
18.2
24.5
in Tab. 13, the interpretations of our artifact- -hased attention alignment model achieve significantly better alignment with the annotated artifacts, while the transfer learning and multi-task models studied
in Sec. 5 achieve no improvement compared with the single-task model. These observations are in line with our qualitative results in Sec. and Sec. 6
D.4 ADDITIONAL. ABLATIONS AND LORA FINE-TUNING FOR ATTENTION ALIGNMENT
The experiments in Sec. 6 are conducted based on full parameter fine-tuning on DINOv2 with small learning rate of le-5. Our observations on the training process suggest that all variants in the ablation study tend to rapidly collapse to similar optima, limiting the observable differences between training configurations. In addition, the Gradient Attention Rollout (Gildenblat) (2020) used for aggregating the model attention requires backward passes to obtain attention gradients, complicating the computation graph and preventing gradient accumulation. This increases training noise and
reduces the stability of the comparisons.
To further validate the effectiveness of attention aligmment, we experiment with a more stable and scalable training recipe, applying LoRA to fine-tune the DINOv2 backbone with rank=8 and learning rate of le-4 with cosine annealing schedule (Loshchilov & Hutter 2017). In addition, we use the standard Attention Rollout (Abnar& & Zuidemal (020) for attention aggregation this setup, which does not require computing attention gradients and allow us to achieve effective batch size of 32.
With this training recipe, we conduct additional ablation of the proposed artifact-based attention alignment with two types of masks: (i) Random mask: randomly sampled convex region within the image; (ii) Benign mask: the largest SAM-segmented region that contains no overlap with any annotated artifact mask (to avoid noisy small fragments). We train the models using these masks under the same attention alignment objective and compare them with the proposed artifact-based alignment. The benign-region weight À and the alignment-loss weight 3 are set to 0.4 and 1.0 for all variants without further tuning. The results Tab. [4 suggest that both Benign and Random masks yield results comparable or worse than the no-alignment baseline, whereas artifact- -based alignment consistently provides the largest improvements across datasets. These findings indicate that the performance gains indeed stem from aligning attention with perceptual artifact regions rather than generic spatial regularization effects.
Table 14: Additional ablation study on attention alignment. Artifact- based alignment outperforms the alignment with other types of masks (Benign, Random, and Saliency) across datasets.
Alignment
X-AIGD
Synthbuster
Chameleon
CommFor
Mask
Acc
Fi
Acc
Fi
Ace
Fj
Acc
Fj
None (8 0)
82.2
82.9
71.2
60.6
68.0
68.2
72.1
63.6
Benign
80.7
81.4
70.6
60.3
65.6
66.4
71.5
63.1
Random
82.3
69.7
57.6
66.6
67.3
72.3
64.1
Saliency
81.3
81.9
71.4
61.8
66.0
66.5
72.4
64.5
Artifact
83.1
84,0
71.8
62.3
68.7
68.8
73.7
66,5
26

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Published as a conference paper at ICLR 2026
NATURAL BRIDGE
Physics
Commonsense
China Consruction
Semantics
suge VS
Mominlaner
Color
188
Sellage Skrther
Textures
Edges Shapes
Freia LIOPOACINT CORMOSTTOCETOOO CRO
Symbols
L Neweee e
Figure 11: Visualization of annotated perceptual artifacts.
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Published as a conference paper at ICLR 2026
Image
DRCT-CLIP
(Classfication)
(Segmentation)
Ground
PASSPORIS
PASIPO
PUSPPORT
MSSPORD
RASTIOO
FILZ
Low-level:
Symbols
Color
Esges&Shapes
High-level:
Semantics
Cognitive-level:
Commonsense
Physics
Figure 12: Results of the interpretations of different models.
E
LIMITATION AND FUTURE DIRECTION
Our benchmark provides pixel-level, categorized annotations of perceptual artifacts, enabling fine- grained analysis of Al-generated image detection. While our evaluation primarily focuses on Authenticity Judgment and Perceptual Artifact Detection. future directions include expanding to multi-dimeesional analyses, such as human aligmment, image realism, and diversity metrics, to further enhance interpretability and robustness in detection systems. To analyze the potentials and challenges of artifact-aware interpretable AIGI detection, we provide several simple baselines such as multi-task learning and attention alignment, shedding light on its inherent complexity. Future work could explore advanced methodology that better utilizes perceptual artifact for enhanced interpretability and performance. Additionally, while the empirical studies in this paper focus on traditional classification and segmentation models, future research could explore grounded evaluation of Multimodal Large Language Models (MLLMs) and their capabilities in boosting artifact-award AIGI detection, which requires sophisticated techniques and represents intriguing direction.
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Published as a conference paper at ICLR 2026
Moreover, training MLLMs for this purpose may require scaling up the annotated data, but it is intractable to rely exclusively on human annotation due to the high cost. Promising directions for cost-effective annotated data construction include (I) semi-automatic expansion based models trained on X-AIGD and fine-tuned with the human-in-the-lopp paradigm, and (2) using controlled image editing to produce pseudo-labeled fake images where the automatically generated editing masks serve as a coarse localization of potential artifact regions.
F BROADER IMPACT
With the continuous advancement and widespread diffusion of AIGI, it has become increasingly challenging for humans to distinguish synthetic images from real images. This poses a significant risk, as malicious actors can exploit synthetic images for creating misinformation, defamation, and fraud, thereby eroding public trust in digital media. To counter these threats, researchers have developed AIGI detectors. However, a critical limitation of current detectors is their lack of explainability; they typically provide only a binary classífication (fake or real) without offering insight into why particular image is flagged.
Addressing this gap, our work introduces X-AIGD, a novel benchmark specifically designed to promote the development and evaluation of explainable synthetic image detection methods. X-AIGD incorporates detailed annotations of peraa--erceptible artifact regions within synthetic images. Our analysis, utilizing this benchmark data, reveals significant discrepancy: current AIGI detectors often focus on image regions that do not align with artifacts easily perceivable by humans, making their decision-making process opaque and untrustworthy from human perspective. This finding underscores the urgent need for detectors that are not only accurate but also provide understandable explanations for their judgments.
With the proliferation of AIGI, pirf-peeformance AIGI detectors are crucial for news organizations, social media platforms, fact-checking organizations, and the general public identify and resist misinformation, defamation, fraud, and other issues. Furthermore, providing evidence (explainability) alongside the detection results can further enhance persuasiveness. By annotating the artifact regions in generated images, we point out the limitations that generation techniques have during the generation process. This might potentially stimulate further development of generation techniques, which, while aimed at improvement, could also inadvertently make generated content harder to detect in the future, potentially exacerbating misinformation. deception, and socictal manipulation if detection methods do not keep pace.
Our work aims to contribute to building a more transparent and trustworthy digital information environment. By providing fine-graine fine-grained benchmark and in-depth analysis. we hope to stimulate more research on the explainability and reliability of AIGI detection, ultimately helping society better navigate the opportunities and challenges brought about by AI technology.
G LARGE LANGUAGE MODEL (LLM) USAGE
We used Large Language Models (LLMs) as general-purpose assistant for writing and editing this paper. Specifically, LLMs were used for: (1) text refinement: to aid in refining and polishing the manuscript, including improving clarity, grammar, and sentence structure; (2) experiment script generation and editing: to assist in writing and debugging some of the experiment scripts used for data processing, results aggregation, elc.
LLMS were not used for research ideation, methodology, or analysis. All core ideas, research contributions, and experimental results presented this paper are the original work of the authors. The authors have reviewed all -LM--reerrted content and take full responsibility for the final contents of this paper.
29


Reviews:
[{"review_id": "x84GS4bdWt", "reviewer": "Reviewer_PcLD", "summary": "The paper presents X-AIGD, a benchmark for interpretable AI-generated image detection. The dataset pairs real and generated images and provides pixel-level masks of perceptual artifacts. Using this benchmark, the authors show that current detectors, despite strong real/fake accuracy, often do not actually focus on these artifact regions. To improve this, they add an artifact segmentation auxiliary task and an attention alignment loss. These changes yield more interpretable predictions and better cross-dataset generalization.", "questions": "See Weaknesses", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The dataset contribution is concrete and timely, providing pixel-level artifact masks with semantic labels and paired real/fake images. This directly supports studying why a detector made its decision instead of treating the task as only real or fake.\n- The analysis of existing detectors is systematic. The authors compare saliency and attention maps with ground-truth artifact masks and show that they often do not overlap, which raises doubts about how explainable current high-performing detectors actually are.\n- The paper appropriately introduces perceptual artifact segmentation as an auxiliary task and constrains the model to focus on artifact regions through attention alignment. This approach leads to more stable F1 scores across datasets and more consistent visual explanations, directly addressing the limitations of prior detectors.\n- The writing quality is strong, and the logic of the paper is very clear and easy to follow.", "weaknesses": "- There is a growing line of work that uses multimodal large language models (MLLMs) to \"look at an image and generate textual explanations,\" and claims to identify \"where it is fake and why it is fake.\" The authors briefly mention related efforts (e.g., using MLLMs to generate explanations), but a more explicit discussion of works such as [1,2,3] would make the connection to this direction clearer for readers and better contextualize the contribution.\n- The authors report 12 human annotators, 3 annotation rounds, and subjective confidence scores, indicating high pixel-level quality. However, the full-resolution / region-level protocol is extremely expensive, and ultimately only ~3k forged samples received high-quality annotations. This cost is understandable, but it raises a concern: is the pipeline realistically reproducible or extensible for larger-scale data? The paper would be stronger with a discussion of scalability and reproducibility, and possible ways to reduce cost (e.g., using MLLMs to assist or pre-label forged regions).\n- **Typos**: On line 52-53, “X-AIGD (eXplainable AI-**Genereted** image Detection)” should be changed to “X-AIGD (eXplainable AI-**Generated** Image Detection)”\n\n[1] Heie: Mllm-based hierarchical explainable aigc image implausibility evaluator\n\n[2] Spot the fake: Large multimodal model-based synthetic image detection with artifact explanation", "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": "5aVrbkkXZY", "reviewer": "Reviewer_S6KQ", "summary": "The paper introduces X-AIGD, a high-quality, fine-grained benchmark for interpretable AI-generated image detection. It provides pixel-level, multi-level  artifact annotations on paired real-fake images generated from 13 state-of-the-art text-to-image models. Using this benchmark, the authors systematically reveal that existing AIGI detectors barely rely on human-perceptible artifacts, even at the lowest distortion level. They further explore artifact detection as an auxiliary task and attention alignment, showing that explicitly grounding model attention to annotated artifact regions significantly improves both interpretability and cross-model generalization.", "questions": "Could the authors provide the results of MLLM-based explanation methods?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "- X-AIGD introduces the first benchmark with paired real-fake images and pixel-level annotations across 7 fine-grained artifact categories, spanning low-level distortions, high-level semantics, and cognitive-level counterfactuals, enabling grounded interpretability evaluation.\n\n- Empirical analysis surprisingly reveals that SOTA detectors achieve good performance without relying on perceptual artifacts—contrary to common intuition that visible flaws should be primary cues, as evidenced by uncorrelated performance across fidelity metrics in Fig. 3 and severely misaligned heatmaps in Fig. 4.\n\n- Attention alignment to ground-truth artifact regions significantly improves both interpretability and cross-generator generalization, providing a practical and effective method to make AIGI detection more human-trustworthy.", "weaknesses": "- No comparison or integration with MLLM-based explanation methods (e.g., GPT-4o, LLaVA) is provided, despite critiquing their lack of grounding—missing an opportunity to show whether X-AIGD enables better spatial reasoning in language models.\n\n- Inter-annotator agreement (e.g., IoU-based Fleiss’ κ) is not reported, especially critical for subjective high-level and cognitive-level artifacts, potentially affecting reproducibility.", "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": "cYhbH99xcI", "reviewer": "Reviewer_xCmJ", "summary": "This paper introduces an interpretable image forgery benchmark that includes pixel-level masks and artifact categories, featuring a fine-grained taxonomy of seven subcategories grouped into three major types, ranging from low-level distortions to high-level semantic or commonsense anomalies. Moreover, the authors demonstrate that existing methods make limited use of perceptible artifacts, relying primarily on high-level, imperceptible cues for discrimination. To address this issue, the authors design an **attention alignment loss** that explicitly guides the model to focus on perceptible artifact regions, achieving superior performance as a result.", "questions": "Please refer to the *Weakness* section for detailed explanations.  \nIf the authors can adequately address my concerns, I would be very willing to raise my score.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The proposed interpretable benchmark with pixel-level artifact masks is of substantial value to the research community, promoting further progress in explainable image forgery detection and attribution.  \n2. The paper reveals that current detectors make limited use of perceptible artifacts. By introducing an attention alignment loss, the authors encourage the model to make judgments from a human perceptual perspective, which adds credibility to the interpretability of the approach.", "weaknesses": "1. The paper lacks detailed information about whether and when the proposed benchmark will be publicly released. Clear disclosure of dataset availability is essential for reproducibility and community adoption.  \n2. The claim that existing detectors rely on imperceptible artifacts is indeed intuitive. To further validate the effectiveness of the proposed attention alignment loss, more ablation studies are needed. For example:  \n   - (1) What happens if the ground-truth mask is randomly assigned?  \n   - (2) What if attention is forced to align with a normal (artifact-free) object region in the image (which can be segmented using tools such as SAM)?  \nThese experiments would help clarify whether the performance gains truly stem from aligning attention with perceptible artifact regions    rather than from generic spatial regularization effects.", "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": "sZiLFWmCuq", "reviewer": "Reviewer_NFjz", "summary": "The paper introduces X-AIGD, a benchmark for interpretable AI-generated image (AIGI) detection featuring pixel-level masks and a three-level taxonomy of perceptual artifacts (low-level distortions, high-level semantics, cognitive-level counterfactuals). Data are paired: each fake is generated from a caption of a real image using 13 modern generators; a subset is densely annotated via multi-round human labeling. Two tasks are specified: Authenticity Judgment (AJ) and Perceptual Artifact Detection (PAD) (category-aware and category-agnostic). Empirically, the authors show: (i) popular detectors largely do not align with human-perceived artifacts; (ii) PAD as an auxiliary task only marginally improves AJ; (iii) an attention-alignment loss that encourages ViT attention to overlap artifact masks,", "questions": "1. Comprehensive assessment of X-AIGD: expand the dataset validation beyond the current coarse plots and include a side-by-side comparison with other annotated resources (e.g., Legion) along these dimensions.\n\n2. Synthetic edit–driven annotations as a unification strategy: Human annotations of semantic/low-level flaws are noisy. Why not also leverage modern image-edit models (e.g., FLUX-kontext, GPT-4o) to produce programmatically controlled flaws with auto-derived pixel masks from edit operations? Please discuss pros/cons and provide an experiment comparing X-AIGD (human) vs Edit-AIGD (synthetic edits) vs Hybrid (human+synthetic).\n\n3. Using X-AIGD to strengthen VLMs under fair conditions: Since X-AIGD offers pixel-level, typed supervision, it’s natural to test whether it measurably improves a strong VLM (e.g., Qwen2.5-VL) on AJ and PAD. Add a controlled finetune where only supervision differs (with/without artifact masks), holding training budget, data volume, and augmentations constant across VLM baselines.", "limitations": "", "rating": 4, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper tackles a core need in AIGI detection: explainability grounded in evidence. X-AIGD’s large, human-effort annotation effort is itself a significant contribution—creating supervision that most existing benchmarks lack and that practitioners actually need to diagnose model behavior.\n\n\n2. Thoughtful three-tier taxonomy (low → semantic → cognitive): The taxonomy organizes artifacts by perceptual level, which is both conceptually coherent and practically useful. It supports analyses across levels (e.g., low-level texture vs. semantic object integrity vs. cognitive/commonsense violations), and invites targeted model improvements rather than one-size-fits-all heuristics.", "weaknesses": "1. My own experience annotating AIGC artifacts suggests substantial inter-annotator variation—especially for semantic flaws and low-level distortion— that the paper does not adequately discuss this important issue. \n\n   * **Granularity ambiguity.** For the “wrong number of fingers” example in Fig. 1, some annotators box the entire palm while others isolate only the finger region. For the misspelled “McDonald’s,” some include the apostrophe while others do not. Both are defensible, but they produce inconsistent spatial supports and labels that are hard to reconcile.\n   * **Diffuse low-level cues.** Low-level distortions (e.g., “over-smooth skin” to annotate a fake person) are inherently fuzzy; deciding “how much” of a face to include varies widely by annotator.\n   * **Why it matters:** This variance directly affects the reliability of the proposed benchmark and any conclusions drawn from artifact-aligned training.\n\n2. Insufficient design for precise and unified human annotation.  The value of X-AIGD hinges on label precision and consistency, yet the paper offers little methodology to *achieve* them.\n\n   * **Missing protocol depth.** There is no clear calibration phase, no adjudication workflow for contentious cases, and no rubric that operationalizes the taxonomy (positive/negative exemplars, near-miss guidance).\n   * **Coarse quality assessment.** Fig. 2 provides only a coarse human assessment; there is no formal inter-annotator reliability.\n   * Without rigorous agreement analysis and a documented process to reduce disagreement, it is hard to trust the benchmark as a gold standard rather than a noisy training signal.\n\n3. Marginal ablation gains for attention alignment (Table 4).  The reported improvements are small and could plausibly be explained by training noise or seed variance.", "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)