RelayFormer: A Unified Local-Global Attention Framework for Scalable Image and Video Manipulation Localization
Authors: Wen Huang
Jiarui Yang
Tao Dai
Jiawei Li
Shaoxiong Zhan
Bin Wang
Shu-Tao Xia
Venue: ICLR 2026 Poster (2026)
Keywords: Image Manipulation Localization; Video Manipulation Localization
Primary area: alignment, fairness, safety, privacy, and societal considerations
TL;DR: A unified framework for image and video manipulation localization.

Abstract: Visual manipulation localization (VML) aims to identify tampered regions in images and videos, a task that has become increasingly challenging with the rise of advanced editing tools. Existing methods face two central issues. The first is resolution diversity. Resizing or padding can distort subtle forensic cues and introduce unnecessary computational cost. The second is the difficulty of extending spatial models for images to spatio-temporal inputs in videos, which often results in maintaining separate architectures for the two data types.
To address these challenges, we propose RelayFormer, a unified framework that adapts to varying resolutions and naturally handles both static and temporal visual data. RelayFormer partitions inputs into fixed-size sub-images and introduces Global Local Relay (GLR) tokens that propagate structured context through a relay-based attention mechanism. This design enables efficient exchange of global cues, such as semantic or temporal consistency, while preserving fine-grained manipulation artifacts.
Unlike prior approaches that depend on uniform resizing or sparse attention, RelayFormer scales to variable resolutions and video sequences with minimal overhead. Experiments across diverse benchmarks demonstrate superior performance and strong efficiency, combining resolution adaptivity without interpolation or excessive padding, unified processing for images and videos, and a favorable balance between accuracy and computational cost. Code is available at~\href{https://github.com/WenOOI/RelayFormer}{https://github.com/WenOOI/RelayFormer}.

Published as a conference paper at ICLR 2026
RELAYFORMER: A UNIFIED LOCAL-GLOBAL AT- TENTION FRAMEWORK FOR SCALABLE IMAGE AND VIDEO MANIPULATION LOCALIZATION
Wen Huang', Jiarui Yang", Tao Dai Jiawei Li', Shaoxiong Zhan', Bin Wang', Shu-Tao Xia TTinnguu Shenzhen International Graduate School, Tsinghua University
College of Artificial Intelligence, Nankai University
*College of Computer Science and Software Engineering, Shenzhen University "Huawei Technologies Co., Ltd ts huang- tesiinnnttt innguu. edu en, eeittaa.edd@mma111 x siastész. z. tsinsnnnn iigguuuaa..... edu.cn
ABSTRACT
Visual manipulation localization (VML) aims to identify tampered regions in im- ages and videos, a task that has become increasingly challenging with the rise of advanced editing tools. Existing methods face two central issues. The first is resolution diversity. Resizing or padding can distort subtle forensic cues and in- troduce unnecessary computational cost. The second is the difficulty of extending spatial models for images to apatio-temporal inputs in videos, which often results in maintaining separate architectures for the two data types. To address these challenges, we propose Relmerormer, a unified framework that adapts to varying resolutions and naturally handles both static and temporal visual data. Relay- Former partitions inputs into fixed-size sub-images and introduces Global Local Relay (GLR) tokens that propagate structured context through relay-based at- tention mechanism. This design enables efficient exchange of global cues, such as semantic or temporal consistency, while preserving fine-grainee manipulation artifacts. Unlike prior approaches that depend on uniform resizing or sparse attention, RelayFormer scales variable resolutions and video sequences with minimal overhead. Experiments across diverse benchmarks demonstrate superior performance and strong efficiency, combining resolution adaptivity without inter- polation or excessive padding, unified processing for images and videos, and favorable balance between accuracy and computational cost. Code is available at https://gtttbb.ccm/////dd///lltt
INTRODUCTION
Visual manipulation localization (VML), covering both static images and temporally extended videos, is a fundamental task in digital forensics. Its goal is to precisely identify tampered re- gions within visual content. With the rapid proliferation of advanced editing tools, detecting and localizing such manipulations has become increasingly challenging (see Fig. Ta)).
While prior research has predominantly focused improving detection performance 7huu et al. 2025 Lou et al.) 2024), robustness (Guillaro et al.) 2023] Qu et al.) 2023), generalization (Zhou al. 2023 Lou al. 2025). and interpretability (Qu al. 2024a) either for static images for videos, existing methods still face two key limitations that hinder their applicability in real-world scenarios. First, resolution diversity poses significant challenge. In-the-wild content ranges from low resolution (c.g., 256x256) to 4K. Unlike in standard vision tasks, interpolation can destroy the subtle low-level traces crucial for forensic analysis (Guillaro al., 22033] Ma al. 2023). Prior works rely on fixed-resolution training, forcing trade-off: down-samplen inputs to uniform size
"Corresponding authors: Tao Dai.
This work is supported in part by the National Natural Science Foundation of China, under Grant (62302309, 62571298), Tsinghua SIGS KA Conperation Fund.

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Published as a conference paper at ICLR 2026
Authentic
Artifocts
Manipulated
Mask
Local artifacta
Splice
Globoal inconslstency
Copy-Move
TOR Cn
Remove
Blobal matoh
Video Impainting
Local artifacts
Illustration of several common types of visual manipulation, including splicing, copy- move, and inpainting. (a) Examples manipulated regions and their corresponding boundaries generated by these methods. (b) A schematic illustration highlighting the need for both local and global information accurately localize manipulated regions.
Figure 1:
(e.g., 512x512) (Gullaro et al. 2023 Su al. 2025). which risks losing manipulation artifacts, or padding smaller inputs to a large canvas (e.g.. (024) (Ma al. 2023), which incurs substantial computational redundancy. Furthermore, uniform resizing disproportionately distorts content with non-standard aspect ratios (e.g., 9:19.5 in modern smartphones), further compromising forensic reliability.
Second, the extension from static to temporal inputs introduces modeling gap. Images and videos belong to the same visual modality, but videos add temporal dimension that requires reasoning over comeeto-ffrane consistency. Existing algorithms are usually designed either for purely spatial inputs or for
inputs. Image-oriente models cannot leverage temporal cues. whereas video- oriented models often struggle to generalize to single images. This limitation forces practitioners to maintain two separate models, increasing both computational cost and system complexity.
Manipulation localization in images and videos demands delicate balance between fine-grained sensitivity and global semantic reasoning. Manipulated regions are typically small and visually subtle, yet their reliable detection often hinges scene-level consistency cues such illumination patterns, object semantics, or temporal coherence across frames. Although dense global attention can, in principle, capture such dependencies. it is computationally prohibitive for high-resolution content. As illustrated in Fig. b). the global cues essential for manipulation detection are relatively coarse, reflecting scene-level regularities rather than exhaustive pixel-level correspondence. For example, in the splicing case (top right), inconsistencies often manifest as illumination mismatches across the scene; in the copy-move case (bottom right), beyond local artifacts, detection relies on structural redundancy between the duplicated region and its source. These characteristics suggest that sparse yet effective global information propagation is both sufficient and desirable.
Building on this insight, we propose RelayFormer, a unified, efficient, and flexible architecture for VML. The key idea to leverage structured global-local interactions without incurring the prohibitive cost of dense attention. RelayFormer dynamically partitions inputs into fixed-size sub- images according resolution and introduces Global-Locs Relay (GLR) tokens that mediate formation exchange through relay-based attention mechanism. Acting as information bottlenecks, these tokens iteratively absorb scene-level consistency cues, transmit compressed semantics across the entire sample, and reinject enriched context back into their respective regions. Unlike prior ap- proaches (Yang et al. 2021 Su et al. 2025) that reduce computation primarily via sparse attention, the proposed architecture dynamically allocates computation according to input resolution while enabling task-oriented global information propagation. This design ensures scalability to variable resolutions and natural extension from static images temporal sequences.
2

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Published as a conference paper at ICLR 2026
To comprehensively validate the effectiveness of our framework, conduct extensive experiments on a wide range of widely used benchmarks covering both static and temporal visual data. We further provide detailed quantitative and qualitative analyses demonstrating that our method consistently achieves superior performance while maintaining efficiency across diverse settings.
Our main contributions are follows:
Resolution adaptivity. We dynamically handle variable input resolutions without interpo- lation while substantially reducing redundant padding, preserving subtle forensic traces.
Unified image-video modeling. We use a single architecture that naturally supports both spatial (image) and spatio-temporal (video) manipulation localization.
Efficient global-local reasoning. We introduce GLR tokens to propagate structured global context efficiently without relying on dense full-resolution attention.
2
RELATED WORK
2.1 IMAGE MANIPULATION LOCALIZATION
Image-level approaches primarily differ in the forensic cues they exploit. Artifact-based meth- ods (Wu et al. 2022; Wu Zhou, 202I) detect low-level traces such as noise inconsistencies or compression residuals. Although effective in controlled settings, these cues are easily disrupted by common post-processing operations such as resizing or recompression, which leads to unstable per- formance in real-world scenarios. Another line of work leverages contrastive learning or structural priors. FOCAL (Wu et al. 2023) and NCL. (Zhou et al., 2023) leverages contrastive learning to improve generalization ability, while work is complementary
it focuses efficiently han- dling dynamic resolutions and mixed image-video inputs. IML-ViT (Ma et al. 2023) demonstrates that high-resolution vision Transformers benefit from edge supervision, but full resluresotution attention incurs high memory cost and limits scalability to diverse or larger inputs. Multi-scale architectures, including Mesorch (Zhu et al., 2025), improve robustness by enlarging receptive fields or combin- ing convolutional and Transformer features. However, their reliance on high-resolution process- ing introduces substantial computational overhead. Pusion-based models such as TruFor (Guillaro) et al. 2023) and CAT-Net (Kwon et al.) 2022) combine RGB information with noise fingerprints or DCT-domain cues, improving the reliability of forensic evidence. Their performance, however, is constrained by assumptions tied to specific compression characteristics, which reduces their appli- cability to images from heterogeneous acquisition pipelines.
2.2 VIDEO MANIPULATION LOCALIZATION
Video manipulation localization extends spatial analysis incorporate temporal information. Vide- oFACT (Nguyen et al. 2024) enriches spatial representations with contextual embeddings through self-attention, but its quadratic complexity restricts the feasible temporal length. ViLocal (Lou et al. 2025) utilizes contrastive learning to detect local spatiotemporal inconsistencies. In con- trast, UVL2 (Pei 2023) integrates cues such as spatial edges and global pixel distributions within a hybrid design to achieve robust localization. These methods achieve strong generalization yet remain computationally demanding due to dense temporal sampling or high-resolution spatial pro- cessing. VIDNet (Zhou al. 2021) integrates RGB features with error level analysis (ELA) cues through a ConvLSTM decoder, although the reliance on ELA makes method sensitive to modern re-encoding pipelines and common real-world perturbations.
3 METHOD
We present RelayFormer, unified and modular framework for Visual Manipulation Localization (VML) that scales to variable image resolutions and temporal lengths. The framework is composed of three main components: Inpur Unification, Global- Relay Attention, and Query-based Mask Decoder. These components together enable efficient ppatial-tempont reasoning by balancing global consistency with local expressivity, while ensuring computational scalability.
3

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Published as a conference paper at ICLR 2026
Video Processing
Global-Local Relay Attention
7 3x333 of W"
Ohes xT) x x
x x 00-0
Step Z: Localonnare Attertion
Tepur Unifectioo
Potch Embed
00-0
Transforma
Block
Step 1:
Transforma
Image Processing
Ming
Ming x x
Image
00-00
00-00
Giotel Artuntionn
Blogk
{00-00
Potch Emebdd
00-00
00-0 00-0
Video
00-00
Layer
Image
Updared (GLR) tokens
B
Learnable Queries
Feoture Maps
Video Patch tokens
([LR] from layer
Figure 2: Overview our proposed framework, which consists three main components. First, the input image video is partitioned into unified local sub-images without interpolation, preserving fine-grained spatial details. Second, we propose the GLRA module to achieve efficient global infor- mation propagation. Finally, carefully designed lightweight mask decoder efficiently produces the prediction masks. For clarity, the positional encoding components are omitted from the figure.
3.1 INPUT UNIFICATION
To unify image and video inputs into a common representation suitable for parallel computation, we decompose all inputs into slightly overlapping local sub-images, which serve as the atomic process- ing elements in our framework.
Image inputs. Given image RCx Mim x Wins partition it into slightly overlapping sub- images of spatial size H, x Wp. Let the sliding strides along height and width be S₁ and S Padding applied if the remaining region is smaller than a full sub-image. The number of sub-images along each spatial dimension
Hing
Hp Sp.
W img
Wp
N
+1,
N..
+1,
Sw
so the total number of sub-images for the image is Nimg No. x No. The resulting tensor has shape (Nimg: C, Hps Wp).
Video inputs. For a video E Cx we first merge the batch and temporal di- mensions, treating the video (T, C, Hvid. Wvig). Each frame is partitioned in the same way as images, producing Nvid N₁ x Nw. sub-images per frame. The resulting tensor has shape (T' Nvia Hp. W,).
Unified representation. batch of shape
Finally, all sub-images from images and videos are concatenated into a
(Brotat, C, Hy, Wp).
where Biotal Σ images Nimg Evidoss T Nvid- Each sub-image is treated as independent sample in the subsequent local modeling stage, enabling large-batch parallel computation without explicitly distinguishing between image and video inputs. We provide pseudocode in the Appendix A.2.I
3.2 GLOBAL-LOCAL RELAY ATTENTION (GLRA)
To balance efficiency and expressiveness, propose Global-Local Relay Attention (GLRA), which enables efficient propagation global context through a small set learnable tokens, while retaining fine-grained local modeling. Fig. shows the detailed structure of GLRA.

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Published as a conference paper at ICLR 2026
Local-aware Attention. For each sub-image Uz. we apply VIT patch embedding to obtain patch tokens X; E Rxx, where P is the number of tokens and d is the feature dimension. We append a small set of learnable Global-Loca Relay [GLR] tokens Т; Rmxd to each sub-image:
(1 x SelfAttmoca (1T (1T(-1) 1); I).
(I)
where = I,..., represents the layer. In this stage, the [GLR] tokens both relay global informa- tion obtained from previous layers and absorb localized details from X₃.
Relay-based Global Attention. To enable global information exchange, we aggregate [GLR] tokens from all sub-images:
Tan Concat (N
where N; denotes the number of sub-images in the sample. Each [GLR] token is encoded with tem- poral index, spatial location, and token identity using 4D Rotary Positional Embeddings (RoPE) (Su
al. 2024 Wang et al. 2024). The global attention step then:
T Tupdated SelfAttngtsbll (Ro
After global attention, the updated [GLR] tokens are injected back into their corresponding sub- images, enabling iterative information relay: 1) the local attention stage, [GLR] tokens transmit global context into local sub-images while gathering new local evidence; 2) in the global attention stage, they exchange these enriched representations with [GLR] tokens of other sub-images.
Parameter-efficient strategy. Using shared parameters for local sub-module and global sub- module would reduce performance because they have conflicting goal. Shared weights lead to poor performance in both. While conceptually separating local and global attention into distinct Transformer layers is straightforward, this naive approach doubles the parameter count overhead for each such block. Our core motivation stems from the hypothesis that the computational processes for local and global attention, while functionally distinct, share a substantial underlying structure. To capitalize on this insight, we propose parameter--ffficient strategy. We maintain single, shared Transformer backbone layer for both the local and global attention computations. To induce the necessary functional specialization, we introduce two distinct adaptation modules (e.g., LoRA (Hu) al. 2022) or Adapters (Poth et al.) 2023)), one for each attention mechanism. Specifically, the shared backbone layer learns the common, foundational features of the attention mechanism. The adaptation module for local attention learns the specific residual transformation required to spe- cialize the shared function for processing fine-grained patterns, while the module for global atten- tion learns the residual required for long-range, contextual reasoning. This approach allows to achieve the expressive power and performance nearly identical to a two-layer model, but with only single-layer baseline, thereby achieving superior trade-
marginal increase in parameters off between performance and efficiency. We provide more implementation details of this in the Appendix A.2.2
4D RoPE Formulation and Extrapolation. We decompose the hidden dimension of each token into five groups: temporal (T), token index (id), vertical (H), horizontal (W), and the remaining. Specifically, for token vector:
x = [TT, Fid, H. TW: Fremls
where IT E IRdr, aid E RRdu, DH E Rdd, zw E IRds Zrem E RRdrem, with dr +daa 2dg + drem d.
For each group, a standard ID RoPE (Su et al. 2024) is applied independently with the correspond- ing positional index (temporal id, token id, height id, width id). Formally, for sub-vector xg E Rd and index Pg. we apply:
RoPE(x
(20) T
(21+1))
xty cos(p)).)
(2i)
xy 12i+1)
sin (p)
=
(20) sin 0p + x
(25-1) cos(p
z
where 0, 10000-2220 24/d,
5

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Step 1: Relay-based Global Attention
Step 2: Local-aware Attention
Add & Norm
Share Weights
Add & Norm
Global Module
Feed
Feed Forward
Local Module
Forward
Add & Norm
Share Weights
Add & Norm
Global Module
Multi-Head Attention
Multi-Head Attention
Local Module
Patch tokens
[GLR] tokens from the last layer
...
Updated [GLR] tokens
Figure Detailed architecture of the proposed Global-Local Relay Attention (GLRA) module.
The final rotated embedding is:
(oPEạD(x) [oPPE(T)) RoP( RoPP(a)) Trem].
This formulation applies independent rotary encodings across temporal, token index, and spatial dimensions, equipping model with strong extrapolation capabilities variable resolutions.
3.3 QUERY-BASED MASK DECODER
To avoid decoding becoming computational bottleneck, we design lightweight query-baser Transformer decoder, inspired by Mask2Former (Cheng et al., (022). Given the reassembled fea- ture map F E H we first project into lower-dimensional space F E H xW, xdhow A small set of learnable queries Q IR My x then interacts with the projected feature map.
The decoder is composed K stacked layers. At the k-th layer (k 1...., K), query features are updated via a cross-attention followed by self-attention operation:
Q(hy CrossAttn(Q(tt1), F),
(4)
((A) SelfAttu(ooPE(((*)))
(5)
Finally, a gating MLP assigns weights to each query, modulating its contribution to the predicted manipulation masks.
3.4 LOss FUNCTION
Following previous methods (Ma et al. 2023). adopt combination of binary cross-entropy (BCE) loss and edge loss. The overall loss is defined as:
L = P. M) + X LEdge O Me, M @ M.)
(6)
where P is the predicted mask, M is the ground truth, M, is the edge mask, @ denotes the point-wise product and is a weighting factor balancing the two loss terms.
The edge loss applies BCE the edge regions to emphasize boundary accuracy:
Lidge (P May M M.) LaCE (P Me, M Ma)

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Protocol
Method
COVERAGE
Columbia
NIST16
CASIAvI
IMI20200
Averuge
Mantra-Net et ml.
2019
0.090
0.243
0.104
0.125
0.055
0.123
MVSS-Net
0.259
0,386
0.246
0.534
0.279
0.341
CAT:Net KWon et 1122
0.296
0.584
0.267
0.594
0.268
0.402
ObjectFomen (Wang al. 2022
0.294
0.336
0.173
0.429
0.172
0.281
PSCC-Net (Lib
0.231
0.605
0,200
0.378
0.233
0.329
MVSS
NCL-IML Zhou m
2023
0.225
0.446
0.260
0,502
0.237
0.334
Trufor (Guillaro al 20225
0.419
0.865
0.311
0.721
0.317
0.527
IML-ViT [Ma
202.5
0,438
0,747
0.269
0.718
0.328
0.500
Mesorch (Zhu a.
402.5
0.276
0.623
0.283
0.743
0.256
0.436
SparseVi [SU
0.287
0.781
0.245
0.646
0.230
0.438
Relay-ViT (Ours)
0.551
0.762
0.335
0.740
0.381
0.554
Relay-Seg (Ours)
0.569
0,756
0.273
0.760
0.357
0.543
Trufor (Guillaro al
2023
0.451
0.875
0,348
0.821
X
0.627
SparseViT [Su
0.671
CAT
Mesorch (Zhu ai.
2225
0.586
0.890
0.392
0.840
0.677
APSC-Ne QQu 2024b
0.523
0.966
0.436
0.837
0.691
Relay-ViT
0.647
0.878
0.476
0.806
0.702
Relay-Seg
0.704
0.883
0.430
0.802
0.705
Table I: Pixel-level comparison on the image manipulation localization task under both MVSS and CAT protocols. Scores indicate the Fl scores with fixed threshold of 0.5.
4 EXPERIMENTS
Datasets. In our experiments, we conducted comprehensive evaluations using diverse set of benchmark datasets, including CASIA v1.0 (Dong et al., 2013), CASIA v2.0 (Dong et al. 2013),
Columbia (Hsu & Chang, (006). Coverage (Wen et al. (016), NIST16 (Guan et al.
2019
IMD2020 (Novozamsky et al.] 2020). Fantastic Reality (Kniaz et al. 2019), TampCOCO (K won et al., 2022), DAVIS-VI (Yu al. (02T). and MOSE100 (Lou et al., 2025). Following widely
accepted and fair evaluation protocols, we adhered to the evaluation guidelines recommended by IMDLBench [Ma et al. 2024), ensuring consistency and comparability with prior studies.
Implementation Details. ensure fair comparisons and consistent experimental conditions, all experiments were conducted using the IMDLBench (Ma et al. 2024) framework. We conduct ex- periments using VIT and SegFormer as backbones, referred to as Relay- ViT and Relay-Seg respec- tively. We set the number of [GLR] tokens the sub-image size to 256 256 and the clip length to 4. We trained our models for 200 epochs using the AdamW optimizer (Loshchilov & Hutter 2019) with base learning rate of le-4, scheduled by
2. sub-image size to 512 x 512. For video, set
a cosine decay policy (Loshchilov & Hutter 2017). For more details, see the Appendix AA.4
Evaluation Metrics. We evaluate the performance the predicted masks using two commonly adopted metries: F1 score (with fixed threshold of 0.5) and Intersection over Union (IoU).
MOSE100
Methods
E2FGVI (IoU/FI)
FuseFormer (loU / FI)
STTN (loU F1)
Mantra-Net Wu et al.
2019
0.378/0.524
0.385/0.531
0.356/0.505
MVSS-Net
(Chen et 202
0.038/0.057
0.051/0.074 0.094/0.133
TruFor Guillaro et al. 2023
0.311/0.414 0.285/0.388
0.260/0.353
Focal Y ang al. 202
0,098/0. 150 0.138/0.200
0.152/0.226
LruVIL
Lou et al.
2024
0.521/0.674
0.557/0.699
0.462/0.612
ViL.cal
[Lou al.
2025
0.485/0.68 6200
0.597/0.721
0.393/0.524
Relay-Vi ViT
0.552/0.689
0.561/0.695
0.549/0.684
Relay-Seg
0.561/0.698 0.554/0.692 0.534/0.674
Table 2: Quantitative comparison on the video manipulation localization task on three different video inpainting methods. For studies without open- implementations, we report the results as presented in their original papers to ensure fair comparison.

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Model
Parameters (M)
GFLOPs
Note
MVSS
150.53
171.11
Input: 512x512
PSCC
3.67
376.83
Input: 256x256
CAT-Net
116.74
137.22
Input: 512x512
TruFor
68.70
236.54
Input: 512x512
Mesorch
85.75
124.93
Input: 512x512
IML-ViT
91.78
576.78
Input: 1024x1024
Relay-ViT
89.35+2.36
119.18/ 238.20 476.12
N 2, N 1,
Relay-Seg
45.90+2.39
52.71 / 105.41 210.83
Table 3: Model complexity comparison: parameter counts (M) and computational cost (GFLOPs). The bolded part in our models indicates additional parameters. Multiple GFLOPs values correspond to different sub-image counts N 1, 2, 4.
PradeTad YOUR
Figure Visual qualitative results for image and video scenarios.
4.1 COMPARE WITH SOTA METHODS
Image Manipulation Localization. Following Protocol-MVSS (Chen et al. 202T). we train on CASIAv2 and test others. As shown in Table [ Relay-ViT and Relay-Seg achieve superior or competitive results across all datasets. Our framework reaches the highest average score (0.554), surpassing prior methods such Trufor and IML-VIT. To further evaluate robustness, we also adopt Protocol-CAT. We utilize mixed training set comprising CASIAv2, Fantastic Reality (Kniaz et al.) 2019, IMD2020, and TampCOCO (Kwon al., 2022 and evaluate on the remaining datasets (excluding IMD2020). In this challenging setting, our methods continue to excel.
Video Manipulation Localization. Following TruLIL (Lou et al. 2024) and ViLocal, we train OP (Oh et al. 2019) and VI (Kim et al. 2019) edited DAVIS2016 (Perazzi et al. 2016), and test on E2FGVI (Li al. (2022), FuseFormer (Liu et al. 2021). and STTN (Zeng al. 2020) edited MOSE. Table 2 shows that both models achieve foate-of-the-woee results: Relay-Seg leads on E2FGVI, while
(Li
Relay- VIT performs best STTN, confirming robustness across different inpainting models.
As shown in Fig. and in the Appendix A.9 our method also demonstrates superior performance in visual results.
8

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Num.
Training set
COV.
Col.
NIST16
CASTAvI
IMD2020
Splice
MOSE100
1
Img+ V-All
0.569
0.755
0.282
0.753
0.357
0,472
0.684
2
I-g+
0.569
0.756
0.282
0.753
0.357
0.476
0.09
3
Imn+V-Inp
0.570
0.733
0.308
0.748
0.357
0.133
0.681
4
V-Spl
0.051
0.147
0.163
0.143
0.219
0.264
0.119
5
V-Inp
0.005
0.139
0.066
0.029
0,061
0,003
0.688
6
Img
0.551
0.762
0.335
0.740
0.381
0.458
0.082
Table 4: F1 across datasets under different training configurations.
[GLR] (n)
Decoder
COV.
Col.
NIST16
CASIAv1
IMD2020
Average
0.486
0.596
0.248
0.691
0.248
0.454
1
0.548
0.718
0.289
0.751
0.301
0.521
0.559
0.696
0.292
0.757
0.355
0.532
2
<
0.551
0.762
0.335
0.740
0.381
0.554
3
0.556
0.714
0.260
0.761
0.327
0,524
Table 5: Ablation study manipulation detection (F1 scores) across five benchmarks. We vary the number [GLR] tokens (n = 0, 1, 2, 3), (n = 0) means without the GLRA module, and evaluate the performance of our mask decoder.
4.2 INTERACTION BETWEEN IMAGE AND VIDEO IN UNIFIED TRAINING
We conduct a series of experiments study how images and videos influence each other when trained within unified model. Table 4 reports the Fl obtained under six training configurations: image-only (Img), video-inpaintingonie (V-Inp), video-splice-only (V-Spl) (Singla et al.) 2023). image + video inpainting (Img+V- and image video inpainting + video splice (Img+V-All).
From Experiments 1, 2, and 3, we observe that adding video forgeries to image data does not no- ticeably improve image-domain performance. This is mainly because current video datasets lack diversity and precise annotations, while image datasets already provide rich and reliable spatial ma- nipulation cues.
Comparing Experiments 2, 4, and 6, we find the opposite direction to be effective: image data clearly strengthen video forgery detection for manipulation types shared across both domains. High- quality image forgeries give the model a solid set of spatial cues that transfer well to video frames, effectively serving strong "starting point" for learning video manipulations.
Finally, Experiments and 5 show that when image and video datasets contain non-overlapping manipulation types, no mutual benefit appears. Without shared artifact patterns, joint training offers no advantage over single-source training.
4.3 FLOPS AND PARAMETERS
As shown in Table 3 our framework adapts dynamically to varying input resolutions, reducing redundant computation with minimal parameter overhead. See the Appendix A.8 for more detailed analysis of time complexity and parallelism.
4.4 ABLATION STUDY
We conduct ablation experiments to assess the contribution of each component from three perspec- tives: (1) the number of [GLR] tokens and the role of the GLRA module (n=0), (2) the Query-based Mask Decoder, and (3) spatial-temporal cues and interpolation strategies.
Table reports results five benchmarks. Adding single [GLR] token (n=1) improves results,
and substituting the MLP with our decoder further boosts performance (0.532). The best perfor- mance occurs n=2, while n=3 slightly degrades results due to redundancy (Fig. 6 We provide

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Spatial Temporal
F1
IoU
Metric
w/o resi ze
w/ resize
0.6124
0,4828
Res.
2958 4437
x024x 1024
0.6745
0.5391
FI
0,453
0.350
0.6877
0.5524
Table 7: Impact interpolation (IMD2020).
Ablation of GLRA along spatial and temporal dimensions (MOSE100).
Table 6:
Res. denotes the maximum resolution.
GaussianBlur
GaussNoise
Jpeg
0.7
0.6
0.4
13
0.2
0.0
Kernel Size
Noise Level
Duslity factor
PSCC
Figure 5: Robustness analysis results the model under common perturbations.
further analysis of the behavior of the [GLR] token along with additional visualizations in the Appendix A.5
Effectiveness GLRA along Temporal Dimensions We further investigate the effectiveness of applying GLRA solely along the spatial dimension versus jointly across both spatial and temporal dimensions in video detection, in order to verify that our method can indeed extend to capturing temporal information in videos. The corresponding results are presented Table 6
Effeet of Input Resolution on Performance and Extrapolation to Higher Resolutions Table illustrates the impact of input resolution on detection accuracy. Resizing 4K inputs to 1024x 1024 substantially degrades performance (0.350 0.453), interpolation obscures high-frequency forensic artifacts. Conversely, the model demonstrates robust extrapolation capability. Despite being trained lower resolutions (max 600, it achieves peak performance on raw 4K images. This confirms that the model generalizes beyond its training scale, effectively leveraging the fine-grained cues available higher resolutions.
4.5 ROBUSTNESS EVALUATION
We assess the robustness different methods under common corruptions: Gaussian Blur, Gaussian Noise, and JPEG Compression. As shown in Fig. [ RelayFormer consistently outperforms prior methods across all distortion types and levels. maintains higher Fl scores under increasing blur, noise, and compression, demonstrating strong generalization real-world degradations.
CONCLUSION
In this work, we introduce RelayFormer, unified framework tackling resolution diversity and temporal extension in manipulation localization. By employing Global Local Relay (GLR) tokens across fixed-size sub-images, relay-based attention efficiently propagates scene-level context while preserving fine-graine -ra-grained forensic evidence. This design enables the processing variable resolutions and video sequences. Extensive experiments demonstrate that RelayFormer achieves superior performance and computational efficiency, offering a scalable solution both static and temporal visual data.
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A APPENDIX
A.1 LIMITATIONS
Limitations on global context modeling. While our partition-and-reil design achieves a favor- able trade-off between accuracy and efficiency, inevitably introduces limitation compared to full global attention. Specifically, when manipulations span across multiple sub-images, the Global- Local Relay Attention (GLRA) propagates contextual information through relay tokens rather than establishing exhaustive pairwise interactions. This relay mechanism is computationally more effi- cient, yet it cannot capture cross-partition dependencies as precisely as global attention scheme computational constraints are disregarded.
A.2 DETAILED DESCRIPTION OF THE METHOD
A.2.1 INPUT UNIFICATION
To more clearly demonstrate how we preprocess videos and images into a unified form, we provide relevant pseudocode
Algorithm Sub-images Extraction
Require: Image set Ving, video set Keis patch size (Hps Wp), stride (Sh, Sw)
Ensure: Unified tensor E H
1: X < empty list
2: for each image x E Xing do
3: H. W spatial dimensions of
4: N₂ < [H 5p +1
5: No + [(W wpp/S=1+1
6: Extract N₁ No patches using sliding window
7: Append patches to X
8: end for
9: for each video x Xii do
10:
T, H. W + dimensions of
N₁ +- [H //
12:
Nw + [W W Sp/ 1+1
13: Extract patches from first frame: P + patches from x(0]
14: Repeat P along temporal dimension: Prall repeat (P. T)
15: Append Piul to
16: end for
17:
Stack all patches into tensor of shape (Brotat, C, Hp. Wp)
18: return X
A.2.2 PARAMETER EFFICIENT STRATEGY
Using shared parameters for both local and global attention severely degrades performance because the two modules serve fundamentally different purposes and operate on distinct feature spaces. Local attention works on dense, low-level patch tokens (X1), focusing on fine-graine fine-grained textures, edges, and object parts. Global attention. by contrast, processes sparse, high-level [GLR] tokens (Trm), which summarize sub-images and model long-range dependencies. A single set of projection weights cannot simultaneously specialize in local detail extraction and global structural reasoning, leading to suboptimal representations in both tasks. Therefore, separate parameterization essential to preserve both local fidelity and global coherence.
As shown in Fig. 3 our solution introduces functional specialization via dynamic parameter- sharing scheme based on Low-Rank Adaptation (LoRA). Each Transformer block maintains a shared set of backbone projection matrices (W W Wv), which are fully trainable. On top of this backbone, we add two task-specific sets of LoRA parameters: {A Broea for SelfAtthiccal and (Aglobal, Bytohal) for SelfAttn Sel During the forward pass, the effective weight is dynamically
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constructed. For example, in local attention:
We Wo Aqcal
while in global attention:
W = We + Aq.glabul
Here, Wq provides a shared backbone, and LoRA contributes lightweight, context-specific adjust- ments.
Unlike conventional LoRA fine-tuning, our backbone remains trainable, and the LoRA parameters are never merged into This design crucial: rather than adapting a frozen model single task, we enable two co-existing functional modes that can be switched dynamically. The result is efficient parameter sharing that preserves specialization for both local and global reasoning.
A.3 DATASETS
A.3.1 IMAGE DATASETS
We use the following publicly available datasets for the detection of spliced and copy-moved images, following previous settings (Ma et al. 2023 2024), we didn't 't use real images in all datasets:
To provide detailed overview of these datasets, Table 8 summarizes key attributes, including the number of images or videos, forgery types, and other relevant characteristics. All details are sourced from official or authoritative descriptions ensure reliability.
Table 8: Overview of benchmark datasets for image forgery detection. Forgery types include splic- ing (S), copy-move (C), removal/inpainting (R), and others (0).
Dataset
Year # Authentic
# Forged
Image Forgery Datasets
CASIA v1.0
2013
800
921
CASIA v2.0
2013
7,491
5,123
Columbia
2004
183
180
Coverage
2016
100
100
NIST16
2016
560
564
IMD2020
2020
414
2010
Al-Generated Forgery Datasets
AutoSplice
2023
2,273
3,621
CocoGlide
2023
A.3.2 VIDEO DATASETS
For video inpainting experiments, the following datasets:
Dataset
Use
DAVIS 2016 (Perazzi et al. MOSE (Ding al. 2023)
2016)
Generate training Cross-dataset test
Table 9: Video datasets used in our workflow.
Details of usage. DAVIS 2016 contains 50 short videos, split into 30 training and 20 validation clips Peraazi et al. 2016). We use two video inpainting models- OP (Oh et al. 2019) and VI (Kim generate manruteed-reconstuuue
et al. 2019)
frame pairs for training.
The MOSE dataset includes videos and object masks with complex scenarios (Ding et al. 2023
We the validation split of 100 clips test set for evaluating, using three methods: E2FGVI ct al. 2022), FuseFormer (Liu et al. (02T). and STTN (Zeng et al. 2020) models to create validation datasets.
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A.3.3 DATA SPLIT SUMMARY
Image tasks: CASIA v2.0 used for training. Other image datasets (CASIA v1.0, Columbia, Coverage, NIST16, IMD2020) used for cross-datasct testing.
Video tasks: DAVIS 2016 is used to generate training data via OP and VI models. MOSE validation split is used for testing with E2FGVI, FuseFormer, and STTN.
A.4 IMPLEMENTATION DETAILS
To ensure fairness and consistency, all experiments are conducted with the IMDLBench (Ma et al, 2024 framework and follow the training configuration of IML-ViT. ViT and SegFormer backbones are adopted (denoted as Relay-Vis ViT and Relay-Seg). All Transformer blocks are replaced by GLRA modules, and the number of [GLR] tokens
to n 2. For 4D RoPE, following (Wang al. 2024), each of the four structured axes (T, id, H, W) is assigned an equal dimension size, and any leftover dimensions are grouped into the remaining part.
Component
Setting
Backbones
HiT-Base-patchle SegFormer
[GLR] tokens
Sub-image size (image)
528 x 528
Sub-image size (video)
256 256
Temporal clip length
4 frames
Mask decoder layers (K)
Learnable queries
Frozen epoch
(freeze pre-trained weights)
Edge loss weight
20
LoRA rank
LoRA scaling factor
Table 10: Model and Architecture Settings.
Image and Video Preprocessing. Images larger than x024 1024 resized by scaling the longer side to 1024 while maintaining aspect ratio, followed by zero-padding to 1024 x 1024. For video data, apply similar process.
Data Augmentation.
We follow IML-VIT (Ma et al. 2023) and apply:
re-scaling and random horizontal flipping.
Gaussian blurring and random rotation,
copy-move and inpainting of rectangular regions.
A.5 UNDERSTANDING GLRA BEHAVIOR
To further investigate the effect of GLRA, we visualize intermediate attention maps and feature activation patterns in Fig. 6 and Fig. 7 Without GLRA, the representations of different spatial sub-images tend to diverge, as local self-attention lacks mechanism for sufficient global interac- tion. This leads to fragmented feature distributions that fail to capture cross-region dependencies. In contrast, GLRA introduces an explicit relay pathway for long-range communication, enforcing semantic consistency across sub-images.
Moreover, we find that when employing two [GLR] tokens, each token naturally specializes in distinct spatial regions, suggesting a clear division of labor that supports complementary global rea- soning. However, when the number of relay tokens increases to three, one of the tokens consistently collapses into attention pattern nearly identical to that of another token, indicating redundancy and an unclear functional role, as shown at the bottom of Fig. 6 We hypothesize that this re- dundancy introduces competition among relay tokens, weakening their ability form stable and
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Parameter
Value
GPUs
x NVIDIA RTX 3090
Precision
AMP (mixed precision)
Per-GPU batch size
Gradient accumulation steps
Effective batch size
64
Epochs
200
Optimizer
AdamW (Loshchilov & Hutter 2019)
Base learning rate
x 10
LR schedule
Cosine decay Loshchilov & Hutter 2017
Warmup epochs
Minimum learning rate
10-7
Weight decay
0.05
Random seed
Test-time augmentation
None
Table 11: Training Configuration.
Manipulated Images
Sub--Image
Attention Maps of [GLR] tokens
[GLR]
[GLR]
(GLR)
Figure 6: Attention map visualization of [GLR] tokens for analyzing their behavioral patterns.
meaningful global interactions. Consequently, the representational ambiguity introduced by the ex- tra token leads to diminished performance. These observations support our empirical finding that setting the number of relay tokens to = 2 strikes an effective balance between modeling capacity and computational efficiency.
A.6 ADDITIONAL CROSS-DATAS EVALUATION
We further evaluate models three additional datasets, with results summarized in Table 12 Overall, our Relay-based methods consistently achieve leading performance under broad testing conditions. In particular, Relay-Seg attains the highest average F1 score (0.372). surpassing all com-
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Temporal inconsistency
Spatial inconsistency
w/ GLRA
w/ GLRA
w/o w/o GLRA
GLRA
Frame 0
Frame
Frame 2
Figure 7: Qualitative results illustrating the role of GLRA.
peting baselines. This demonstrates that the proposed GLRA mechanism generalizes well across diverse forgery types and input distributions.
noteworthy observation the impact of backbone choice when facing Al-generated forgery datasets. For example, both Relay-ViT and IML-ViT, which are based Vision Transformer archi- tectures, achieve a similar level of performance. contrast, Relay-Seg outperforms other methods, while Sparse VIT also exhibits competitive results.
These findings not only validate the robustness of our relay-based formulation but also suggest that backbone-level inductive biases play significant role in detecting Al-generated content. Im- portantly, our approach benefits from these architectural strengths while introducing only minimal overhead, thereby offering both efficiency and adaptability.
Method
AutoSplice CocoGlide
Defacto-12K
Average
IML-VIT (Ma al. 2023)
0.221
0.210
0.367
0.266
Sparse ViT (Su et al. 2025
0.386
0.142
0.242
0.257
Mesorch Zhu et al. 2025
0.216
0.120
0.292
0.209
Relay-Vit ViT
0.289
0.203
0.416
0.303
Relay-Seg
0.379
0.328
0.409
0.372
Table 12: Performance comparison of different methods on three datasets (metric: Fl score, higher better)
A.7 MORE ABLATION STUDIES
Studies on the Model Architecture. To evaluate the effectiveness of GLRA when integrated into different parts of the backbone, we conducted experiments with three replacement strategies on the CASIA vl dataset: (1) inserting GLRA into sparse set of layers across the transformer encoder 30,3,77 11], (2) replacing all layers in the latter half of the encoder (6-11), and (3) replacing all layers in the encoder (i.e., full replacement). As shown in Figure g progressively increasing the number of GLRA-applied layers Icads to consistent improvements in Fl scores: 73.24%, 74.21%, and 75.50%, respectively. These results indicate that GLRA contributes more significantly when applicd to deeper layers and that full-layer integration yields the best performance. This suggests that GLRA is both effective and scalable when applied throughout the model architecture.
Effect of 4D RoPE. To isolate the impact positional encoding, we remove the 4D RoPE from the GLRA RA module and observe both training stability and final performance. As shown in Table 13
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Impact of GLoRA Layer Replacement on CASIA v1 Performance
0.760
0.7550
0.755
0.750
0.745
0.7421
Score
0.740
0.735
0.7324
0.730
0.725
0.720
[0, 3, 7, 11]
[6-11] Replaced Transformer Layers
All Layers
Figure Impact of GLRA layer replacement strategies on CASIA vl.
the removal of RoPE leads significantly more unstable training dynamics, exhibiting larger gradi- ent fluctuations and slower convergence. Performance is also consistently worse across all datasets, with the average score dropping from 0.554 to 0.517. These findings indicate that 4D RoPE provides essential spatial consistency for the compressed relay representations, stabilizing the optimization process and enhancing the model's ability capture long-range structural information.
RoPE Coverage
Columnbia
NIST
CASIAv1
Avg.
0.542
0.682 0.762
0.301
0.707
0.517
0.551
0.335
0.740
0.554
Table 13:
Ablation on the use of 4D RoPE in the GLRA module. Removing RoPE results unstable training and degraded performance.
Ablation on Sub-Image Size and Overlap. For video inputs, we adopt a sub-image size of 256 x 256. The primary constraint the resolution of the training data, whose maximum spatial size is 512 x 512. Using 512 512 sub-images would reduce GLRA to full attention. Hence, 256 x 256 is the largest feasible choice that preserves spatial hierarchy while maintaining the intended behavior of GLRA.
For image inputs, larger sub-images provide stronger discriminative capacity but incur higher quadratic computational cost. We adopt 512 x 512 as balanced choice between accuracy and efficiency. further illustrate this trade-off, we additionally compare 256 x 256 sub-images with and without overlap. The results are summarized in Table [4
Coverage
Columnbia
NIST
CASIA
IMD2020
Avg.
overlap
size
0.389
0.658
0.260
0.685
0.355
0.469
w/o
256
0.434
0.645
0.261
0.709
0.337
0.477
w/
256
0.551
0.762
0.335
0.740
0.381
0.554
512
Table 14: Ablation sub-image size and overlap for image inputs.
These results show that 512 512 yields notable performance gains over the 256 x 256 setting, supporting choice effective trade-off between accuracy and computational cost.
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We use overlap of 16 pixels, corresponding exactly one ViT-Base patch. This ensures conti- nuity across adjacent sub-images while remaining compatible with the pretrained backbone. Larger overlaps substantially increase computation with limited benefit, whereas removing overlap weakens cross-region consistency.
A.8 COMPLEXITY AND PARALLELISM ANALYSIS
As shown in Table both Relay- ViT and Relay-Seg introduce only negligible number of ad- ditional parameters 2.4M) compared to their respective backbones, demonstrating that GLRA incurs minimal overhead. Despite this, our methods substantially reduce computational cost rela- tive to prior wansformer-basee baselines. For example, Relay- ViT achieves a lower GFLOPs budget than MMLVVIT even when operating with =4 sub-images at 1024 x 1024 resolution. Moreover, the scalability of our design is evident: the GFLOPs grow linearly with the number of sub-images, while the parameter count remains nearly constant. This highlights the efficiency of our relay-based for- mulation, which decouples global reasoning capacity from the quadratic growth in input resolution. Overall, Relay-ViT and Relay-Seg strike a favorable balance between model size, computational efficiency, and representational power, validating the practical advantage of the proposed GLRA mechanism.
Time complexity. In the local attention stage, each sub-image U3 contains P patch tokens and m [GLR] tokens, yielding a total of P m tokens per sub-image. The self-attention operation in Eq. equation I thus requires ((P++m)² computations per layer, where dl the hidden dimension. Across all sub-images in the batch, the total complexity scales linearly with Biotal, the unified number of sub-images.
In the global attention stage, the complexity depends only on the number of [GLR] tokens. For a sample with N; sub-images, the concatenated sequence length N; m, leading to a cost of O(NNm)) (d) per layer in Eq. equation 3 Compared to local attention, this is relatively lightweight, since m < P and N; is typically small.
The unified representation in Sec. 3.1 enables straightforward parallelization across all sub-images. However, the number of sub-images N; may vary across samples due to differing input resolutions, resulting in variable numbers of [GLR] tokens. To enable efficient batched com- putation, we pad the sequence of [GLR] tokens in each sample to the maximum length within the batch. This ensures that global attention can be executed in parallel without irregular memory access patterns. Since the number of sub- sub-images per sample is usually small, the overhead introduced by such padding is negligible in practice, while the benefit of full parallelization is substantial.
Parallelism.
A.9 MORE VISUALIZATION RESULTS
In this section, provide additional qualitative comparisons further demonstrate the effective- ness of our method. Figure 9 showcases a variety of manipulated images and videos, along with their corresponding ground truth masks and the predicted results from different baseline methods, including CAT-Net, PSCC-Net, Trufor, and Mesorch. Our method, RelayFormer, consistently gen- erates more accurate and fine-grate -ee-graieed manipulation masks, with better localization and fewer false positives compared to previous approaches.
For manipulated video sequences, our model not only detects spatial tampering more precisely but also captures temporal consistency across frames, which is essential for robust manipulation detec- tion videos.
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Monipulated Image
Grownd Truthh
CAT-Net
PSCC-Net
Trufor
Mesorch
RelsyFormer
K
K
Manipul afed Video
Manipulated Video
Ground Truth
Ground Truth
Predicted Mosk
Predicted Mask
Manipulated Video
Manapula pulap Video
Ground Truth
Ground Truth
O
Predicted Mask
Predicted Mask
Figure 9: Qualitative comparisons on manipulated images and videos. Our method (RelayFormer) shows superior performance in both spatial and temporal prediction accuracy compared to prior methods.
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B STATEMENT ON THE USE OF LARGE LANGUAGE MODELS (LLMS)
In the preparation of this manuscript, Large Language Model (LLM) was used solely for the purpose of language polishing, including minor grammar correction and stylistic refinement of the authors' original text. The LLM did not contribute to the conceptualization of the research, the design of experiments, the analysis of results, or the interpretation of findings. All research ideas, methods, and conclusions presented in this paper are entirely the work of the authors.
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Reviews:
[{"review_id": "eLgQH9EQfK", "reviewer": "Reviewer_9ZYn", "summary": "This paper proposes RelayFormer, a unified framework for visual manipulation localization across both images and videos. It addresses two major challenges: resolution diversity and the image–video modality gap. The key innovation is the Global-Local Relay Attention (GLRA), where Global-Local Relay (GLR) tokens propagate global context efficiently between local sub-images, enabling scalability without interpolation or dense attention. A lightweight query-based decoder produces precise masks. Extensive experiments show state-of-the-art accuracy, strong robustness, and excellent efficiency on multiple benchmarks.", "questions": "Will the code be publicly available?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "- The insight of bridging image and video manipulation localization holds novelty. It will offer a benchmark case for future multi-modal VML tasks.\n- The proposed method’s resolution strategy offers a significant computational efficiency advantage within the current field of manipulation detection.\n- The experiments in this paper are comprehensive and well-organized, effectively demonstrating the proposed claims.", "weaknesses": "- The proposed method shares some similarities with Visual Prompt Tuning[A], as it introduces additional trainable tokens to transmit information and assist decision-making. It is recommended that the authors discuss this connection in an appropriate section of the paper.\n- To the best of my knowledge, RoPE is introduced for the first time in a manipulation localization model. The authors may consider analyzing the advantages of this positional embedding for a pure computer vision task like manipulation detection, ideally supported by additional experiments.\n- We are currently in an era that values scaling up as an important perspective. Although the authors claim that the proposed structure bridges image and video manipulation localization, the experiments are conducted separately for the two modalities. It would be interesting to explore whether joint training on both images and videos could lead to further improvements or provide additional insights into scaling behavior.\n\n## Reference\n- [A] Jia, Menglin, et al. \"Visual prompt tuning.\" European conference on computer vision. Cham: Springer Nature Switzerland, 2022.", "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": "CBu0ROmg4z", "reviewer": "Reviewer_3x25", "summary": "The paper tackles visual manipulation localization for both images and videos, addressing resolution diversity and modality gaps. It proposes RelayFormer, which partitions inputs into fixed-size sub-images and introduces Global-Local Relay (GLR) tokens to propagate global context via a Global-Local Relay Attention (GLRA) mechanism. The method includes a parameter-efficient strategy using shared backbone layers with task-specific adapters, a 4D RoPE positional encoding, and a lightweight query-based mask decoder. Experiments on multiple image and video benchmarks show improved F1/IoU performance against prior work, with analyses of FLOPs/parameters and robustness.", "questions": "Please see the weaknesses.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 4, "strengths": "A new framework for unified image and video forensics.\n\nAchieves top average F1 on image benchmarks, and consistently high IoU/F1 across video inpainting methods on MOSE.\n\nRobustness curves for Gaussian blur, noise, and JPEG compression.", "weaknesses": "The proposed approach relies on fusing local and global information, a well-established technique in computer vision whose effectiveness is often assumed.\n\nWhile results from training on both are presented, there is no clear explanation or investigation into how these modalities mutually influence each other. An intuitive hypothesis is that images can be considered single frames of videos, and training on image forgery could enhance video forgery detection (and vice-versa). To truly demonstrate the benefit of a unified approach, it is suggested to include experiments evaluating model performance when trained exclusively on one modality (image or video) and then tested on both. This would shed light on the synergy between the two data types.", "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": "3hIdxRGm53", "reviewer": "Reviewer_M9mm", "summary": "This paper proposes the RelayFormer framework for unified visual manipulation localization in both images and videos. The authors claim their method addresses two primary challenges: resolution diversity and the image-video \"modality gap.\" The core innovation is the Global-Local Relay Attention (GLRA) mechanism, which propagates global context across fixed-size sub-images through learnable Global-Local Relay (GLR) tokens. The paper divides inputs into overlapping sub-images to avoid interpolation, introduces GLR tokens as an information bottleneck for efficient global-local interaction, and employs a LoRA-based parameter-efficient strategy. Experimental validation is conducted on multiple image and video manipulation datasets.", "questions": "Refer to the weaknessses.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1.The research problem is well-motivated. The paper correctly identifies that manipulation detection requires simultaneous consideration of both fine-grained local artifacts and coarse-grained global consistency cues (such as illumination mismatches and structural redundancies). This provides a sound foundation for proposing an efficient global information propagation mechanism and offers valuable insights for the forensics community.\n\n2.The input unification strategy (Section 3.1) is practical. By partitioning variable-resolution images and videos into fixed-size overlapping sub-images, the method avoids interpolation operations that could destroy forensic traces. This strategy is both simple and effective, supports parallel processing, and demonstrates practical engineering value.\n\n3.The experimental validation is comprehensive. The paper evaluates the approach on 8 datasets covering both image and video scenarios. Ablation studies systematically analyze GLR token quantity (Table 4), temporal dimension (Table 5), and interpolation effects (Table 6). Robustness evaluation (Figure 5) tests against common perturbations, with generally sound experimental design.", "weaknesses": "1. The authors emphasize that images and videos belong to different modalities, which is imprecise. Multimodality typically refers to different data types (e.g., vision and text, vision and audio). Both images and videos belong to the visual modality, differing only in the presence or absence of temporal dimension. This conceptual confusion weakens the theoretical positioning of the paper. It would be more appropriate to frame this as a \"temporal dimension extension problem\" rather than a \"modality gap.\"\n\n2. The technical novelty is limited. The technical contributions of this work are relatively incremental. The use of fixed-size sub-images and Global-Local Relay (GLR) tokens represents fairly standard techniques. Fixed-size sub-images are a common operation in image and video processing (standard practice in ViT and similar architectures). The proposed GLR tokens show no significant distinction from existing prompt tuning methods [1][2][3][4], as they essentially all perform information aggregation through learnable tokens. The paper fails to adequately differentiate GLR tokens from prior work such as Dgl [3] and Visual Prompt Tuning [4]. The innovative contributions are primarily at the application level rather than the methodological level.\n[1] Liu, Xiao, et al. \"P-tuning v2: Prompt tuning can be comparable to fine-tuning universally across scales and tasks.\" arXiv preprint arXiv:2110.07602 (2021).\n[2]Zhang, Ji, et al. \"Dept: Decoupled prompt tuning.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n[3] Yang, Xiangpeng, et al. \"Dgl: Dynamic global-local prompt tuning for text-video retrieval.\" Proceedings of the AAAI Conference on Artificial Intelligence. Vol. 38. No. 7. 2024.\n[4] Jia, Menglin, et al. \"Visual prompt tuning.\" European conference on computer vision. Cham: Springer Nature Switzerland, 2022.\n\n3. The readability needs improvement and the related work section is weak. The writing quality requires enhancement. For example, lines 103-104 and 152-153 employ excessive use of dashes. The Related Work section (Section 2) is overly brief, appearing more as a literature listing than a critical analysis. It lacks in-depth discussion of why existing methods fail. The writing should be more direct and professional to enhance clarity.\n\n4. The 4D RoPE design lacks sufficient justification. The 4D RoPE positional encoding design (Section 3.2) lacks adequate theoretical foundation. How are the dimension allocations (d_T, d_id, d_H, d_W, d_rem) determined (lines 261)? The paper only provides formulas without sensitivity analysis. More importantly, the paper claims \"strong extrapolation capability\" (line 270) but provides no experimental verification. For instance, what is the performance on 2K or 4K images after training at 512×512 resolution? This claim lacks empirical support.\n\n5. Hyperparameter selection lacks rigor. The choice of n=2 for GLR token quantity (line 311) is determined only through ablation studies, but why does n=3 lead to performance degradation? The paper merely speculates \"due to redundancy\" without deeper analysis. Similarly, the choices of sub-image sizes (512×512 for images, 224×224 for videos) and overlap size (16 pixels) lack theoretical or experimental justification. Additional experimental analysis of hyperparameter selection should be provided.", "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": "bJlCkDFJkU", "reviewer": "Reviewer_k3yb", "summary": "This paper introduces RelayFormer, a unified attention framework designed to address two core challenges in Visual Manipulation Localization (VML): resolution diversity and the modality gap across images and videos. The core of RelayFormer is a novel Global-Local Relay Attention (GLRA) mechanism. This mechanism partitions inputs of arbitrary resolution into fixed-size sub-images and introduces a small set of learnable \"relay\" tokens to absorb fine-grained manipulation artifacts locally before engaging in a sparse information exchange at the global level to propagate context. Furthermore, the framework incorporates a parameter-efficient design, leveraging a shared Transformer backbone with task-specific LoRA adapters to optimize efficiency with minimal impact on performance. The authors validate the framework's efficiency and performance through extensive experiments on multiple image and video benchmarks.", "questions": "1. Quantifying Relay Compression Loss:\nHow much performance degradation, if any, occurs when comparing GLRA against a full global-attention model (even at a reduced resolution)?\nSuch an experiment could empirically validate the trade-off between efficiency and precision.\n\n2. Evaluation under CAT Protocol:\nGiven that the paper currently reports results under the MVSS protocol, could the authors also train and evaluate a CAT-protocol version of RelayFormer?\n\n3. Temporal Sparsity in Manipulation Frames\nIn real-world video forgeries, only a few frames may be manipulated while the majority remain authentic.\nGiven that RelayFormer aggregates temporal information through relay tokens across frames, how effectively can the model detect and localize sparse manipulations that occur in only a small subset of frames?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. The core GLRA mechanism introduces a small set of learnable [GLR] (Global-Local Relay) tokens to act as information proxies. This design reduces the computational complexity of global attention from quadratic in the total number of patches to quadratic in the number of sub-images, achieving an exponential reduction in computational cost. The framework employs an elegant information fusion strategy, iteratively executing a two-step process of \"local perception\" and \"global relay\" within each Transformer layer. \n\n2. The paper ingeniously employs a strategy that combines a shared Transformer backbone with two independent, lightweight LoRA adapters. By dynamically switching between these adapters, the design achieves functional specialization for the distinct local and global attention tasks at a minimal additional parameter cost.", "weaknesses": "1. Information Bottleneck of Relay Tokens:\nSince global information exchange is mediated through a small set of relay tokens, this mechanism may introduce mild information loss when manipulations span multiple sub-images with complex cross-region dependencies (e.g., fine-grained copy-move forgeries).\nAlthough this trade-off is computationally justified, it would be useful to quantify how much contextual precision is sacrificed compared to a full-attention baseline.\n\n\n2. Missing CAT Protocol Comparison:\nThe image manipulation localization experiments primarily follow the MVSS protocol (training on CASIA-v2 and testing on standard benchmarks).\nHowever, stronger and more comprehensive evaluation settings, such as the CAT protocol,should also be considered.", "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)