All Patches Matter, More Patches Better: Enhance AI-Generated Image Detection via Panoptic Patch Learning
Authors: Zheng Yang
Ruoxin Chen
Zhiyuan Yan
Ke-Yue Zhang
Xinghe Fu
Shuang Wu
Xiujun Shu
Taiping Yao
Shouhong Ding
Zequn Qin
Xi Li
Venue: ICLR 2026 Poster (2026)
Keywords: Generative Models
Synthetic Image Detection
AIGC Detection
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: The rapid proliferation of AI-generated images (AIGIs) highlights the pressing demand for generalizable detection methods. In this paper, we establish two key principles for AIGI detection task through systematic analysis:
**(1) All Patches Matter**, since the uniform generation process ensures that each patch inherently contains synthetic artifacts, making every patch a valuable detection source; and
**(2) More Patches Better**, as leveraging distributed artifacts across more patches improves robustness by reducing over-reliance on specific regions.
However, counterfactual analysis uncovers a critical weakness: naively trained detectors display **Few-Patch Bias**, relying disproportionately on *minority patches*.
We identify this bias to **Lazy Learner** effect, where detectors to limited patch artifacts while neglecting distributed cues.
To address this, we propose **Panoptic Patch Learning** framework, which integrates: 
(1) *Randomized Patch Reconstruction*, injecting synthetic cues into randomly selected patches to diversify artifact recognition; 
(2) *Patch-wise Contrastive Learning*, enforcing consistent discriminative capability across patches to ensure their uniform utilization.
Extensive experiments demonstrate that PPL enhances generalization and robustness across datasets.

Published as a conference paper at ICLR 2026
ALL PATCHES MATTER, MORE PATCHES BETTER: ENHANCE AI-GENERATED IMAGE DETECTION VIA
PANOPTIC PATCH LEARNING
Zheng Yang'; Ruoxin Chen Zhiyuan Yan', Keyue Zhang",
Xinghe Fu'
Shuang Wu", Xiujun Shu', Taiping Yao", Shouhong Ding", Zequn Qin Xi Li
!College of Computer Science and Technology. Zhejiang University
Youtu Lab, Tencent Peking University *Wechat Pay, Tencent
Sccooo of Software Technology, Zhejiang University
ABSTRACT
The rapid proliferation of Al-generated images (AIGIs) highlights the pressing demand for generalizable detection methods. In this paper, we establish two key principles for AIGI detection task through systematic analysis: (1) All Patches Matter, since the uniform generation process ensures that each patch inherently contains synthetic artifacts, making every patch a valuable detection source; and (2) More Patches Better, as leveraging distributed artifacts across more patches improves robustness by reducing over-reliance on specific regions. However, coun- terfactual analysis uncovers a critical weakness: naively trained detectors display Few-Patch Bias, relying disproportionately this bias to Lazy Learner effect, where detectors to limited patch artifacts while neglecting distributed cues. To address this, we propose Panoptic Patch Learning framework, which integrates: (1) Randomized Patch Reconstruction, injecting synthetic cues into randomly selected patches to diversify artifact recognition; (2) Patch-wise Contrastive Learning, enforcing consistent discriminative capa- bility across patches to ensure their uniform utilization. Extensive experiments demonstrate that PPL enhances generalization and robustness across datasets.
minority patches. We identify
INTRODUCTION
The rapid evolution of generative Al models has precipitated an exponential growth of Al- Al-generated
images (AIGIs) in digital ecosystems (Goodfellow et al. 2014 Karras et al. 2018 2019 Ho et al.
2020; Rombach et al. 2022] Zhang et al.] 2023 Ramesh et al. 2021 Yan et al. 2025b 2024c
2025a Zhang al. [025 2024 [Song et al. 2025bla). This proliferation raises concerns regarding
information security and content authenticity, highlighting the need for AIGI detection to distinguish synthetic images from authentic ones. Unlike conventional classification tasks, AIGI detection operates as a "cat-and-mouss game", presenting unique challenges due to: (1) continuous emergence of new generative architectures, and (2) frequent updates to existing generative models. Consequently, exhaustive training all synthetic data becomes impractical (Ojha al. 2023). thus necessitating detectors with strong generalizability.
Despite these challenges, AIGIs exhibit a distinctive property absent in traditional classification: Universal Artifact Distribution. the context of AIGIs, discriminative features are not confined to object-centric regions; instead, synthetic images contain artifacts uniformly across all patches, a consequence of the consistent generation process of modern generative models [T] This indicates that every patch, defined as partitioned local sub-blocks, contains synthetic traces, forming our first principle for AIGI detection: All Patches Matter, This principle is supported by two lines of evidence: (1) visual analytics (Tan al. 2024c; Cozzolino et al., 2024] Chen et al. (025) confirm pixel-level discriminative patterns, revealing artifacts at patch granularity; and (2) recent patch-wise
detectors (Chen et al. 2024b Zhong et al. 2024) demonstrate comparable performance to full-image
Equal Contribution, f: Corresponding Author
'This work adheres to the mainstream AIGI detection setting Chen et al. 2024a Ojha et al. 2023 Tan et al.
2024a| Liu et al., (2024) He et al. 2024 Zhu et al. 2024 Tan al. 2024c Lin al. 2025 where the entire
image generated by Al models.

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Published as a conference paper at ICLR 2026
Gentmage
Charssioo
Panoptic Patch
23.3
Learning
03.9
Input
Naively Trained
PPL (Ours)
CNNSpst
Je-lsker
JrHWFD
DACT
C2PCLI
AIDE Prt. Jouns)
(a) Visualization of attention maps.
(b) Comparison on different datasets.
Figure (a) PPL produces more uniform attention distribution across patches, indicating its effectiveness capturing artifacts comprehensively. (b) PPI. outperforms peer methods on Genlmage, DRCT-2M, AIGCDetectionBenchnakk and the in-the-wild Chameleon. More details are in Section
approaches, validating the discriminative capability of individual patches. Although artifacts vary across patches, detectors that capture diverse artifacts across distributed regions reduce over-reliance on specific patches. Capturing these distributed artifacts enhances cross-generator generalizability by mitigating detectors' blind spots. This leads to our second principle: More Patches Better.
However, counterfactual analysis of existing AIGI detectors (Ojha et al. 2023 Liu et al. 2024
Tan et al., 2024a; He et al., 2024: Chen et al.) 2024a) reveals an unfavorable tendency- Fww-Feeteeee Bias supporee by two empirical observations and quantitative analysis. Empirically, observe: (1) detectors' attention maps disproportionately focus on very limited patches; (2) detectors exhibit severe patch-specific fragility, where masking single patch reduces accuracy by 18.7% + 4.1% on average. Quantitatively, using causal inference tool CDE WVanderWeele) 2013) to quantify each patch's s impact-measured as the classification logit difference with and without that patch-we find that naively trained detectors produce skewed distributions: few patches show high CDE values, while most patches exhibit significantly lower contributions. This suggests that most patches remain underutilized. despite also containing generative artifacts. Moreover, detection methods with more uniform CDE distributions exhibit stronger generalizability; for instance, DRCT, with more high-CDE patches, performs substantially better than UnivFI). We attribute such Few-Patch Bias to the propensity of detectors as Lazy Learner (Hermann et al. 2024 Zhang et al. 202 1 Wang et al. 2022 Zhao et al.) 2024 Ghosh et al.) 2023 Tang et al.) 2023 Sun al. 20244] Yuan et al. 2024 Yan et al. [2024b]. Specitically, AIGI detectors follow curriculum-like learning pattern: once
easy-to-leard artifacts in certain patches minimize loss, the presence these patches discourages exploration of broader regions.
To address this challenge, propose the principle: "All Patches Matter, More Patches Better", which prevents detectors from shortcutting to few regions and instead encourages robust feature learning across the entire image. To operationalize this principle, introduce the Panoptic Patch Learning (PPL) framework, which consists of two components: (1) Randomized Patch Reconstruction, which manually injects synthetic artifacts into randomly selected patches of real images via diffusion reconstruction. forcing the model to discriminate based on these chosen regions and discouraging over- reliance on specific patches; and (2) Patch-wise Contrastive Learning, which aligns the representations of real and synthetic patches, thereby enforcing consistent discriminative capability across all regions of the image. Fig. illustraaes the effectiveness of PPI.. Our main contributions are threefold:
We formally propose the principle "All Patches Matter, More Patches Better", showing that exploiting distributed artifacts enhances AIGI detection.
2

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Published as a conference paper at ICLR 2026
2. We provide a detailed patch-wise analysis using CDE, revealing that Few-Patch Bias is pervasive in existing detectors.
3. Building this principle, design Panoptic Patch Learning and validate its effectiveness through extensive experiments.
RELATED WORK
Existing AIGI detection methods be broadly categorized into two types: local and global (Tan et al. 2024a). We summarize both lines of research below.
detection
Local AIGI detection methods. Local approaches exploit localized information to distinguish Al-generated images from real ones, assuming that low-level feature differences exist between the two. These methods can be divided into patch-wise and pixel-wise detectors.
Patch-wise methods include: SSP (Chen et al. 2024b) achieves notable performance using only a single patch. Patcheraft (Zhong et al.) (2024) separates processing of the simplest and most complex patches by entropy-based selection. (Zheng et al. 2024) employ patch-based CNN leveraging all patches avoid selective sampling and aggregate patch features. TextureCrop (Konstantinidou et al. 2025) partitions an image via sliding windows and selects high-frequency texture-rich regions. Despite these advances, patch-wise detectors often over-rely on limited subset of patches, leading to information under-utilization. Pixel-wise methods include: NPR (Tan et al. 2024c) detects AIGIs by analyzing differences in neighboring pixel relationships. FreqNet (Tan al.] 2024b) and SAFE (L) et al. 2024) exploit high-freen feggfrreyencc signals to capture localized patterns. However, pixel-wise methods are sensitive to small perturbations in pixel relationships, limiting their robustness.
Global AIGI detection methods. Global approaches leverage holistic image characteristics to distinguish AIGIs from real images, aiming to capture inconsistencies that may not be observable at the local level. CNNSpot (Wang et al. (020) applies CNN directly for detection, achieving strong in- distribution performance but suffering from poor cross-generator generalization. UnivED (Ojha et al. 2023) improves robustness by adopting a CLIP visual encoder as feature extractor. FatFormer (Liu et al. 2024) further adapts CLIP by introducing a frequency adapter. C2P-CLIP (Tan et al. 2024a) tine-tunes CLIP with carefully designed image-text pairs to embed the notions of "real" and "fake." DRCT (Chen et al. 2024a) strengthens UnivED with a contrastive loss on hard cases. Nevertheless, global methods often overlook fine-grained forensic artifacts, which constrains their effectiveness.
3
MOTIVATION
3.1 ALL PATCHES MATTER, MORE PATCHES BETTER
The principle of All Patches Malter is supported by three key findings.
1. Principle: Because every patch of synthetic image is itself generated, each inherently contains artifacts. Localized detection methods (Chen et al.) 2024b Zhong et al.) 2024) demonstrate that cues within small regions can effectively discriminate real from synthetic content, underscoring that every patch carries discriminative signals.
2. Visualization: Fig. 2 illustrates distinct artifact patterns across patches, showing that each
synthetic patch exhibits identifiable features distinguishing it from real patches. Moreover, the variability of these cues across patches highlights the considerable diversity of artifacts present in synthetic images.
3. Experiments: We further validated this principle by evaluating detectors on single randomly selected patches. By replicating one patch across the image to isolate its features, detectors still achieved 90% accuracy on the SDv1.4 1.4 subset of Genlmage. This confirms that even single patch contains sufficient information for reliable discrimination.
Together, these findings demonstrate that artifacts in synthetic images are both pervasive and diverse. Detectors exploit these patch-level cues, motivating the principle of More Patches Better: lever- aging more patches enhances robustness and generalization by capturing complementary artifact patterns. However, observations reveal that existing detectors do not align with this principle.
3

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Published as a conference paper at ICLR 2026
b. Broken Line
Original Real
Nolsy
Reconstrueted Fake
Visual evidence of patch-wise artifacts. We observe diverse traces- vrcces-such as broken lines, unnatural noise, and boundary detail loss showing that multiple regions of synthetic images contain cues. This observation underscores the importance of leveraging more patches to enhance recognition of diverse artifacts. Images sourced from MSCOCO (Lin et al. 2014
Figure 2:
Univfd (CLIP)
Input
CLIP Lora
DINOv2
DINOv2
Lora
Median Impact
Impact Range
Recail Rate (%)
75
70
Masked Patch Size
(a) Visualization of Attention Maps.
(b) Impact of Patch Removal on Recall Rate.
Figure 3: (a) Attention maps reveal the few-patch bias of naively trained detectors, where attention concentrates on a small number of dominant patches, reflecting over-reliance on limited regions. (b) Recall degradation occurs when single patches of varying sizes are occluded, showing that detectors are overly sensitive to corruption in specific regions and suffer notable performance drops.
3.2 FEW-PATCH BIAS
Observations. Our empirical observations indicate that existing detectors often overly rely on limited number of patches. Our experiments reveal that existing detectors tend to over-rely on a limited set of patches. Fig. (ia) shows attention maps from naively trained ViTs, where attention weights concentrate on only few regions. This phenomenon persists even when changing the backbone or applying LoRA, suggesting a model-agnostic bias. further validate this observation, we systematically mask patches of varying sizes and measured the corresponding recall rate degradation. Fig. B(b) illustrates the performance UnivED under patch occlusion. Masking a single patch Ieads to substantial drop in accuracy, and the impact varies across different patches, confirming that detectors are disproportionately sensitive to specific regions.
Quantitative analysis. Building on the above observations, employ the Controlled Direct Effect (CDE) to quantify the impact of each patch. Conceptually, if both X + Y and Z + Y. then the outcome results from the combined influence of X and Z. The CDE measures the contribution of X by comparing outcomes with and without its effect while keeping other factors fixed.
For an image, the CDE of the patch at row i, column defined as:
CDF 51 81-(6.)) 8 = logitsynth logit
(1)
4

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MoMMra
Impul:
Morgan
UnivFD
DRCT
Braaingg
PPL(Ours)
Figure 4: CDE heatmap of existing methods generated images selected from the DRCT-2M dataset (Chen et al. 2024a). A broader and more uniform highlighted region indicates greater number of patches contributing determining fake image.
where / denotes the original image and / (i, the image with the (i,j)-th patch masked (imple- mented by setting the patch values to zero). By computing the CDE for each patch, we quantify its relative contribution the synthetic classification decision.
Fig. presents CDE heatmaps. From top to bottom, the number of active patches increases and the CDE distributions become more uniform. Stronger detectors consistently activate broader set of patches. These visualizations highlight the prevailing bias toward few dominant patches with disproportionately high CDE, motivating us to mitigate few-patch reliance.
METHODOLOGY
Panoptic Patch Learning is comprehensive framework based on the principles of "All Patches Matter" and "More Patches Better," achieved through innovative data and learning strategies, as illustrated Fig. Specifically, the data strategy, Randomized Patch Reconstruction (RPR), discourages the model from over-relying on any specific patches, thereby enhancing its recognition capability for various artifacts across more patches. Following this, the learning strategy, Patch-wise Contrastive Learning (PCL), ensures that all patches, both frequently attended and underutilized, are brought closer in the feature space, thereby uniformizing the impact of all patches.
Randomized Patch Reconstruction encourages "More Patches Better". The RPR process is carried out by performing diffusion reconstruction on randomly selected patches with a specified proportion, injecting synthetic cues into specific regions of the image while maintaining the overall semantics of the image (as the reconstructed image closely resembles the original image). In practice, RPR is implemented by first applying diffusion reconstruction to the entire image to obtain a reconstructed version. Then, the selected patches in the original image are replaced with their reconstructed counterparts, resulting in synthetic image where only specific regions contain synthetic artifacts. Here, we emphasize that we inject synthetic features via diffusion reconstruction rather than stitching synthetic patch, in order preserve the global semantics and integration of the produced image, and to prevent the model from overfitting to images with disconnected semantics. We use E 1.0] to denote the ratio of reconstructed patches relative the whole image.

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Published as a conference paper at ICLR 2026
Old Dominant Palch Out
New Dominont Patchj
More Uniterrm Distribution
Patch
ITZ
Patch Lovel Aligning
Mixing
Training
Embed
Training
More Patches Leveraged by RPR
All Patches be Uniform by PCL
dominant patch
non-dominant patch
Figure 5: The Panoptic Patch Learning (PPL) framework embodies the principles of All Patches Matter and More Patches Better through two key components: Randomized Patch Reconstruction (RPR) and Patch-wise Contrastive Learning (PCL). During training, the model may excessively rely on dominant patches, neglecting others. RPR mitigates this by randomly replacing dominant patches with real ones, encouraging the model to detect artifacts in non-dominant patches and thereby expanding the coverage of dominant regions. PCL further promotes balanced patch utilization by aligning the embeddings of patches with the same labels. Together, RPR and PCL foster comprehen- sive and uniform exploitation of patches.
Algorithm 1 Patch- wise Contrastive Learning (PCL) Training Procedure
Input:
X input image tensor after RPR, shape [B, C, H. W] labelgt image-level ground-trute labels, shape [B]
pulchgt gt patch-level ground-truth labels, shape [B, K]
weighting coefficient for contrastive loss
1: imgenbedding: patchembeddding ViT Encoder (X) & imggmbeddinng-
patcheendddbi
[B, K,
2: Upred &- Linear(imgembedder (imgembedtiing)
Image-level class logits, shape [B. 2]
3: Lee < RICELoss(yyree) labelgt)
D Image-level classification loss > Patch-level contrastive loss
4: Leon &- connraattveesssppaeeeeeeeee
patchg:)
5: Ltotel + A Leon + X) Lee
6: Lottal backkard()
Patch-wise Contrastive Learning emphasizes "All Patches Matter". PCL operationalizes the principle of "All Patches Matter" by aligning the embedding vectors of different patches, bringing patches with identical labels closer together while distancing those with different labels. We employ contrastive learning to cluster synthetic patches more closely within each batch, while maintaining margin that separates synthetic and real patches. This approach ensures that if an image contains any dominant patch with easily learnable artifacts, the model improves performance on the remaining patches, thus promoting the utilization of all patches. Specifically, for each batch, utilize margin-basee contrastive loss (Hadsell et al. 2006)::
Loon
Σ A.j: (
max (0, -d)],
where i,j represent the indices of patch tokens within a batch. d measures the Euclidean distance between the patch embeddings. defines minimum distance threshold between negative sample pairs, thereby enhancing the model's ability to distinguish between similar and dissimilar pairs. Y indicates whether two patches share identical labels, thus pulling positive patch pairs closer and pushing negative patch pairs further apart. The overall loss function is weighted combination of the cross-entropy loss and the patch-wis wise contrastive loss:
Luval XLeu + (1 X)
The practical implementation of PCL is shown in Alg. 1
5
EXPERIMENTS
Implementation details. We adopt CLIP (Radford et al. 202T and DINOv2 (Oquab et al. 2023) two vision foundation model backbones and fine-tune them using LoRA. Unless otherwise

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Published as a conference paper at ICLR 2026
Table 1: Cross-model accuracy (Acc) on GenImage. All methods are trained on the SDv1.4 subset. Results are taken from C2P-CLIP (Tan et al. (0244)) except SAFE and Effort, which are reported in their original papers. For Breaking (Zheng et al. 2024), we re-implement the method because no Genlmage results or checkpoints are publicly available. Our results are bolded when they achieve the highest accuracy among all methods.
Midjoumey
Wakong
VQDM
BigGAN
RosNer-90 He ml. 2016
CVPR20066
34,9
59.9
99.7
53.5
61.9
98.2
36.6
52.0
72.
22.6
DeiT-S
Touvron al
2014
ICML2021
55.6
99.9
99.8
19.8
58.1
95.9
56.9
53.5
71.6
23.2
ICCV2021
CNNSppr Waing 2c
2020
CVPR2020
52.8
96.3
95.9
50.1
39.8
78.5
53.4
46.8
64.2
22,5
Spec
(harny ol 2019
WIFS2019
52.0
99.4
99.2
49.7
49,8
94.8
55.6
49.8
68.8
24.1
F3Ner
GramNet eL el 2020
CVPR2020
54.2
99.2
99.1
50.3
54.6
98.9
50.8
51.7
69.9
24.2
UnivED Oflli
CVPR2022
93.9
96.4
96.2
71.9
$5.4
94.3
81.
90.5
88.8
1: 8.6
NPR Tub
CVPR2024
81.0
98.2
97.9
76.9
96.9
84.1
84.2
88.6 8.3
89.6
98.6
86.3
75.8
11.6
FalFormet (L Fi PAPG
CVPR2024
92.7
100,0
99.9
75.9
88.0
99.9
98.8
55.8
88.9
15.7
DRCT
ICML 2024
91.5
V5.0
94.4
79.4
89.1
944
81.6
89 + 5.9
Tairete
Breaking (Zheno drale
NIPS2024
83.9
98.9
93.0
99.1
97.7
85.4
92.7
90.5
92.7 5.8
SAFF (1. 2024
KDD2022
95.3
99.4
99.3
82.1
96.3
98. 2
96.3
97.
95.6
5.6
C2P CPP-CEPPPT
2024m
AAAI2025
88.2
97.9
96.4
98.8
96.5
98.7
95.8
4.0
OunDDINOV2
90.4
98.2
91.7
91.3
96.3
98.0
97.7
56.2
95.9
Ous/CIII
94.8
98.5
98.3
94.7
96.1
98.6
98.5
98.0
97.2 + 1.7
Table 2: Cross-model accuracy (Ace) DRCT-2M. All methods are trained on the SDv1.4 subset. Results of other methods are taken from DRCT (Chen et al. 2024a).
Vanuurs
ControlNet Virtatie
VaTurro
S0x1A
STYU
LCM
SDK
str/.
SE/U)
SEN-:
2013-11
CNNS Blaz
93.01
57.55
96.55
73.42
61.32
60.93
51.41
FIN Qunert 2000
83
95.33 1.77
99.79
85.05
55.05
47.57
08.29
63.00
07. 5959
54.58
077 00
13.39
81 1999
65.43
90.3
50.37
77.13
= 18.1
CLIPNNN Penteeee
00
99.99
91.61
62.08
91.43
83.57
64.00
57:43
$0.69
65:83
40.67
$0.47
50.05
18.3
Delake
92.10
95.53
09.51
83.55
03.03
10.59
50.12
Cars-B
99.97
100.0
m7
05:84
64.44
82.00
80:83
00.75
99 37
63.13
99 m0
$3.80
73.25
61.65
51 70
50.41
79.11
18.
93:83
91.01
01.91
85.93
18.09
81:06
50.05
83.86
17.
Ous/CLIP
09.70
99.20
99
99.67
99.71
90.40
99.48
99.40
99.62
99.30
99.66
99.64
99.51
99.63
99.67
97.80
99.99
specified, in our proposed Panoptic Patch Learning (PPL), image reconstruction is performed with SDv1.4 inpainting at generation strength of s 0.25. The inpainting pipeline uses step 50 and guidance scale 7.5. During training, images are randomly cropped to 224 224, while at test time they center-cropped to the same resolution. For the randomized patch reconstruction module, the reconstruction patch size is set to x 14, consistent with the patch size of VIT. Each fake image in the original training set has probability of Prpr 0.9 of being replaced with RPR image, where Trpr = 50% of patches from a real image are randomly selected to do diffusion reconstruction. For patch-wise contrastive learning, the weight of the contrastive loss is set to X 0.3, with margin parameter a 1.0.
Peer methods. The compared methods involve ResNet-50 (He al.) 2022).
2016). Conv-B (Liu et al. (Li et al. (021). SAFE (L)
Swin-T (Liu et al. (021). ) CNNSpot (Wang et al.) 2020). F3Net
al. 2024). UnivFD (Ojha al. 2023). FatFormer (Liu al. 2024 DRCT (Chen et al. 2024a).
CLIP (Tan et al. 2024a). Ellort Yan et al. 2024b).
5.1
COMPARISON WITH OTHER METHODS
Comparison on GenImage. Tab. Tab. compares PPL with other methods Genlmage. We observe: (I) PPL consistently achieves higher accuracy across different backbones. (2) The standard deviation of PPL's accuracy is smaller, indicating improved stability in detecting diverse generative models.
Comparison on DRCT-2M. Tab. reports results on DRCT-2M. The results indicate: (1) PPL consistently achieves SoTA with the lowest std, demonstrating both effectiveness and stability. (2) While DRCT shows relatively poor performance on SDXL-related subsets, PPL maintains a more balanced performance across diverse subsets, underscoring its robustness.
7

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Published as a conference paper at ICLR 2026
Table 3: Cross-dataset and cross-model accuracy (mAcc) on AIGCDotcctionBnnhhnaak. PPL is
trained on the Genlmage SDv1.4 4 subset due to its reliance on diffusion- based reconstruction. Baseline methods are trained on ProGAN data provided by AIGCDetectionBenchmak, which more in- distribution with the test set, thereby giving them an inherent advantage under this setting. Baseline results are taken from AIDE (aan et al. 2024ap.
Trotect
59:35
78.33
11:07
76.77
10.57
66.19
10.73
63.43
5413
15.87
18.39
18.21
11.00
45.30
14:00
(410
(0000
85:30
87:30
7730
$5:30
06.38
49.00
$2.33
11:30
51.00
$3.10
0933
91.85
63.52
96344
53.23
13.66
33-22
13:10
07:33
06.11
60:33
03:22
00.00
12.55
25.23
83.42
75.44 15.
B
05:31 11
8181
11.1
70.14
15
6770
76.31
18
1534
71.36
15.01
08:31
19.3
13.08
51.06
66:15
(8166 1100
10030
02:71
70.12
$3.59
90.33
1330
12.33
$333
13.35
19.30
18.00
21.11
75.45
14.00
23443
13.07
11.30
23.38
12.33
19.29
02.77
VP
43330
64.35
18:11
3177
1159
20712
5910
9731
8510
3711
197m
1111
15
1110
Table 4: Cross-dataset and cross- accuracy (mAcc) on the UniversalFakeDDeteet. PPL is trained on the SDv1.4 subset of Genlmage, while other methods are trained on GAN. Baseline results are taken from C2P-CLIP (Tan et al., 2024a).
GAN
LDM
GLIDE
Mahhale
Ref
Pro-
Cydle-
Bie-
Siste-
GaB-
Star-
Guidee
200
200
1.00
1.00
.00
100
DALLE
GAN
GAN
DANN
strps
wicfg
-27
1.0
CNN-Spe
CVPRESTO
99:99
85.30
78.35
V1:30
00:00
54.14
16.775
14,90
Patshie
ECCV2020
75.03
63:97
68.47
63.04
67.41
76.50
76.10
75.77
74.81
73.28
63.52
67.91
Coveccurence
Blect Imag.
07:00
63:15
00:50
70.20
70.35
71.00
70.25
09:00
69:90)
67.55
12.68
Ppeg
BCCV2020
99.38
76.38
65.33
100.00
69.20
75.35
68.50
81.65
83.05
66.30
12.47
UD
CVER2023
100.00
95.50
94.50
97.00
70,03
94.19
94.36
79.07
79.55
78.14
56.78
$7.69
L.Grad
CVPIC3025
99.84
85.30
$2.88
94.81
72.45
99.62
77.50
94.33 14.30
05.88
04.30
67.40
90.70
89.55
65.35
00.53
7.70
FragNct
AAA12024
97.30
95.84
50.45
97.55
90.34
93.41
86.70
34.55
09.58
65.56
85.59
97.40
88.15
59.06
85.01 = 11.56
NPK
CVPR23024
99.56
95.00
$7.55
96.23
$6.57
99.75
$4.55
97.65
98.00
90.20
96.25
97.15
97.35
$7.15
94,37 5: 5.19
Vaiteemer
CVP122124
99 59
99. 32
59 $9.50
97-15
99.41
99.75
76.
98.08 60
94.90
59.65
94.35
94.05
54.30
98.75
96.88 3.95
CIP-CLIP
AAA22025
99.98
07.31
99.12
96.44
99.17
99 69
69. 10
95.25
97.25
99.30
05.25
05:25
96.10
98.55
05.83
7.57
OUIDDINOV2
06.94
89.44
94.75
94.27
93.99
$9.99
92. 30
99.00 80
59.4 40
99.40
98.35
95.60
08.5
99.40
96.05 3: 3.58
Ous/LIP
89.12
97.16
$3.57
89.94
75.29
97.12
94.55
98.90
98.80
90.80
96,90
97.40
97.65
98.80
93.85 6.78
Table 5: Cross-dataset accuracy (Acc) on the in-the-wild dataset Chameleon. Results of other methods
are directly taken from AIDE (Yan et al.) 2024a). For each training dataset, the first row reports the overall Acc on the Chameleon test set, while the second row presents Acc separately for fake images and real images for detailed analysis.
Training Dataset
CNNSpot
FreDect
Fusing
UnivED
DIRE
PatchCraft
NPR
AIDE
Ous/CLIP
Ours/INN022
60.11
56.86
57,07
55,62
59.71
56.32
58.13
62:601
63.94
66.63
SD
8 8698866
1,3998.57
0.00999.66 96
17,69/3350
L88695667
3,07/96335
2,4/1000000
20.3384,38
17,20/99.01
64 6568888.12
60,89
57.22
$7.09
60.42
$7.83
55.70
37.81
65.77
69.35
72,07
Ganlinage
86-999992
0.8999555
0.02/9.98
85
35.52/41.56
2.09/99.33
1.3996.52
1.68/100.00
26 26980955
38.93/92118
68.8800
Tab. 3 and Tab. 4 present results on AlGCDeteelBennhmarkk (Zhong et al. 2024) and UniversalFakeDeteer (Ojha et al. [023) respectively. The results of baseline methods are taken from (Yan et al., 2024a) and (Tan et al. 2024a). We observe that PPL, when trained solely on diffusion- -eruuiinn-geerrteed data, generalizes effectively to detecting GAN-generated images even surpassing baseline methods trained directly on GAN data- high--highlggtting PPL's strong generalization capability.
Comparison on AlGCDetecBBucchmakkk and UniversalFake
Comparison on the in-the-wild dataset Chameleon. Tab. reports results on Chameleon, challenging dataset comprising diverse images collected from online websites. We observe that most existing methods only marginally exceed the accuracy of random guessing (50%). In contrast, PPL achieves 70% accuracy on Chameleon, demonstrating strong generalization on real-world data.
5.2 ROBUSTNESS STUDIES
We conduct robustness experiments on GenImage to evaluate the reliability of our method under common image corruptions. As shown in Fig. 66, both CLIP and DINOv2 backbones sustain high accuracy even under severe JPEG compression and strong Gaussian blur, demonstrating the robustness of our approach.
5.3 ABLATION STUDIES
Ablation on the hyperparameters. Fig. reporrs the influence of key hyperparameters in PPL. The results suggest four main observations: (1) PPL achieves peak accuracy at À = 0.3. (2) PPL. is
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FatFormer
PPL(CUP)
100
100
100
Balanced Acc (%)
Balanced Acc
60
60
Balanced
60
40
20 100
90
80 70
60
29.50
0.75
1.00 1.25
1.50
20
2
Blurring Sigma Figure 6: Robustness to image corruptions on Genlmage Dataset. Performance is evaluated under JPEG compression (quality factor Q (100, 90, 80, 70, 60)), Gaussian blur (standard deviation E {0.0, 1.0, 2.0, 3.0)), and resizing (scaling factor S E {0.5,04 0.75, 1.0, 1.25, 1.5))-
JPEG Quality Factor
Resizing Scale
CLP
CLIP
CLP
CLP
D190
DIVO
CINO
2IND
Arcurscy (5)
0.2
0.3
0.5
0.7
0.5
6.5
0.3
0.5
0.7
2.0
10
20
30
73
80
0.25
0.5
0.75
1.0
(aw(%)
61%)
Figure 7: Ablation study on hyperparameters: À representing the weight of contrastive loss. Prpr representing the probability of replacing the fake images with RPR images during training. Trpr representing the ratio of reconstructed patches, and representing the strength of reconstruction.
relatively robust Prpr. the probability of replacing fake images with RPR images during training. (3) PPL is sensitive to the patch reconstruction ratio Typr, where accuracy degrades at excessively high ratios, with the best performance obtained around Frpr 50%. (4) Smaller reconstruction strength not only improves accuracy but also reduces computational cost, making it preferable in practice.
Ablation the impact of each module. Fig. g
Reconstruction
highlights the contributions of Randomized Patch
Reconstruction (RPR) and Patch-wise Contrastive Learning (PCL... We compare two strategies for injecting synthetic artifacts into real images: (1)
with
92.6
diffusion-based reconstruction (blue), and (2) random patch replacement from original synthetic images (red). The results show that reconstruction serves as more effective mechanism for introducing syn- thetic cues to guide the model. While either RPR PCL alone enhances performance, their combination yields markedly stronger improvements. Additional ablation studies are provided the Appendix due to space constraints.
Accuracy
90.0
88.77
91.2
80.5
90.1
90.5
87.5
85.0
82.5
80.0
LoRRA
+RPR
+PCL
+RPR+
Figure 8: Ablation study on each module.
CONCLUSION
Our work is based on the nature of the AIGI detection problem, which can be concluded as "All Patches Matter, More Patches Better." However, our observations indicate that existing detectors are unable to fully take advantage all patches in an Al-generated image. To address this issue, we propose randomized patch reconstruction augmentation combined with patch-wise contrastive learning strategy. This approach effectively prevents the model from becoming a lazy learner and enhances the utilization of every patch. We achieve state-of-the toee----ttht-tttt- performance several well- known academic datasets across various benchmark datasets. The outstanding performance achieved in both settings supports findings and proves the efficacy of the proposed learning framework.

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ACKNOWLEDGMENTS
This work was supported in part by the National Science Foundation for Distinguished Young Scholars under Grant 62225605, "Pioneer" and "Leading Goose" R&D Program of Zhejiang (00,0020022,4 Ningbo Science and Technology Special Projects under Grant No. No.20252028, Project 12326608 supported by NSFC, and the Fundamental Research Funds for the Central Universities.
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This appendix provides supplementary details and additional analyses. Section describes the implementation setup; Section[B reports more ablation studies; and Section presents a comparative CDE analysis with both statistical and visual demonstrations.
A IMPLEMENTATION DETAILS
Implementation of RPR. In Randomized Patch Reconstruction (RPR), diffusionn-reconsttuuteedd real images are replaced with their original counterparts on a patch-wise basis. For consistency, we use Stable Diffusion vI (SDv1) the reconstruction model and reconstruct real images in the training subset of SDv1.4 from both GenImage and DRCT. The reconstruction is performed with the inpainting pipeline (50 denoising steps, guidance scale 7.5) using an empty prompt, zero-filled mask of the same size as the input image, and the original image as inputs.
Implementation of PCL. Patch-wise Contrastive Learning (PCL) introduces moderate computa- tional overhead. Training LoRA model with only binary cross-entropy (BCE) loss on CLip-Large requires 16 GB of GPU memory, while incorporating the margin- based contrastive loss increases memory usage to 19 GB. Similarly, training one epoch on Genlmage with an NVIDIA V100 GPU increases the runtime from hours.
B ADDITIONAL ABLATION STUDIES
investigate the impact of different contrastive losses applied to embedded patch tokens. Two widely used losses considered: InfoNCE and the margin-basee based contrastive loss. InfoNCE maximizes similarity between positive pairs while minimizing for negative pairs, typically formulated as:
exp(q k+/T)
Lg - log En explg k./T)
where 9 and k4 denote the embeddings of the sample and its positive counterpart, and temperature parameter. InfoNCE requires computing similarities between each sample and all negative samples, resulting in a computational complexity that grows quadratically with batch size.
In contrast, the margin- based contrastive loss, constrains the Euclidean distance between sample pairs by pulling positive pairs closer and pushing negative pairs apart with a margin a:
Lcontrastion
Σ [Y .d + (1- max(0, -dfy]
(5)
where d, = |/Embjus per Embjal 12 is the Euclidean distance between embedded patch tokens, and Y an indicator function that equals 1 if the pair shares the same label and 0 otherwise. This loss only penalizes negative pairs whose distance is less than the margin, thus avoiding computations over all negative pairs and reducing the overall computational cost. The di could also the cosine distance, which is also compared in our experiments.
Impact of loss function. Tab. fillustrate the effectiveness of these loss functions. Unless otherwise specified, all experiments reported in the appendix are conducted on the GenImage dataset, and performance is measured using mean accuracy (mAce).
Impact of randomized patch reconstruction vs. fixed position reconstruction. Our randomized patch reconstruction method employs random selection process for image reconstruction, allowing fake patches to appear in different regions across the entire image. Alternatively, replacing patches at fixed positions also yields images composed of both real and synthetic elements. Tab. 7 iillustrates the effectiveness of randomized patch reconstruction compared to fixed -osition reconstruction, both of which leverage patch-wise contrastive learning.
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Table 6: The impact of contrastive loss choice.
Loss
Midjourney
SDv1.4
SDv1.5
ADM
GLIDE
Wukong
VQDM
BigGAN
mAcc
Infonce/r=0.5
93.1
99.4
99.5
89.8
96.0
99.5
99.4
89.6
95.8
Margin/cosine
92.8
99.7
99.5
82.3
91.5
99.7
91.5
85.6
92.8
Margin/euclidean
94.8
98.5
98.3
94.7
96.1
98.6
98.5
98.0
97.2
Table 7: The impact of random patch replacement vs. fixed position replacement.
Position
Midjourney
SDv1.4
SDv1.5
ADM
GLIDE
Wukong
VQDM
BigGAN
mAcc
Upper Half
87.1
99.7
99.6
83.9
99.7
99.2
96.0
95.0
Lower Half
87.5
99.7
99.6
99.7
99.1
94.6
Left Half
87.7
99.8
99.5
84,4
93.8
99.7
99.0
90.6
94.3
Right Half
81.6
99.8
99.7
74.8
79.6
99.8
98.7
86.2
90.0
Random
94.8
98.5
98.3
94.7
96.1
98.6
98.5
98.0
97.2
Table 8: The impact of patch size of random patch replacement.
Patch Size
Midjourney
SDv1.4
SDv1.5
ADM
GLIDE
Wukong
VQDM
BigGAN
mAce
112
99.6
99.3
92.3
92.7
99.5
95.8
99.5
94.3
96.6
56
99.2
93.8
90.8
99.4
95.8
99.1
97.2
28
98.7
98.3
95.7
95.4
98.8
97.5
98.5
94.8
98.5
98.3
94.7
96.1
98.6
98.5
98.0
97.2
Table 9: The impact of random patch replacement vs. random patch dropout.
Dropout Rate
Midjoumey
SDv1.4
SDv1.5
ADM
GLIDE
Wukong
VQDM
BigGAN
mAce
0.10
71.3
99.9
99.8
67.9
82.7
99.8
97.3
77.0
88.5
0.15
94.3
98.7
98.6
87.9
90.4
98.7
98.5
90.9
94.2
0.20
77.0
99.9
99.8
69.2
72.0
99.8
98.7
87.7
89.1
0.25
77.2
99.9
99.8
71.1
74.7
99.8
98.6
89.3
90.2
Replacement
94.8
98.5
98.3
94.7
96.1
98.6
98.5
98.0
97.2
Impact of patch size of randomized patch reconstruction. Our randomized patch reconstruction method reconstructs real images into fake counterparts where the patch size may influence perfor- mance. with patch size potentially influencing performance. Using patches of size 14 x 14 is intuitive, as it aligns with the token size used Vision Transformers (ViTs). Tab. T iillustrate that the training process is not sensitive to reconstruction patch size, and smaller patch sizes are preferred.
Impact of randomized patch reconstruction random patch dropout. Our randomized patch reconstruction method involves substituting fake image patches with their real counterparts, thereby compelling the model exploit
artifacts from the remaining patches. An alternative approach shown in Fig. 9 is random patch dropout, which certain patches are removed, resulting in images with fewer patches. Tab. illustrates the effectiveness of Randomized Patch Reconstruction, by comparing patch reconstruction with patch dropout at various dropout rates, with both methods employing patch-wise contrastive learning. The results indicate that, with an appropriate dropout ratio, patch dropout also achieves favorable performance, supporting our hypothesis that models tend to over-rely on a subset of patches. Dropout thus serves as remedy for this issue. However, patch dropout underperforms patch reconstruction, possibly because patch reconstruction preserves the overall appearance and input domain of the image (i.e., complete image rather than masked one), thereby increasing the task's difficulty.
C MORE VISUAL COMPARATIVE ANALYSIS
CDE distribution of different subsets of GenImage. To better analyze the models' ability to leverage all patches from image, CDE to count the contribution of patch which can be
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a) Input Image
b) Image with Random Mask
c) Input Image with RPR
Figure 9: Visual comparison between random patch dropout (masking) and reconstruction, is evident that, by reconstruction, the overall visual appearance remains unchanged.
Ours
Ous
Ous
Cur
(a) SDv1.4
(b) VQDM
(c) BigGAN
(d) Glide
Figure 10: CDE distribution on different generators of ours and UnivFD.
defined as the difference in logits at the (i.j) position of an image before and after being masked. Fig. [10illustrate the CDE distribution of UnivED and our method. For better statistical analysis, we normalize the CDE values normalization facilitates the measurement of differences between less dominant patches and the most dominant patches in the images. The figure demonstrates that greater number of patches from our method are more uniform.
range (0, 1] using the exponential function (D -DD This
Visual showcase of CDE distribution of different subsets on GenImage. To better showcase our model's better ability leverage all patches from an image, we present visual analysis of CDE across various subsets of the Genlmage dataset. The Genlmage dataset is divided into multiple subsets, each representing distinct image generation methods. These subsets include GAN-base models such as BigGAN, and diffusion- models, including Stable Diffusion, VQDM, and ADM. Due to space limitations in the main text, we showcased limited images; here, we present most subset models of GenImage: the diffusion-based Stable Diffusion v v1.4 (Fig. [T], the closed-source Midjourney (Fig. [2), and the GAN-base BigGAN (Fig. 13). The rest diffusion- model are from Fig. 14 to Fig. 16 We use CLIP as backbone for our visualization.
Visual showcase of GradCAM on GenImage. To provide more intuitive understanding of how our method alters the model's decision-malee process, we employ Grad-CAX: visualize the attention maps of the baseline (naive LoR A-tuning) versus our proposed PPI. using a CLIP backbone. As illustrated Fig. 17 the baseline model tends to overfit to sparse, highly localized regions while ignoring the rest of the image. This confirms the prevalence of "Few-Patch Bias" in standard training. In contrast, our method demonstrates a significantly broader spatial coverage, attending to a diverse range of forgery traces across the entire image.
To further investigate the universality of these biases, we extend our visualization to CNN-based architectures. We analyze the activation maps of ConvNeXt- detector with specific focus on the evolution within the deepest blocks (Stage 3, Block 0,1,2) which are most closely related to decision-making according (Zeiler & Fergus 2014). Fig. T8 shows that "aa'Fe-PPac Bias' is not
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Published as a conference paper at ICLR 2026
exclusive ViTs but is a fundamental characteristic of the decision layers in modern CNNs, further justifying the necessity of our proposed learning strategy.
D THE USE OF LARGE LANGUAGE MODELS (LLMS)
In this paper, we only use the large language model to help polish our text. The large language model has no role in the research conception.
Input
UnivFD
DRCT
Ours
Figure 11: Showcase of CDE map on SDv1.4. Images are sourced from Genlmage
Zhu al. 2024).
Input
UnivFD
DRCT
Ours
Figure 12: Showcase of CDE map on Midjourney. Images are sourced from GenImage (hu et al. 2024
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Input:
UnivED
DRCT
Ours
Figure 13: Showcase of CDE map on BigGAN. Images sourced from Genlmage (Zhu et al. 2024).
Imput
UnivFD
DRCT
Ours
Figure 14: Showcase of CDE map on ADM. Images sourced from Genlmage Zhu et al. 2024
IOAK
Input
IDAK
UnivFD
IDAK
DRCT
OAKK
Ours
Figure 15: Showcase of CDE map on Glide. Images are sourced from GenImage (Zhu et al. 2024
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SHIDIL FAFE aDevas
Input
UnivFD
DRCT
Ours
Figure 16: Showcase of CDE map on VQDM. Images are sourced from Genlmage Zhu al. 2024).
Input
PPL
w/o PPL
Input
w PPL
w/o PPL
Figure 17: Showcase GradCAM map of PPL. vs naive LoRA-tuning on GenImage. Images are sourced from GenImage (Zuu al. 2024).
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Published as a conference paper at ICLR 2026
stagon3 Block
stagod blocks
stages3 blocks
stages blocks 0
stagos Nlocks
stagos hleeks.
Figure 18: Showcase of GradCAM map on last blocks of ConvNeXt. Images are sourced from GenImage (Zhu et al. 2024).
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Reviews:
[{"review_id": "pc5dVZ1IhM", "reviewer": "Reviewer_LYB1", "summary": "This paper proposes two principles for AI-generated image (AIGI) detection: \"All Patches Matter\"  and \"More Patches Better\". The authors contend that existing detectors suffer from \"Few-Patch Bias,\" relying excessively on a minimal number of highly discriminative patches. To address this issue, they propose the Panoptic Patch Learning (PPL) framework, comprising Randomized Patch Reconstruction (RPR) and Patch-wise Contrastive Learning (PCL). The method achieves superior cross-model generalization performance.", "questions": "1. In Section 5, the citation of SAFE is incorrect.\n2. In Table 6, the row labeled “Infonce/tau=0.5” is not aligned with the notation used in the main text.\n3. In Section 3.1, the text uses “donot,” which is a typographical error. It should be “do not.”\n4. In Algorithm 1, the classification loss is denoted as ( L_{ce} ) in Step 3, but it appears as ( L_{bce} ) in Step 5 when forming the total loss. \n5. The terminology for the proposed bias is inconsistent. The Introduction (in the italicized part) uses “Few Patch Bias,” while other parts of the paper use “Few-Patch Bias.”", "limitations": "", "rating": 2, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "- The methodology is well-aligned with the motivation: RPR corresponds to the principle of \"More Patches Better,\" while PCL reflects the idea that \"All Patches Matter.\" Both methods are clearly motivated and logically consistent with the proposed principles.\n- The experiments are comprehensive: performance is reported on benchmarks such as GenImage, DRCT-2M, and Chameleon, and robustness under various corruptions (e.g., compression and blur) is demonstrated. The study also includes ablation studies and hyperparameter analysis.", "weaknesses": "- The paper presents “All Patches Matter / More Patches Better” as a primary principle, but ideas such as “any local patch of an AI-generated image contains artifacts, and even a single patch can be sufficient for reliable discrimination” have already been explored in prior patch-based detection work discussed in the related work section. This makes the proposed patch-level principles feel incremental rather than conceptually new.\n- Similarly, although the proposed PPL framework is shown to be effective, it is essentially an engineering-level refinement rather than a fundamentally new paradigm. The core idea of RPR is to move the DRCT-style diffusion reconstruction from the image level down to the patch level, while PCL is a straightforward application of supervised contrastive learning at the patch-token level.\n- The paper relies on TDE for attribution. However, TDE does not appear to be a commonly adopted attribution method in deep learning. Why not use more standard interpretability approaches, such as Grad-CAM and its variants, or LRP (Layer-wise Relevance Propagation) [1], to explain what the detector is actually using?\n- The paper focuses primarily on CLIP, DINOv2, and other ViT-style backbones, while largely ignoring CNN-based detectors. This raises an important question: do the core principles proposed in this paper still hold under CNN-based architectures?\n- Going further, [2] uses Guided-GradCAM and LRP as attribution methods, extracts transferable forensic features from different layers of a CNN-based detector, and maps them back to the input image patches. The results indicate that color statistics are a key signal for CNN-based forgery detectors, rather than an extreme reliance on a few spatial patches. Different layers highlight different input regions. This behavior is not the same type of bias that the authors call “Few Patch Bias.” Therefore, the bias analysis in this paper may not generalize to CNNs, and it is not yet convincing that the claimed principles are architecture-agnostic.\n\n[1] Transformer Interpretability Beyond Attention Visualization\n\n[2] Discovering Transferable Forensic Features for CNN-generated Images Detection", "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": "9bA1HprsRs", "reviewer": "Reviewer_EuiK", "summary": "This paper tackles the challenge of generalizing AI-generated image (AIGI) detectors across different generation models. Through systematic analysis, the authors identify a key issue — “Few-Patch Bias” — where existing detectors over-rely on a small number of image patches despite artifacts being uniformly distributed across all regions of synthetic images. They propose two guiding principles, *All Patches Matter* and *More Patches Better*, and introduce the **Panoptic Patch Learning (PPL)** framework to operationalize them. PPL combines **Randomized Patch Reconstruction (RPR)**, which injects synthetic artifacts into randomly chosen patches to diversify learning, and **Patch-wise Contrastive Learning (PCL)**, which enforces consistent discriminative capability across patches. Extensive experiments on major benchmarks (GenImage, DRCT-2M, AIGCDetectionBenchmark, and Chameleon) demonstrate that PPL achieves state-of-the-art accuracy and robustness, significantly improving generalization to unseen generators and real-world data.", "questions": "I have no further questions.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper introduces clear and insightful principles (“All Patches Matter” and “More Patches Better”) that reveal a fundamental property of AI-generated images and motivate the proposed framework.\n2. The proposed Panoptic Patch Learning method is conceptually simple yet effective, combining randomized patch reconstruction and patch-wise contrastive learning to mitigate few-patch bias.\n3. Extensive experiments across diverse benchmarks demonstrate strong generalization and robustness, supported by thorough analyses and clear visual evidence.", "weaknesses": "1. In the second paragraph of the introduction, the term *“patch”* appears for the first time but lacks a clear definition or motivation. Since AIGI detection includes many CNN-based detectors that do not explicitly rely on patch-level representations, introducing the patch concept without clarification may confuse readers about its relevance to this task. It is recommended that the authors explain why they adopt patch as the basic analytical unit and cite related works that have previously used patch-based approaches, which would make the motivation more convincing.\n2. In line 364, the reference of SAFE is wrong.\n3. In Tables 3 and 4, I notice that all baseline methods are trained on GAN-based datasets, while the proposed PPL is consistently trained on SDv1.4 (a diffusion-based model). Although it is reasonable to fix one generator for evaluating generalization, different training datasets may have biases toward different test distributions (e.g., a model trained on diffusion data may generalize better to diffusion-based test sets). Therefore, I suggest that the authors also report results trained on a GAN-based dataset for these two benchmarks. This would ensure a fair comparison and further demonstrate that the proposed method is also effective when trained on GAN-generated data.", "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": "NlWi9rE2JE", "reviewer": "Reviewer_sAHA", "summary": "This paper proposed a novel patch-based AI-generated image detection method. The main motivation is the few-patch bias observed in existing detectors, i.e., they overly rely on a limited proportion of patches and neglect the diversity of artifacts across patches. To encourage the utilization of information from all patches, Randomized Patch Reconstruction (RPR) is proposed, which applies diffusion reconstruction to real images and replaces a random set of the original image patches with the corresponding reconstructed patches. Patch-wise Contrastive Learning (PCL) further encourages the learning and utilization of all patch features. Results on several benchmarks suggest the state-of-the-art generalization performance of the proposed method.", "questions": "1. How are the reconstructed images (in Figure 2 and Section 4) produced? Is there any image processing process, such as upsampling and downsampling, that can affect the low-level details of the image or introduce artifacts?\n2. Why does the proposed method generalize effectively to GAN-generated images, despite the training is solely based on diffusion models?\n3. Is it possible to set $p_{rpr}$ to 1, i.e., using only the real images and the RPR images for training?\n4. In Figure 8, what contributes to the difference between the blue and red bars at +LoRA, given that RPR is not used?", "limitations": "", "rating": 6, "confidence": 5, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. The analysis of the few-patch bias of AIGI detectors reveals a significant limitation of existing methods.\n2. The proposed RPR and PCL effectively encourage the model to utilize the information across all patches.\n3. The generalization of the proposed method is comprehensively evaluated, including testing on challenging datasets like Chameleon and robustness studies.", "weaknesses": "1. The motivation of the *All Patches Matter* principle requires further clarification.\n- The two lines of evidence stated in Lines 45-49 only support that \"some patches matter\" (i.e., some of the patches contain discriminative patterns) rather than \"all patches matter\".\n- The \"Theory\" in Line 146 may be an inappropriate title for the first key finding, as no theoretical results are provided. In addition, the assumptions behind the statement \"Because every patch of a synthetic image is itself generated, each inherently contains artifacts\" need further clarification (e.g., what \"artifacts\" are and why generative models produce them across every pixel).\n- The details for the \"Experiments\" in Lines 154-157 are not specified.\n- Given that \"a single patch contains sufficient information for reliable discrimination\" (Line 157), it seems unnecessary to emphasize the utilization of all patches. This point may not support the *All Patches Matter* principle.\n2. It seems that the attention maps in Figure 3(a) can be explained by the observations in [1] that vision transformers tend to utilize patch tokens in low-informative background areas as registers for aggregating global information. Repeating the visualization experiments with vision transformers with dedicated registers proposed in [1] may eliminate this possibility. Besides, the acquisition of the attention maps needs explanation.\n4. The experimental details for Figure 3(b) are not specified, including what detectors are tested and how the patch masking is implemented. This is important for supporting the generalization of the conclusion.\n5. It seems that the Total Direct Effect (TDE) described in Lines 210-213 should be the Controlled Direct Effect (CDE).\n6. Previous reconstruction-based methods such as [2] and [3] should be discussed and compared.\n\n\n[1] Vision Transformers Need Registers. ICLR 2024.  \n[2] Aligned Datasets Improve Detection of Latent Diffusion-Generated Images. ICLR 2025.  \n[3] A Bias-Free Training Paradigm for More General AI-generated Image Detection. CVPR 2025.", "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": "1IMrh1zcyg", "reviewer": "Reviewer_JM5D", "summary": "This paper identifies a Few-Patch Bias in current AI-generated image detectors and proposes a Panoptic Patch Learning (PPL) framework consisting of (1) Randomized Patch Reconstruction (RPR), reconstructing random real-image patches using Stable Diffusion v1.4 inpainting,  and (2) Patch-wise Contrastive Learning (PCL) applied to ViT patch tokens. The method aims to enforce equal attention to all patches and enhance artifact distribution information. Extensive experiments across several benchmarks show substantial gains.", "questions": "1. In terms of Weakness-1, (1) have you tried using GAN-based reconstruction or other diffusion models within RPR? (2) And how does the performance change when training RPR with different reconstruction backbones?\n\n2. In terms of Weakness-2, please see the questions in the comment.\n\n3. Could you report the results of directly training or evaluating CLIP/DINOv2 on the same datasets to quantify the actual gain from PPL fine-tuning?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The Few-Patch Bias is demonstrated visually (attention/TDE) and via patch-mask counterfactuals. The fix (RPR+PCL) is straightforward and well-motivated.\n\n2. The combination of RPR and PCL provides measurable improvements over existing works.\n\n3. The experiment is conducted on both toy and in-wild datasets, and demonstrates its SOTA performance.\n\n4. There are rich ablation studies to examine the designs of the proposed method.", "weaknesses": "1. RPR reconstructs patches of real images using Stable Diffusion v1.4 inpainting (empty prompt). This introduces a major domain-specific bias. It can train additional variants of RPR using GAN-based reconstruction and observe the performance changes. This can further examine the generality of PPL framework. Also, since some baselines, like SAFE, train on GAN-generated images and test on all, such an experiment can make the comparison more fair.\n\n2. In Table 9, Midjourney and ADM accuracies with dropout = 0.15 (94.3% and 87.9%, mAcc=94.2%) are much higher than at 0.1, 0.2, or 0.25 (≈70%), and close to the proposed PPL result (mAcc=97.2%). This raises several questions:\n\n(1) Why does such a narrow dropout range produce a sudden performance surge? Is there randomness in patch masking or seed selection? \n\n(2) If a properly tuned dropout rate (0.15) nearly matches PPL while avoiding diffusion reconstruction and PCL fine-tuning, the claimed advantage of PPL becomes less convincing. \n\n3. The paper fine-tunes CLIP and DINOv2 with PPL but does not clearly report the results of directly training CLIP and DINOv2.", "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)