Title: Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable
Venue: NeurIPS 2025
Accepted: yes
Keywords: AIGC Detection
Abstract: The rapid increase in AI-generated images (AIGIs) underscores the need for detection methods. 
Existing detectors are often trained on biased datasets, leading to overfitting on spurious correlations between non-causal image attributes and real/synthetic labels.
While these biased features enhance performance on the training data, they result in substantial performance degradation when tested on unbiased datasets.
A common solution is to perform data alignment through generative reconstruction, matching the content between real and synthetic images. 
However, we find that pixel-level alignment alone is inadequate, as the reconstructed images still suffer from frequency-level misalignment, perpetuating spurious correlations.
To illustrate, we observe that reconstruction models restore the high-frequency details lost in real images, inadvertently creating a frequency-level misalignment, where synthetic images appear to have richer high-frequency content than real ones. This misalignment leads to models associating high-frequency features with synthetic labels, further reinforcing biased cues.
To resolve this, we propose Dual Data Alignment (DDA), which aligns both the pixel and frequency domains. 
DDA generates synthetic images that closely resemble real ones by fusing real and synthetic image pairs in both domains, enhancing the detector's ability to identify forgeries without relying on biased features.
Moreover, we introduce two new test sets: DDA-COCO, containing DDA-aligned synthetic images, and EvalGEN, featuring the latest generative models. Our extensive evaluations demonstrate that a detector trained exclusively on DDA-aligned MSCOCO improves across diverse benchmarks. 
Code is available at https://github.com/roy-ch/Dual-Data-Alignment.

Dual Data Alignment Makes AI-Generated Image Detector Easier Generalizable

Abstract

The rapid increase in AI-generated images (AIGIs) underscores the need for detection methods. Existing detectors are often trained on biased datasets, leading to overfitting on spurious correlations between non-causal image attributes and real/synthetic labels. While these biased features enhance performance on the training data, they result in substantial performance degradation when tested on unbiased datasets. A common solution is to perform data alignment through generative reconstruction, matching the content between real and synthetic images. However, we find that pixel-level alignment alone is inadequate, as the reconstructed images still suffer from frequency-level misalignment, perpetuating spurious correlations. To illustrate, we observe that reconstruction models restore the high-frequency details lost in real images, inadvertently creating a frequency-level misalignment, where synthetic images appear to have richer high-frequency content than real ones. This misalignment leads to models associating high-frequency features with synthetic labels, further reinforcing biased cues. To resolve this, we propose Dual Data Alignment (DDA), which aligns both the pixel and frequency domains. DDA generates synthetic images that closely resemble real ones by fusing real and synthetic image pairs in both domains, enhancing the detector's ability to identify forgeries without relying on biased features. Moreover, we introduce two new test sets: DDA-COCO, containing DDA-aligned synthetic images, and EvalGEN, featuring the latest generative models. Our extensive evaluations demonstrate that a detector trained exclusively on DDA-aligned MSCOCO improves across diverse benchmarks. Code is available at https://github.com/roy-ch/Dual-Data-Alignment .

1 Introduction

The rise of AIGIs [12, 18, 41, 49] poses risks to digital security, including the potential for misinformation, fraud, and copyright violations [12, 20, 21, 18, 34, 54, 52, 51, 47, 24, 36]. This severe security issue underscores the urgent need for reliable detection methods to differentiate synthetic images from authentic ones. Despite advances in AIGI detection techniques [4, 32, 35], the rapid evolution of generative models and the emergence of new architectures present cross-domain generalization challenges. This is especially evident in zero-shot scenarios involving previously unseen generation paradigms.

The generalizability of AIGI detectors is hindered by dataset biases [13, 4, 33, 14]. Existing datasets often exhibit systematic discrepancies in attributes unrelated to the authority. Works [38] illustrate semantic bias through word frequency analysis, and studies [35] demonstrate image size bias by

RealImage

SyntheticImage

JPEG

PNG

Dog

Cat

256x512

512x512

FormatBias

SemanticBias

SizeBias

JPEG

600

256x512

All synthetic images are mistakenly classified as REAL X

95.1

06

81.4

91.7

66.6

72.9

63.

49.0

82.1

Online

BFree

60.2

92.2

73.9

53.9

59.4

suus

61.8

77.7

49.7

Alignment

DualData

64.6

90.5

50.79

T'86

90.1

84.8

77.4

53.9.

67.8

52.3

90.3

78.7

606

SAFE

AIDE

■

DRCT

AlignedForensics

Ours

87.8

analyzing on the datasets where synthetic images are uniformly sized as multiples of 128 × 128. These non-causal features could be exploited by models to distinguish real from synthetic images, resulting in biased detector performance that fails to generalize across different datasets. Figure 1 visually illustrates such bias. Dataset alignment holds promise in addressing the issue of dataset bias by ensuring synthetic images closely resemble real ones, excluding authenticity-related factors and directing detectors to focus on forgery-related cues . Specifically, studies [13] reveal systematic discrepancies in format and size biases: real images are JPEG-encoded and vary in size, whereas synthetic images are uniformly PNG-encoded and fixed in size. SemGIR [53], DRCT [4], B-Free [14] aim to mitigate content discrepancies using diffusion reconstruction techniques that generate images semantically similar to real ones. Works [15, 55, 58] prevent models from learning semantics-dependent features by breaking images into patches and shuffling them.

However, in this paper, we ask: Does reconstruction truly eliminate potential misalignment and bias? Our answer is no . We find that although reconstruction-based methods align datasets at the pixel level, they still introduce subtle misalignments at the frequency level . Specifically, generative reconstruction based data alignment tends to preserve or even amplify details across all frequency bands. In particular, reconstructed images often restore high-frequency components that are diminished in real images-typically due to compression during transmission or storage, where such components are removed to reduce file size because they have little impact on human visual perception. Consequently, synthetic images exhibit disproportionately strong high-frequency details, whereas real images contain much weaker ones , creating a noticeable discrepancy in the magnitude of high-frequency components rather than in their semantic content. This spurious correlation can lead detectors to overfit these frequency cues, mistakenly identifying high-frequency richness as an indicator of synthetic origin.

In this paper, we propose D ual D ata A lignment (DDA), an effective technique that aligns synthetic images with real ones across both pixel and frequency domains. DDA consists of three steps: 1) V AE reconstruction for pixel alignment, 2) high-frequency fusion to eliminate bias, and 3) pixel mixup for further alignment in the pixel domain. As shown in Figure 2, a single model trained on DDAaligned MSCOCO demonstrates significant improvements across benchmarks: +11.4% on Chameleon, + 26.6% on BFree-Online and + 19.4% on EvalGEN, with much lower fluctuations across subsets - usually 1/2 that of baselines. We attribute this significant performance boost to the data alignment process: when synthetic images are carefully aligned with real images across key domains, the model learns a tighter, more transferable decision boundary, enhancing generalizability

!Syn.1

PNG

Dog

512x512

!Syn.2

JPEG

Cat

256x512

!!Syn.3

PNG

Dog

512x512

11

Aligned

Syn.

JPEG

Dog

256x512

!!Real

JPEG

600

256x512

PNG

Syn.2

Cat

Syn.2

Syn.2

Syn.2

256x512

Syn. 1

Syn. 1

Syn. 1

Syn. 1

Aligned

JPEG

Real

Dog

Real

Real

Syn.

Real

512x512

Syn.3

Syn.3

Syn.3

Syn.3

DecisionBoundaryTrained on Syn.1

DecisionBoundaryTrained onSyn.2

DecisionBoundary Trained onSyn.3

DecisionBoundary Trained onAligned Syn.

FormatBias

ContentBias

ResolutionBias

NoBias

to unseen data, as demonstrated in Figure 3. Moreover, we introduce two new evaluation datasets: 1) DDA-COCO, a test set consisting of real images from MSCOCO and their DDA-aligned counterparts. This dataset evaluates whether the detector captures inherent discriminative features or relies on other biases. Prior detectors suffer significant performance drops on DDA-COCO. 2) EvalGEN, a test set consisting of FLUX, GoT, Infinity, NOVA, and OmniGen, which includes both advanced auto-regressive and diffusion generators, serving for measuring detectors' generalizability under newly evolved generative models.

2 Related Works

AIGI Detection. CNNSpot [42] trains a vanilla CNN model, finding that detectors easily recognize synthetic images from seen models but struggle to generalize to unseen ones. UnivFD [32] employs CLIP as backbone, showing the improvements in generalizability in detecting unseen generators. Subsequent works [25, 38, 55, 50] explore model architectures and image preprocessing for more generalizable detection. C2P-CLIP enhances the pretrained CLIP backbone for AIGI detection by injecting 'real' and 'fake' concepts. Works [39, 6, 23, 19, 57] exploit frequency domain artifacts, showing that frequency artifacts could well discriminate. NPR [40] explores the upsampling artifact in generative models. Vision-language approaches [27, 17, 26, 5, 46, 45] pursue explainable detection by leveraging VLMs' semantic priors. However, these methods' generalizability is limited by either content bias or frequency-level bias, with a chance of exploiting non-causal features like image format, which can degrade performance on unbiased test sets.

Dataset alignment. The evaluation bias issue in AIGI detection is firstly introduced in the work [13], showing that image format and size are common biases unintentionally exploited by detectors. FakeInversion [3] introduces a bias-reduced evaluation benchmark, mitigating thematic and stylistic biases by collecting synthetic images that match real images in both content and style. A line of subsequent works explores eliminating bias in the training set to enhance generalizability. SemGIR [53] regenerates synthetic images by semantic-level reconstruction conditioned on the real counterpart's description, aiming to better align synthetic and real images semantically. DRCT [4] employs diffusion reconstruction for improved semantic alignment. B-Free [14] addresses dataset bias through self-conditioned inpainted reconstructions and content augmentation. However, this inpainting paradigm can alter the center object, corrupting the semantic alignment. AlignedForensics [33] performs simple VAE reconstruction without latent space manipulation, resulting in synthetic images that closely match real images in semantics and resolution. However, both B-Free and AlignedForensics overlook format alignment, creating space for JPEG-based shortcuts in discrimination.

3 Methodology

3.1 Motivation and Analysis

Misaligned Dataset. In the absence of additional supervision, detectors rely exclusively on the training set to learn the concept of 'real' versus 'synthetic'. When these two classes' data differ systematically in non-causal attributes-such as compression format or semantic content-the model may incorrectly learn to associate these irrelevant features with authenticity. These spurious signals

Low

High

High-FrequencyfromReal

High-FrequencyfromVAERec.

High

Real(JPEG75)

FT-EMA (PNG)

FT-MSE (PNG)

SD15(PNG)

SD21(PNG)

SD35(PNG)

SDXL(PNG)

syntheticACC(%)

100

VAEReC.

High-Freq. Masked VAE Rec.

75

-58.0%

-58.0%

-57.0%

-54.0%

50

90.0%

94.0%

95.0%

95.0%

25

0

32.0%31.0%30.0%

SDXL

10.0%

36.0%35.0%33.0%

FT-EMA

19.0%

FT-MSE

41.0%39.0%39.0%

SD21

16.0%

are often more salient than subtle, genuine artifacts that actually distinguish real from synthetic images, making it more difficult for the model to learn truly generalizable features.

Reconstruction-based Alignment. To align synthetic images with real ones, some approaches [53, 59, 14] employ txt2img generative models to generate images with similar semantic content, conditioned on the image label or image captions obtained through pretrained models. However, images generated using this approach often differ from the originals due to the lack of strong and detailed supervision, which prevents the generated images from fully matching the original images in all semantic details. DRCT [4, 14] leverages Img2Img diffusion reconstruction, directly using the image itself to guide the reconstruction of a real image x into a synthetic counterpart ˆ x as follows:

where z represents the encoded latent of the real image, while ˆ z is modified by adding noise and subsequently denoising, creating new latents that subtly differ from z .However, such self-supervised diffusion reconstruction can still lead to changes in image details due to modifications in the latent space, which is responsible for the generation of semantics. The work [33] further simplifies the reconstruction process by using a Variational Autoencoder (VAE)-a submodule used in all stable diffusion generators-without any modification to the latent. This approach generates images that closely match the original real image at the pixel level.

Frequency-Level Misalignment Exists and Can Be Exploited. Frequency domain has been widely explored in AIGI detectors [37, 23, 30, 48], demonstrating that frequency information is crucial for AIGI detection. This motivates us to revisit the frequency-domain alignment. Surprisingly, despite pixel-level alignment, synthetic counterparts exhibit significant discrepancies in high-frequency content. Figure 4 visualizes this discrepancy between the real image and synthetic images reconstructed using various VAEs. Real images are often with relatively poor high-frequency information, which is due to JPEG compression removing high-frequency details. Having identified this frequency-level discrepancy, another question arises: "Can this disparity be leveraged, or are we overestimating its impact?" To evaluate its effect, we assess the impact by measuring the variance in

DDA

Level

Level

Real

+

VAE Reconstruction

VAE

Real

VAE Rec.

High

High

DDA

Freq. Alignment

DDA

VAE ReC.

Inpaint

Real

DDA

VAERec.

Diff.Rec.

Inpaint

T21

Diff. Rec.

T21

SD15

44.4±0.0

65.3±0.0

208.9±114.5112.8±16.5 5792.5±3.7e6

0

2.0

1.5

SDXL

0.5

2133.5±0.0

3533.7±0.0 3737.4±5318.1 4196.2±7.4e5

6325.0±5.6e6

0.0

1.5

SD21

72.3±0.0

431.0±0.0

1053.8±524.5 1207.6±1409.9 11404.7±1.3e7

0.0

detector performance on VAE-reconstructed images. As shown in Figure 5, the empirical results are striking: visually identical V AE-reconstructed images are detected by the frequency-based detector SAFE [23] with a 93% success rate, indicating a significant difference in the frequency domain. However, when we mask high-frequency information slightly, the detection rate drops dramatically. This substantial decline cannot be attributed solely to information loss; rather, it suggests that detectors exploit biased features-specifically, the richer high-frequency details in synthetic images due to their not undergoing JPEG compression, unlike real images.

3.2 Dual Data Alignment

Motivated by the previous observation, we propose DDA, a technique that generates synthetic images aligned with real ones in both the pixel and frequency domains to mitigate the learning of biased features. As illustrated in Figure 6, DDA consists of three steps: 1) VAE Reconstruction: Generate

Real

Txt2image

Diffusion Rec.

VAE Rec.

DDA

FT-MSE

FT-EMA

SDXL

SD21

pixel-wise similar images containing VAE-specific artifacts. 2) Frequency-Level Alignment: We identified that discrepancies in the frequency domain primarily arise from JPEG compression. To mitigate this, we align the frequency by applying the same JPEG compression with an equivalent quality factor to both real and V AE-reconstructed images. In practice, we estimate the quality factor of each real image before training and apply the same compression to its reconstructed counterpart during training. 3) Pixel-Level Alignment: Apply mixup between real and frequency-aligned images to ensure pixel-domain alignment. A closely aligned synthetic image is generated as follow:

where r pixel ∈ [0 , 1] controls the degree of pixel-level alignment. A higher r pixel value yields a closer synthetic image in the pixel space. In practice, r pixel is sampled from a uniform distribution U (0 , R pixel ) . Together, these steps ensure that the resulting synthetic images preserve generative artifacts while maintaining close alignment with real data, both spectrally and spatially.

The generalizability of DDA is built upon two foundations: 1) VAE artifacts generalize across generators. Because VAE-reconstructed images are the closest synthetic counterparts to real images, decision boundaries learned from these pairs are likely to remain effective for distinguishing other, more distant synthetic variants (e.g., those from text-to-image generators). Moreover, since the V AE decoder is typically the final stage in diffusion-based generators, its artifacts are less influenced by subsequent modules. 2) Dual-domain alignment mitigates dataset bias. By aligning synthetic images with real ones in both the frequency and pixel domains, DDA reduces real-synthetic discrepancies more effectively than alternative reconstruction-based methods. In particular, it eliminates high-frequency bias-commonly introduced by compression or generative artifacts-leading to stronger generalization and reduced reliance on spurious features.

Comparison to Dataset Alignment Methods. We validate that DDA creates the closest realsynthetic image pairs when compared to other alignment methods from the following three viewpoints: 1) Pixel domain: Left of Figure 7 shows that DDA-aligned images lead to minimal MSE loss compared to the original image; 2) Frequency domain: Right of Figure 7 shows that DDA-aligned images are most similar to the original image in frequency space; 3) Feature domain: Figure 8 validates that the cluster center of DDA-aligned images is closest to the center of real images.

4 Experiments

4.1 Experimental Setup

Datasets All compared detectors are evaluated on eleven diverse datasets, including seven benchmark datasets (GenImage [59], DRCT-2M [4], Synthbuster [1], DDA-COCO, EvalGEN, AIGCDetectionBenchmark [56] and ForenSynths [43] ) and four in-the-wild datasets (Chameleon [48], WildRF [2], SynthWildx [7] and BFree-Online [14]), where images are sourced from the web. These datasets contain real images from different sources and various generators, including diffusion models, GAN models, auto-regressive models, and other unknown models. They differ in format, content, and resolution, thereby minimizing evaluation bias. Table 1 outlines the datasets' details.

Method

Manually Curated Datasets

In-the-Wild Datasets

Avg

Min

GenImage

DRCT-2M

DDA-COCO

EvalGEN

Synthbuster

ForenSynths

AIGCDetection

Benchmark

Chameleon

Synthwildx

WildRF

Bfree-Online

1G + 7D

16D

5D

3D + 2AR

9D

11G

7G + 10D

Unknown

3D

Unknown

Unknown

NPR (CVPR'24)

[8]

51.5

±

6.3

37.3

±

15.0

42.2

±

5.4

2.9

±

2.7

50.0

±

2.6

47.9

±

22.6

53.1

±

12.2

59.9

49.8

±

10.0

63.5

±

13.6

49.5

46.1

±

16.1

2.9

[32]

64.1

±

10.8

61.8

±

8.9

52.4

±

1.5

15.4

±

14.2

67.8

±

14.4

77.7

±

16.1

72.5

±

17.3

50.7

52.3

±

11.3

55.3

±

5.7

49.0

56.3

±

16.5

15.4

FatFormer (CVPR'24)

[31]

62.8

±

10.4

52.2

±

5.7

51.7

±

1.5

45.6

±

33.1

56.1

±

10.7

90.0

±

11.8

85.0

±

14.9

51.2

52.1

±

8.2

58.9

±

8.0

50.0

59.6

±

14.6

45.6

SAFE (KDD'25)

[23]

50.3

±

1.2

59.3

±

19.2

49.9

±

0.3

1.1

±

0.6

46.5

±

20.8

49.7

±

2.7

50.3

±

1.1

59.2

49.1

±

0.7

57.2

±

18.5

50.5

47.6

±

16.0

1.1

C2P-CLIP (AAAI'25)

[38]

74.4

±

8.4

59.2

±

9.9

51.3

±

0.6

38.9

±

31.2

68.5

±

11.4

92.0

±

10.1

81.4

±

15.6

51.1

57.1

±

4.2

59.6

±

7.7

50.0

62.1

±

15.6

38.9

AIDE (ICLR'25)

[48]

61.2

±

11.9

64.6

±

11.8

50.0

±

0.4

19.1

±

11.1

53.9

±

18.6

59.4

±

24.6

63.6

±

13.9

63.1

48.8

±

0.8

58.4

±

12.9

53.1

54.1

±

12.8

19.1

DRCT (ICML'24)

[4]

84.7

±

2.7

90.5

±

7.4

60.2

±

4.3

77.8

±

5.4

84.8

±

3.6

73.9

±

13.4

81.4

±

12.2

56.6

55.1

±

1.8

50.6

±

3.5

55.7

70.1

±

14.6

50.6

AlignedForensics (ICLR'25)

[33]

79.0

±

22.7

95.5

±

6.1

86.5

±

19.1

68.0

±

20.7

77.4

±

25.0

53.9

±

7.1

66.6

±

21.6

71.0

78.8

±

17.8

80.1

±

10.3

68.5

75.0

±

11.1

53.9

DDA (ours)

91.7

±

7.8

98.1

±

1.4

92.2

±

10.6

97.2

±

4.2

90.1

±

5.6

81.4

±

13.9

87.8

±

12.6

82.4

90.9

±

3.1

90.3

±

3.5

95.1

90.7

±

5.3

81.4

DDA-COCO and EvalGEN DDA-COCO consists of five subsets containing reconstructed images of MSCOCO [28] validation set by different VAEs, utilizing frequency-level alignment. We construct the EvalGEN dataset using the five latest text-to-image (T2I) generators using aligned prompts from the GenEval benchmark [11]. Notably, we are the first work to involve auto-regressive-based T2I generators for image forensics in the AIGI detection field. Specifically, we introduce each generator as follows: (1) Flux [22]: the SOTA diffusion-based generator, offering extremely higher-resolution output images. (2) GoT [10]: A multimodal model combining LLM and diffusion processes to enable reasoning-guided image generation. (3) Infinity [16]: A bitwise auto-regressive model using infinite-vocabulary tokenization and self-correction for faster and higher-fidelity image generation. (4) OmiGen [44]: A unified multimodal framework capable of handling diverse image generation tasks within a single, simplified architecture. (5) NOVA [9]: A non-quantized auto-regressive model designed for efficient image and video generation, achieving high fidelity with reduced computational overhead. These models allow our EvalGEN to serve as a very high-quality benchmark for evaluating the generalizability of detectors on unseen generators.

Implementation Details We use DINOv2 as the backbone and fine-tune it with LoRA, using a rank of 8. The input resolution is set to 336×336, employing random cropping during training and center cropping during validation. Padding is applied when the image height or width is insufficient. The training data exclusively consists of MSCOCO [29] images and their DDA-aligned counterparts. During VAE reconstruction, to ensure that the reconstructed image size matches the real one, we first center-crop each image to the largest size that is a multiple of 8, following the V AE model's design. For frequency alignment in DDA, we apply the same JPEG compression to each reconstructed counterpart with a 50% probability during training, allowing the model to encounter both JPEG and PNG formats of synthetic images. All evaluations are conducted using a single model without any dataset-specific fine-tuning or threshold adjustments.

Evaluation Metrics and Comparative Methods Unless otherwise specified, we report balanced accuracy, the average of real and fake accuracies, as the evaluation metric, following works [8, 32, 31, 23, 38, 48, 4, 33, 14]. The methods compared include four frequency-based detectors: NPR [8], SAFE [23], and AIDE [48]; three CLIP-based detectors: UnivFD [32], Fatformer [31], and C2P-CLIP [38]; and two data alignment methods: DRCT [4] and AlignedForensics [33].

4.2 Cross-Dataset and Cross-Model Comparison

Overall Comparison on 11 Benchmarks. Table 2 presents a comprehensive comparison across 11 datasets-7 manually curated and 4 in-the-wild-covering most known open-source AIGI evaluation benchmarks. To the best of our knowledge, the first large-scale comparison of its kind. As benchmarks vary greatly, the benefits of exploiting any bias are minimized, making the average accuracy across the 11 benchmarks more representative of the detector's practical performance. The results show that: 1) DDA achieves an average accuracy of 90.7%, marking a 15.7% improvement over the second-best method. Notably, DDA reaches 82.4% accuracy on the challenging Chameleon benchmark, where only AlignedForensics achieves above 70%; 2) DDA also has the highest minimal accuracy of 81.4%, significantly outperforming the second-best method by 27.5%. Moreover, DDA exhibits the smallest deviation across benchmarks, less than half of the other methods, suggesting it is more reliable and robust; 3) An interesting observation is that, when comparing methods across datasets, detectors trained on more aligned data tend to achieve much higher average accuracy. The degree of data alignment in the detectors, in increasing order, is as follows: UnivFD (no data alignment), DRCT (data alignment via diffusion reconstruction), AlignedForensics (data alignment via VAE reconstruction), and DDA (dual-domain data alignment). The overall average accuracy follows the same order as the degree of data alignment. This clearly demonstrates the effectiveness of data alignment in improving a detector's generalizability, supporting our previous assertion that data alignment helps models learn more transferable decision boundariess.

Detailed Comparison on DRCT-2M, GenImage, AIGCDetectionBenchmark, ForenSynths, Synthbuster, SynthWildx, and WildRF. Table 3 to Table 8 report the detailed performance of various methods across these subsets. From the results, we observe the following: (1) Consistent superiority: DDA not only surpasses other detectors by a substantial margin in average accuracy (ranging from 3% to 10%) but also achieves consistently lower deviations across all benchmarks. Given the diversity of real image sources and the inclusion of both GAN- and diffusion-based models,

these results strongly demonstrate the effectiveness and generalizability of DDA. (2) Exception on ForenSynths: DDA underperforms slightly on the ForenSynths benchmark. We attribute this to the fact that the two methods outperforming DDA-FatFormer and C2P-CLIP-were trained on ProGAN, which is also the generator used in the ForenSynths subsets, giving them an advantage. Moreover, some data in the ForenSynths are generated by older and smaller models, which deviate significantly from modern generators, contributing to DDA's performance degradation.

Comparison on DDA-COCO and EvalGEN Table 9 and Table 10 report accuracies on our two proposed benchmarks, DDA-COCO and EvalGEN, respectively. We observe the following: (1) Generalization across diffusion models: DDA, trained solely on SD 2.1-reconstructed data, generalizes well to other diffusion models and exhibits a smaller standard deviation. This suggests that DDA learns a universal upsampling artifact shared across diverse generative models, reinforcing our claim that data alignment enhances generalizability. (2) Effectiveness of data alignment: Results on DDA-COCO highlight the importance of data alignment. Methods lacking explicit alignment-such as NPR [8], UnivFD [32], FatFormer [31], C2P-CLIP [38], AIDE [48], and SAFE [23]-exhibit large disparities between real and fake accuracies, revealing underlying dataset biases. (3) Performance on emerging generators: DDA also achieves SOTA on EvalGEN, excelling on auto-regressive generators, further validating its strong cross-architecture generalizability.

Comparison on Generation Time Cost We compare three methods which introduce train data generation-DRCT [4], AlignedForensics [33], and B-Free [14]. Table 11 presents the number of real and synthetic images used for training, generation method and the estimated reconstruction time (Single Image & Full Set) using each method, which is tested by generating 100 synthetic images. Results show that our DDA requires the least amount of training data and reconstruction time, confirming its effectiveness and efficiency in terms of training cost.

Method

# Real / Fake

Generation Method

Time Per Image

Full Construction Time

DRCT

118K / 354K

Diff. Rec.

0.6569 ± 0.0050 s

64.6 h

AlignedForensics

179K / 179K

VAE Rec.

0.1756 ± 0.0692 s

8.73 h

B-Free

51K / 309K

Diff. Rec + Inpaint.

3.0150 ± 0.0125 s

258.79 h

DDA (ours)

118K / 118K

VAE Rec. + DDA

0.1792 ± 0.0704 s

5.9 h

(a) Impact of

P

pixel

(b) Impact of

R

pixel

(c) Impact of VAE

NPR

FatFormer

SAFE

C2P-CLIP

AIDE

DRCT

AlignedForensics

DDA

100

100

100

80

80

80

Balanced Acc

AcC

AcC

60

Balanced

60

Balanced

60

40

40

40

B

B

20100

200.5

200.0

90

80

70

60

1.0

1.5

2.0

0.5

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JPEGQualityFactor

ResizingScale

BlurringSigma

100

100

100

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90

BalancedAccuracy

90

BalancedAccuracy

90

80

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60

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SD35

Figure 10: Ablation studies. (a) P pixel : probability of applying pixel-level alignment; (b) R pixel : upper bound for sampling the pixel mixup ratio; (c) V AE: backbone used for training data reconstruction. Results show the impact of each hyperparameter on the performance of DDA.

4.3 Evaluation on Robustness

Figure 9 shows the results of three robustness evaluations on the GenImage-JPEG96 dataset for all compared methods. Results show that: (1) DDA shows strong robustness across all three postprocessing methods, outperforming the second-best method by 10.5%, 4.1%, and 5.7% under JPEG 60, RESIZE 2.0, and BLUR 2.0, respectively. (2) Methods lacking alignment, such as NPR [8], SAFE [23], and AIDE [48], demonstrate poor robustness under JPEG compression and resizing. In contrast, methods with alignment perform much better, emphasizing the importance of data alignment.

4.4 Ablation Studies

Figure 10 illustrates the impact of P pixel , R pixel , and the choice of V AE in training data generation. Results indicate that the detector maintains consistent accuracy when P pixel and R pixel are between 0.2 and 0.8, with performance drops observed at 0.0 and 1.0. Experiments with different VAEs confirm that SD21 is the most effective choice.

5 Conclusion

In this paper, we demonstrate that pixel-domain alignment alone is insufficient for fully aligning real and synthetic image pairs. Building on this, we propose DDA to align synthetic images with real ones across both pixel and frequency domains, thereby mitigating bias. We also introduce two AIGI benchmarks: DDA-COCO and EvalGEN. Extensive experiments across eleven benchmarks demonstrate the consistent superiority of DDA. We believe that DDA, DDA-COCO, and EvalGEN provide a solid foundation for advancing the generalization of AIGI detection.

Limitations and Future Work While DDA demonstrates strong performance across extensive benchmarks, there remains a gap in its application to real-world scenarios, particularly due to the heavy post-processing applied to images in such contexts. In practice, we observe that even authentic photos taken by smartphones may exhibit synthetic-like artifacts, likely resulting from the AI-based enhancements embedded in modern smartphone camera pipelines, which adds complexity to realworld AIGI detection. In future work, we plan to develop a more practical AIGI detector tailored to address these real-world challenges.

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The Appendix provides additional technical and evaluative details of our work. Section A presents the implementation details of DDA. Section B summarizes the peer methods . Section C visualizes the degree of data alignment of different methods in the feature space. Section D presents ablation studies on input size and backbone . Section E provides details of our proposed dataset EvalGEN , including example prompts used for generation and visualizations of selected samples. Finally, Section F visualizes the regional detection results of DDA .

A Implementation Details

Training Details. All experiments were conducted on eight NVIDIA V100 GPUs. We trained the detector on a dataset consisting of MSCOCO images and their synthetic counterparts generated through DDA alignment using the VAE from Stable Diffusion 2.1. The model was optimized with a base batch size of 16 and a learning rate of 1e-4. To achieve an effective batch size of 64 without exceeding GPU memory limits, gradient accumulation was applied over four iterations. Balanced accuracy was evaluated on all datasets every 10,000 iterations, and early stopping was employed to prevent overfitting. To help the model better shape its decision boundary, each batch was manually constructed to include both real images and their DDA-aligned counterparts, allowing the model to simultaneously observe closely aligned positive and negative samples.

B Peer Methods

Below we provide a brief description of the compared methods used in Section 4 of main paper.

NPR[8] This detector leverages low-level features-neighboring pixel relationships-to distinguish synthetic images from real ones. NPR trains a ResNet-50 to identify upsampling patterns.

UnivFD [32] Instead of conventional supervised training, this method utilizes features from a vision-language model (CLIP-ViT) combined with a linear classifier. This approach avoids overfitting to specific generative artifacts and generalizes better to unseen generators.

FatFormer [31] FatFormer builds on a ViT backbone, incorporating a forgery-aware adapter that adapts features in both the image and frequency domains. It introduces language-guided alignment using contrastive learning with text prompts to improve generalization.

SAFE [23] This method focuses on frequency domain artifacts. The detector is built upon a ResNet backbone and trained with several data augmentation techniques, including random masking.

C2P-CLIP [38] The method utilizes CLIP embeddings with category-specific prompts to enhance deepfake detection generalizability. Image captions are generated using ClipCap and enhanced with category common prompts. During training, these enhanced caption-image pairs train the image encoder through contrastive learning. For inference, only the modified image encoder and a linear classifier are used.

AIDE [48] This work employs a hybrid approach that combines low-level patch statistics with high-level semantics. It uses DCT scoring to select extreme frequency patches for extracting noise patterns through SRM filters, while utilizing CLIP embeddings to capture semantic information. These complementary features are fused through channel-wise concatenation before classification.

DRCT [4] This method reconstructs real images using diffusion models to generate challenging synthetic samples that retain visual content while introducing subtle artifacts. Contrastive learning is employed to guide detectors toward recognizing these fingerprints, improving generalization.

AlignedForensics [33] This method creates aligned datasets by reconstructing real images through a single forward pass in an LDM's autoencoder. This forces the detector to focus exclusively on artifacts introduced by the V AE decoder, avoiding reliance on spurious correlations.

●Real

Txt2image

DiffusionRec.

VAE Rec.

●DDA

Better Alignment

DRCT

Aligned

Forensics

DDA (ours)

FT-MSE

FT-EMA

SDXL

SD21

B-Free [14] B-Free introduces a training paradigm using self-conditioned diffusion-based reconstructions. It ensures semantic alignment between real and synthetic images so that differences arise solely from generation artifacts. The approach includes content augmentation via inpainting and fine-tunes a DINOv2+reg ViT using large crops to retain forensic signals.

C More Comparison Results

Comparison to Dataset Alignment Methods in Feature Domain Fig 11 presents t-SNE visualizations of real and synthetic image features, generated by detectors trained with varying data alignment strategies. Each row, from top to bottom, represents detectors trained on datasets with progressively stronger alignment. Detectors trained on better-aligned datasets yield more separable feature distributions, suggesting that enhanced alignment facilitates clearer decision boundaries between real and synthetic content. These findings reinforce the role of data alignment in improving feature separability and overall AGI detector performance.

D More Ablation Results

Ablation on Input Size Table 12 presents an ablation study of our method across different input sizes, ranging from 224 to 504. Detectors achieve comparable accuracies across these input sizes.

Ablation on Backbone Table 13 presents an ablation study comparing the performance of different backbone architectures. The ResNet backbone is excluded from this study due to training instability and failure to converge. The relatively poor performance of linear probing methods is attributed to the limited representational capacity of a single linear layer. This observation aligns with the convergence issues observed with ResNet, suggesting that the universal artifacts in our training data are inherently more difficult to learn. In contrast, AlignedForensics [33] successfully employs a ResNet backbone, implying that the artifacts used in our training setup may be subtler or more complex than those captured in prior work. Another key finding is that DINO-LoRA outperforms CLIP-LoRA. This

performance difference is likely due to the architectural focus of each backbone: CLIP emphasizes high-level semantic features, while DINO is more attuned to low-level visual patterns-which are more indicative of AI-generated image artifacts. Moreover, DINO-LoRA achieves a lower standard deviation, indicating greater stability for robust AGI detection.

E More Details of EvalGEN

To construct EvalGEN, we used 553 distinct prompts, each generating 20 synthetic images per generator, resulting in 11,060 images per generator and a total of 55,300 synthetic images in the complete dataset. All images are stored in JPEG format with a quality factor of 96. A subset of prompts is provided above to illustrate the dataset's diversity and semantic coverage, while

Fig. 12 shows visual examples from EvalGEN. To balance efficiency and representativeness, for the comparison in Table 2 and Table 10 of the main paper, we selected the first (index 0) image generated for each prompt, yielding 55,300 / 20 = 2,765 samples for quantitative evaluation.

F Regional Detection Analysis

Figure 13 displays heatmaps of detection scores across segmented image regions, with numerical overlays indicating the detector's predictions. These results reveal that detection scores vary by region, indicating that synthetic artifacts are spatially uneven. This observation suggests that localized detection strategies could further enhance robustness.

Flux

GoT

Infinity

NOVA

Synthbuster

0.47

0.46

0.55

0.55

Chameleon

0.81

0.91

0.67

0.33

0.19

0.92

0.69

0.57

0.59

0.54

ForenSynths

0.92

0.05

SynthWildx

0.05

0.07

0.06

0.05

AIGCDetection

Benchmark

WildRF

EvalGEN

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Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes Dual Data Alignment (DDA), a method to improve the generalization of AI-generated image detectors by aligning real and synthetic images in both pixel and frequency domains. Unlike prior work that only aligns pixel-level content, DDA also corrects frequency-level biases that detectors may exploit. The authors demonstrate that detectors trained on DDA-aligned data significantly outperform baselines across diverse benchmarks, including unseen generative models and real-world datasets. Two new datasets, DDA-COCO and EvalGEN, are also introduced for robust evaluation.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**\n\n1. Proposes a dual-domain alignment (pixel + frequency), addressing overlooked frequency-level biases in existing methods.\n1. Achieves strong generalization across 8 diverse benchmarks, outperforming prior state-of-the-art detectors.\n1. The DDA method is efficient to implement and significantly reduces training data generation time.\n\n\n\n**Weaknesses:**\n\n1. Since using VAE reconstruction to build training datasets has already been proposed in [1], the main novelty of this paper, to my understanding, lies in two aspects: (i) **Frequency-Level Alignment**, which replaces high-frequency DCT coefficients of synthetic images with those from real images; and (ii) **Pixel-Level Alignment**, a mixup-style operation applied between real and synthetic images.\n2. Regarding the first point, I have two main concerns. First, it is unclear whether the authors apply the DCT transform at the original image resolution or in 8×8 blocks—this is not explicitly specified in the paper. Second, the authors appear to define the high-frequency region as a bottom-right rectangle in the DCT space. However, DCT coefficients increase in frequency in a zigzag pattern from the top-left to the bottom-right. As such, this rectangular selection might omit some high-frequency components, making the design potentially suboptimal.\n3. For the second point, while mixup is a well-established technique in image classification, it has not been applied in AIGI detection before, so I acknowledge this as a valid contribution. However, in Fig. 9(c), the ablation results show that even when rpixel=0rpixel=0 or rpixel=1rpixel=1, the detector still achieves high accuracy. This is counterintuitive—could the authors clarify why extreme values still perform well?\n4. The detector is fine-tuned using DINOv2 with an input size of 336×336. It would be helpful for the authors to include ablations on different commonly used backbones (e.g., CLIP, ResNet-50) and input sizes (e.g., 224×224), since most baselines are trained under these settings. Also, any justification for selecting DINOv2 as the backbone would be appreciated.\n5. The proposed method appears to be model-agnostic. Can the authors demonstrate whether DDA-aligned data could also enhance performance when used to train other existing detection methods?\n\n**In summary**, this paper tackles a critical challenge in AIGI detection—robust generalization in real-world scenarios—and achieves state-of-the-art performance across multiple benchmarks. However, several technical and design questions remain to be addressed.\n\n\n\n[1] Aligned Datasets Improve Detection of Latent Diffusion-Generated Images, ICLR'25\n\nQUESTIONS:\nSee the weakness part.\n\nLIMITATIONS:\nSee the weakness part.", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-21"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a new method for AI-generated Image (AIGI) detection. The paper hypothesizes that dataset bias, both in pixel-level semantics and in frequency domain is an important factor for the insufficient generalizability of the existing detection methods to unseen generative models. Therefore, this paper, for the first time introduces a dual data alignment (DDA) pipeline to minimize the effect of dataset bias and create a real vs. AI-generated dataset from MS-COCO (referred to as DDA-COCO).  By fine-tuning a DINOv2 backbone using LoRA on DDA-COCO, the proposed method achieves state-of-the-art performance in terms of balanced accuracy on several existing benchmarks as well as two new benchmarks introduces in this paper.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths**\n1. The paper is well-written, and it is easy to follow. \n2. The frequent use of clear visualizations and figures helps with understanding the concepts and the proposed novelties.\n3. The proposed method is properly motivated and is an easy-to-understand and elegant solution.\n4. The proposed method shows robust performance across several datasets, achieving state-of-the-art results.\n5. The experimental results are extensive and the proposed method's performance is compared against recently published papers in top-tier venues on recent benchmarks.\n\n**Weaknesses**\n1. The main hypothesis of the paper (dataset bias is the problem and that the data alignment helps) is not verified in isolation. Although extensive experimental results show strong performance of the proposed method, the strong performance cannot be solely attributed to the use of the data alignment pipeline. There are several differences between the proposed method and existing approaches other than the use of DDA. For example, the original source of data used in this paper is different from those used in competitive methods. Additionally, the backbone, the fine-tuning strategy and the input size to the backbone are all different compared to existing methods. Given this, it is hard to be sure if DDA is the main reason behind the proposed method's strong performance.\n2. The paper does not report threshold-less metrics such as AP or AUROC which are commonly used in many published papers in this area. Threshold-less metrics are important measures of the separability between the representation of real vs AI-generated samples. Additionally, the difference in accuracy numbers can be attributed to the poor choice of the decision thresholds. \n3. The mechanism for choosing a decision threshold is not discussed in the paper.\n\nQUESTIONS:\nReferring to the weaknesses section I have the following questions:\n\n1. How would existing methods' performance change if DDA was used to minimize the training dataset bias?\n2. How AP or AUROC of the proposed method compare with that of the existing methods?\n3. How thresholds for different methods are chosen in this study?\n\nBased on the authors' response, I would be willing to increase my rating to 5 (accept).\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 4, "CLARITY": 4, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-02"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis study identifies that single reconstruction alone does not suffice to achieve comprehensive alignment between real and synthetic image pairs. To address this limitation, the authors propose a novel approach termed Dual Data Alignment (DDA), which aligns synthetic images with their real counterparts in both pixel and frequency domains, thereby reducing bias in AIGI detectors. Furthermore, two new AIGI datasets are presented to expand testing scenarios across diverse domains. Extensive evaluations conducted on eight benchmark datasets validate the effectiveness of the proposed methodology.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The writing of this manuscript is generally clear and easy to follow.\n2. The topic of detecting AIGC (AI-Generated Content) images is both timely and interesting.\n3. The authors have conducted extensive experiments, which demonstrate the effectiveness and relevance of the proposed method.\n4. The proposed method is novel in that it simultaneously considers both pixel-level and frequency-domain alignment.\n\nWeaknesses：\n1. The authors claim that existing datasets suffer from biases in format, content, and size. However, these biases can often be mitigated through data augmentation or by expanding the dataset, without the need for complex reconstruction-based approaches. For instance, JPEG compression and cropping augmentation could address format and size biases effectively.\n2. The primary objective of reconstruction-based methods is typically to uncover the intrinsic differences between real and fake images, rather than to align real and fake data distributions.\n3. It is unclear whether the proposed method and the baselines in Table 3/4/5/6 were trained under the same conditions, particularly with respect to the training dataset. Clarification on this point would be helpful for a fair comparison.\n4. Since the proposed method uses DINOv2 as the backbone, whereas some baselines rely on CLIP or ResNet, it would be important to discuss how the choice of backbone affects the results. This would help ensure a fair and meaningful comparison.\n5. The proposed method has not been evaluated on the ForenSynths dataset (CNNSpot CVPR 2020), a commonly used benchmark for detecting CNN-generated images. This limits the completeness of the experimental validation.\n6. The generalization capability of the proposed DDA method to GAN-generated images is not discussed. It would be beneficial to evaluate its performance on such images to better understand its applicability across different types of generative models.\n\nQUESTIONS:\n1. Please refer to weakness.\n2. The authors should clarify the fairness of the experimental comparisons. Specifically, in Table 3, it is unclear whether the proposed method and the baseline methods were trained on the same datasets and under comparable settings. This information is critical for a fair evaluation.\n3. The backbone used in the proposed method is DINOv2, while some baselines adopt different backbones such as CLIP or ResNet. The authors should discuss how the choice of backbone influences performance and whether it contributes significantly to the observed improvements.\n\nLIMITATIONS:\nThe paper should provide a more comprehensive discussion on the motivations behind image reconstruction as well as address issues related to experimental fairness, ensuring greater transparency and equity in the evaluation process.", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-07-05"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper introduces Dual Data Alignment (DDA), a method to improve the generalizability of AI-generated image (AIGI) detectors by addressing dataset biases. Existing detectors struggle with new data because they often overfit on non-causal attributes like image format or size. DDA aligns synthetic and real images in both pixel and frequency domains, a crucial improvement over pixel-level alignment alone, which still leaves frequency-level discrepancies. The method involves VAE reconstruction, high-frequency fusion, and pixel mixup. DDA demonstrates significant performance improvements across diverse benchmarks, including new datasets like DDA-COCO and EvalGEN, highlighting its ability to create more unbiased and robust AIGI detectors\n\nSTRENGTHS AND WEAKNESSES:\nStrengths: \nQuality based on robust experimental validation - The paper presents extensive evaluations across eight diverse datasets, including \"in-the-wild\" benchmarks, which significantly strengthens its claims of improved generalizability. The consistent outperformance of DDA over state-of-the-art methods (e.g., +12.4% on GenImage, +9.8% on Synthbuster, +17.7% on EvalGEN) is a strong indicator of its effectiveness. The inclusion of new, challenging test sets like DDA-COCO and EvalGEN is particularly valuable for rigorously assessing detector performance against new generative architectures and aligned data. The robustness analysis under various post-processing methods (JPEG compression, resizing, blurring) further validates DDA's practical utility, showing its ability to maintain high performance even when images are altered. This is critical for real-world deployment where images are frequently compressed or modified. The paper tackles the fundamental issue of dataset bias in AIGI detection, a well-recognized challenge that hinders the real-world applicability of detectors. By identifying and addressing both pixel-level and, crucially, frequency-level misalignment, the authors pinpoint a subtle yet significant source of bias that previous reconstruction methods missed.\n\nClarity - The paper clearly articulates the \"frequency-level misalignment\" problem with existing reconstruction methods, using Figure 3 and Figure 4 to visually and empirically demonstrate the issue. This strong motivation for DDA's dual-domain alignment is a major plus. The paper addresses very clear methodology, Figure 5 provides a clear visual pipeline of the proposed method.\n\nSignificance - The primary significance lies in the demonstrated improvement in generalizability for AIGI detectors. This is a crucial step towards more reliable and deployable fake image detection systems, addressing a pressing societal concern regarding misinformation and fraud.\n\nWeaknesses:\nQuality - The paper Limited Explanation of T freq and R pixel Parameter Selection and needs more information. \n\nClarity - \"Theory Assumptions and Proofs\" Claim: The paper states \"Yes\" for providing full assumptions and complete/correct proofs in Section 3.2. However, Section 3.2 primarily describes the methodology and motivation, not formal theorems, lemmas, or rigorous mathematical proofs in the traditional sense. This checklist answer might be misleading and could be interpreted as a claim of theoretical contribution that isn't fully supported by the content of Section 3.2.\n\nQUESTIONS:\n1. Clarify and Substantiate the \"Universal Upsampling Artifact\" Claim.\nQuestion: The paper states: \"We hypothesize that this artifact arises during the VAE-based decoding process\". While interesting, this remains a hypothesis. What specific characteristics define this \"universal upsampling artifact\"? Can the authors provide more empirical evidence or theoretical reasoning to support its \"universality\" across diverse generative models beyond the VAE decoding stage?\n\n2. Address Performance on Heavily Post-Processed Images (Chameleon Dataset).\nQuestion: The paper acknowledges that \"Our method performs relatively lower on FLUX... and struggles to detect images with strong post-processing artifacts, as shown in the results on the Chameleon dataset\". Given that real-world scenarios often involve aggressive post-processing, how do the authors envision mitigating this limitation? Is DDA inherently sensitive to certain types of artifacts, or are there planned extensions to improve robustness in these challenging cases?\n\n3. Re-evaluate \"Theory Assumptions and Proofs\" Claim for Clarity.\nQuestion: The NeurIPS checklist response states \"Yes\" for \"For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof?\" with a justification referring to Section 3.2. However, Section 3.2 primarily describes the methodology and provides intuitive explanations, rather than formal theoretical results, theorems, or proofs. This might be a misunderstanding of what constitutes a \"theoretical result\" in this context.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-07"}