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REVIEW 4 major objections 4 minor 89 references

Any-Resolution AI-Generated Image Detection by Spectral Learning

T0 review · 4 major / 4 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read SPAI learns the frequency spectrum of real images and flags AI-generated pictures as out-of-distribution samples, averaging 91.0% AUC across 13 generators.

desk verdict Solid detector with a misleading self-supervised framing and a test-set-tuned headline number; deserves peer review but needs an honest rewrite. read the letter →

arxiv 2411.19417 v2 pith:6UYEN253 submitted 2024-11-28 cs.CV cs.LGeess.IV

Dimitrios Karageorgiou , Symeon Papadopoulos , Ioannis Kompatsiaris , Efstratios Gavves This is my paper · ORCID
classification cs.CVcs.LGeess.IV
keywords AI-generatedimagedetectionspectralartifactsmaskedfrequencymodelingself-supervisedlearningout-of-distributionvisiontransformersforensicsany-resolution
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This paper claims that the frequency spectrum of real photographs is a stable, learned pattern that separates them from AI-generated images no matter which generator produced them. The authors train a vision transformer to reconstruct masked frequency components of real images only, then flag any image whose reconstructed spectrum diverges from the observed one. On a benchmark of 13 recent generators and five real-image sources, the resulting detector, SPAI, reaches an average AUC of 91.0%, 5.5 points above the previous best method, and keeps an edge under JPEG/WebP compression, blur, noise, and resizing. Because the model never trains on artifacts of any specific generator, the paper argues it should also catch generators not seen in training.

What carries the argument

The load-bearing objects are: (1) a frequency mask M defined by a fixed radius r=16 around the spectrum center, splitting an image into low- and high-frequency components via the 2D DFT; (2) the frozen ViT-B/16 backbone G pretrained with masked frequency modeling, which acts as the learned spectral model of real images; (3) Spectral Reconstruction Similarity (SRS), the cosine similarity lambda(z_A,z_B)=z_A·z_B/(||z_A||||z_B||) between projected token representations of original, low-pass, and high-pass versions, aggregated into per-block means and standard deviations; (4) Spectral Context Vector (SCV), which summarizes spectral context by attending over block statistics with a learnable spectral map; and (5) Spectral Context Attention (SCA), which attends over image patches with a learned importance vector and fuses their spectral vectors in O(K) time. Together they convert the question 'does this image's spectrum look real?' into a scalar score trained end-to-end with binary cross-entropy.

What would settle it

Run SPAI on a curated set of real photographs from a camera or computational-photography pipeline whose spectral fingerprint was not among the five training sources; if a substantial fraction of these genuine images are flagged as synthetic, the claim that real-image spectra form a universal invariant fails, because the learned 'invariant' would instead be dataset-specific.

Watch

Extended reading notes

Core claim

The paper's central claim is that the spectral distribution of real images is both invariant and highly discriminative, so AI-generated images can be treated as out-of-distribution samples of a learned spectral model of reality. The model G is a frozen ViT-B/16 transformer pretrained with masked frequency modeling on ImageNet; only a reconstruction head is trained, on 180k real images, using a frequency-distance loss. At inference, Spectral Reconstruction Similarity computes the cosine similarity between representations of the original, low-pass, and high-pass versions of an image across all transformer blocks, and the paper reports that this score reliably separates real from generated content across generators never seen during training. Spectral Context Attention then pools the most discriminative patch-level similarity values using a learned context vector, allowing images of any resolution to be scored at native size. On 13 generative models and five real-image sources, the method reports 91.0% average AUC, a 5.5-point absolute gain over the best prior detector, with higher accuracy under common perturbations.

Load-bearing premise

The entire method rests on the premise that one frozen transformer, pretrained on ImageNet with a fixed masking radius, captures the true spectrum of all real photographs, so any generated image can be spotted simply by being an outlier in that learned spectrum.

Editorial extensions

If this is right

  • A detector can be built from real images alone, without any labeled examples from a specific generator, and the same trained model transfers across the 13-generator benchmark.
  • Average detection AUC improves by 5.5 absolute points over the previous best detector (85.5 to 91.0) on the same test set.
  • Because SCA processes patches at native resolution, images of many megapixels can be checked without downsampling; the supplementary material shows roughly linear scaling up to gigapixel inputs.
  • Stability under JPEG/WebP compression, Gaussian blur, noise, and resizing is consistently above the compared methods, meaning recompressed online copies remain detectable.
  • The paper's own failure analysis shows that derivative copies such as screenshots or printed photos can destroy spectral cues, so those cases remain a limitation.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The invariance claim could be tested by training or fine-tuning the spectral model on real images from a disjoint camera era and checking whether SRS still separates current generators; if performance decays, the 'real-spectrum invariance' is partly history-dependent.
  • The same SRS and SCA machinery could be applied to other open-set out-of-distribution tasks, such as manipulated video frames, since the method learns only the positive distribution.
  • An adversarial generator engineered to match real amplitude spectra, for example by adding camera-ISP-style post-processing, may collapse SRS; testing that scenario would clarify whether the invariant is genuinely spectral or partly generator-dependent.
  • Nothing in the method uses generator labels, so the classifier head could in principle be replaced by a calibrated one-class rule on SRS alone, making the approach fully self-supervised at decision time.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. The paper proposes SPAI, an AI-generated-image detector built around the idea that the spectral distribution of real images is an invariant, discriminative pattern. The method computes Spectral Reconstruction Similarity (SRS) from the features of a frozen ViT backbone, aggregates patch-level scores with a Spectral Context Vector (SCV) and a linear-complexity Spectral Context Attention (SCA), and trains a small classifier with binary cross-entropy on 180k real and 180k single-generator fake images. The authors report average AUC 91.0 over 13 generators and 5 real-image sources, a 5.5% absolute improvement over RINE, plus robustness experiments and a runtime/memory analysis showing linear scaling to gigapixel inputs.

Significance. If the reported numbers withstand scrutiny, SPAI would be a useful, generalizing detector with a genuinely practical any-resolution mechanism: SCA is O(K) rather than quadratic, and the supplementary contains a serious runtime and memory evaluation, including gigapixel processing. The paper also ships code, data, and trained models, and the benchmark spans 13 recent generators and 5 real sources, which is a valuable public resource. However, two load-bearing claims are currently not supported: that the method performs self-supervised spectral learning on real images, and that the 5.5% average AUC gain is a reliable, consistent improvement over the state of the art.

major comments (4)
  1. [Sec. 3.1 and Sec. 3.5] The central methodological claim is contradicted by the implementation. Section 3.1 describes training a model G under the pretext task of frequency reconstruction on real images, and the abstract and contributions repeatedly state that SPAI is self-supervised and models the spectral distribution of real images. However, Sec. 3.5 states: 'As our spectral model of real images G we use a ViT-B/16 transformer pre-trained on ImageNet by Xie et al., using a masking radius r = 16. We keep its weights frozen.' Consequently, the paper does not train G on its 180k real images at all; the 'masked spectral learning' is borrowed from a pretrained Masked Frequency Modeling checkpoint. The ablation labeled 'w/o spectral pretraining' in Table 3 therefore tests the value of using MFM pretrained weights versus random initialization, not the value of the proposed frequency-reconstruction pretext task. This does not support the contribution 'we show that the pretext task of frequency reconstruction is an effective approach for modeling the spectral distribution of real images.' The authors should either retrain G on their real-image data under the frequency-reconstruction objective and compare, or substantially reframe the paper as using a fixed pretrained spectral feature extractor.
  2. [Sec. 4.3, Table 4] The headline hyperparameters appear to be selected on the same test set used for the final AUC. Table 4 reports average AUC over the 13-generator/5-real-source test set for the masking radius r, latent dimensionality D, and learning rate, and the values r=16, D=1024, lr=5e-4 are chosen as 'optimal' from this table. The supplementary describes a validation split, but explicitly only for selecting the best epoch, not for selecting hyperparameters. Thus the reported 91.0 average AUC and the 5.5% margin over RINE include test-set selection bias. The authors should state whether any hold-out protocol was used for hyperparameter selection; if not, they should rerun the selection on a validation split and report the corresponding test numbers, or use nested cross-validation. Without this, the central quantitative claim is not yet established.
  3. [Table 1] The claim that SPAI exhibits 'consistently high detection performance across all generative models' is not supported by the per-generator results. Compared with RINE, SPAI is worse on 10 of 13 generators, e.g., Glide 90.2 vs. 95.6, Flux 83.0 vs. 93.0, and GigaGAN 85.4 vs. 92.9. The entire positive average margin comes from three generators, most dramatically SD3 (75.9 vs. 39.1), DALLE3 (90.2 vs. 41.8), and Firefly (96.0 vs. 82.9). The aggregate 5.5% improvement is therefore fragile to the composition of the generator set. The authors should report per-generator comparisons and confidence intervals or significance tests, and soften the 'consistent across all generators' claim unless the per-generator profile can be justified.
  4. [Sec. 3.2 and Sec. 3.5] The out-of-distribution interpretation of SRS is not directly evidenced. The SRS features are computed from a frozen backbone, but the projection operators Pn, the SCV, SCA, and the final classifier are all trained end-to-end with binary cross-entropy on labeled real and fake images (Sec. 3.5). The paper does not show that SRS values, by themselves and without supervised training, separate unseen generators from real images. The conceptual claim that generated images are out-of-distribution samples of a spectral model of real images would be much stronger if the authors reported an unsupervised analysis, e.g., thresholding SRS statistics alone or comparing SRS distributions on held-out generators before any classifier training. As written, SRS may simply be a supervised feature whose usefulness is learned from the specific training generator.
minor comments (4)
  1. [Sec. 3.2] There are several typographical issues in the notation, including 'L = H ·W/p2' where p2 should be p^2, and 'Each token is embedded to a space of sized' which should read 'a space of size d'. These should be corrected.
  2. [Supplementary Sec. 6.1] The description of the projection operators P1 and P2 contains a typo: 'using the projection operators P1(·) : R2D → RD and P1(·) : RD → RD that share the same architecture' should refer to P2 for the second operator.
  3. [Table 4] The hyperparameter tuning table would be more informative if it reported per-generator or per-source breakdowns, or at least the standard deviation across the 13 generators, because the average AUC alone cannot reveal whether a hyperparameter choice is uniformly good or dominated by a few generators.
  4. [Sec. 4.5] The failure-case discussion is honest and useful, but the statement 'Our method successfully detects their early-shared copies' would benefit from reporting the actual confidence scores or detection margins for the shown examples, so the reader can calibrate how close these cases are to the decision boundary.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: SPAI's spectral reconstruction similarity is a feature computed from an externally pretrained frozen backbone, and the reported AUC is measured on generators unseen during training; the self-citations are comparative or contextual, not load-bearing.

full rationale

The derivation chain is self-contained. SPAI uses a frozen ViT-B/16 pretrained externally by Xie et al. [74] with masked frequency modeling on ImageNet as its spectral model G (Sec. 3.5); the SRS score is defined as cosine similarity between G's token representations of original, low-pass, and high-pass versions of an image (Eq. 5), and SRS/SCV/SCA outputs are combined by a three-layer MLP trained with binary cross-entropy on 180k real and 180k synthetic images from Corvi et al. [7] (Sec. 3.5). The headline AUC of 91.0 is then evaluated on 13 generators and 5 real-image sources not used for training (Sec. 4.1, Table 1). No fitted parameter is renamed as a prediction, and no result is defined in terms of the target it is supposed to explain. The self-citations (RINE [40] as a baseline, SIDBench [63] as an evaluation framework, and [37] as context on online synthetic imagery) are not load-bearing for the validity of the method. The main caveats -- hyperparameters r, D, and lr selected using the same 13-generator test set (Table 4) and the fact that SPAI trails RINE on 10 of 13 generators -- are threats to the strength and statistical interpretation of the 5.5% claim, but they are correctness/overfitting concerns rather than circularity, because the reported test AUC is not algebraically forced by the hyperparameter choice.

Assumptions & free parameters 5 free parameters · 5 assumptions · 3 invented entities

The central claim rests on the pretrained MFM backbone (external) and on three new modules (SRS, SCV, SCA) whose only evidence is AUC on the paper's benchmark. The three hyperparameters r, D, lr are tuned on the test set. No new physical or data-generating entities are introduced.

free parameters (5)
  • Masking radius r = 16
    Selected via Tab 4 by maximizing average AUC on the test benchmark; also used to match Xie et al. pretraining. Other radii give 82.4 to 85.0.
  • Latent dimensionality D = 1024
    Selected via Tab 4 on test AUC; alternatives range from 85.3 to 88.8.
  • Learning rate = 5e-4
    Selected via Tab 4 on test AUC; alternatives range from 82.7 to 85.6.
  • SCA hidden dim Dh = 1536
    Empirically set in Sec 6.2; no sweep reported.
  • Number of patches Ktraining = 4
    Chosen to allow training on 256x256 images with 224x224 patches; see Sec 3.5.
assumptions (5)
  • domain assumption The spectral distribution of real images is invariant across acquisition devices, content and processing, changing only with long-term technology.
    Invoked in Sec 3 intro to justify using a single real-spectrum model for all image sources; not independently verified.
  • domain assumption A frequency reconstruction model trained on real images reconstructs real-image frequencies more accurately than AI-generated ones.
    Sec 3.2 states this expectation; it is the basis of SRS and is tested only through end-to-end AUC.
  • domain assumption A frozen ViT pretrained with masked frequency modeling on ImageNet is a sufficient spectral model of real images.
    Sec 3.5 loads Xie et al. weights and freezes them; no training on the paper's 180k real images, so universality is assumed.
  • domain assumption Resizing or cropping images discards high-frequency information essential for detecting AI-generated content.
    Sec 3.4 motivates SCA; not empirically isolated from the effect of resolution in general.
  • standard math 2D DFT, inverse DFT and masking operations are mathematically standard.
    Used in Sec 3.1 for frequency decomposition without proof.
invented entities (3)
  • Spectral Reconstruction Similarity (SRS)
    purpose: Measures alignment between original, low-pass, and high-pass representations as an out-of-distribution score.
    Introduced in Sec 3.2; only evidence is AUC on the benchmark.
  • Spectral Context Vector (SCV)
    purpose: Summarizes block-level spectral context to weight SRS values.
    Sec 3.3; no external validation.
  • Spectral Context Attention (SCA)
    purpose: Pools patch-level spectral vectors at any resolution with O(K) complexity.
    Sec 3.4; runtime analysis in supplementary but no external benchmark.

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Cite this review

Pith. "Pith review of Any-Resolution AI-Generated Image Detection by Spectral Learning." pith.science (2026). https://pith.science/paper/6UYEN253

@misc{pith2026241119417,
  author       = {Pith},
  title        = {Pith review of: Any-Resolution AI-Generated Image Detection by Spectral Learning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6UYEN253}},
  note         = {Machine review of arXiv:2411.19417}
}
read the original abstract

Recent works have established that AI models introduce spectral artifacts into generated images and propose approaches for learning to capture them using labeled data. However, the significant differences in such artifacts among different generative models hinder these approaches from generalizing to generators not seen during training. In this work, we build upon the key idea that the spectral distribution of real images constitutes both an invariant and highly discriminative pattern for AI-generated image detection. To model this under a self-supervised setup, we employ masked spectral learning using the pretext task of frequency reconstruction. Since generated images constitute out-of-distribution samples for this model, we propose spectral reconstruction similarity to capture this divergence. Moreover, we introduce spectral context attention, which enables our approach to efficiently capture subtle spectral inconsistencies in images of any resolution. Our spectral AI-generated image detection approach (SPAI) achieves a 5.5% absolute improvement in AUC over the previous state-of-the-art across 13 recent generative approaches, while exhibiting robustness against common online perturbations. Code is available on https://mever-team.github.io/spai.

Figures

Figures reproduced from arXiv: 2411.19417 by the authors.

Figure 1
Figure 1. SPAI employs spectral learning to learn the spectral dis [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Overview of the SPAI approach. We learn a model of the spectral distribution of real images under a self-supervised setup using [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Robustness evaluation on common perturbations. Average AUC is presented over the perturbed versions of two sources of real [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figures from the paper (22 more)
Figure 4
Figure 4. Figure 4: Qualitative analysis of spectral context attention. A [PITH_FULL_IMAGE:figures/full_fig_p008_4.png]
Figure 5
Figure 5. Figure 5: Failures in detecting derivative AI-generated images. [PITH_FULL_IMAGE:figures/full_fig_p008_5.png]
Figure 6
Figure 6. Figure 6: Runtime comparison between vision transformer’s self [PITH_FULL_IMAGE:figures/full_fig_p014_6.png]
Figure 7
Figure 7. Figure 7: t-SNE embeddings for the spectral context vector (SCV) [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Accurately detected Flux images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Accurately detected GigaGAN images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Accurately detected MidJourney-v6.1 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p017_10.png]
Figure 11
Figure 11. Figure 11: Accurately detected Stable Diffusion 3 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p017_11.png]
Figure 12
Figure 12. Figure 12: Accurately detected DALLE2 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p018_12.png]
Figure 13
Figure 13. Figure 13: Accurately detected DALLE3 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p018_13.png]
Figure 14
Figure 14. Figure 14: Accurately detected Firefly images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p018_14.png]
Figure 15
Figure 15. Figure 15: Accurately detected Glide images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p018_15.png]
Figure 16
Figure 16. Figure 16: Accurately detected MidJourney-v5 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p019_16.png]
Figure 17
Figure 17. Figure 17: Accurately detected Stable Diffusion 1.3 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p019_17.png]
Figure 18
Figure 18. Figure 18: Accurately detected Stable Diffusion 1.4 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p019_18.png]
Figure 19
Figure 19. Figure 19: Accurately detected Stable Diffusion 2 images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p019_19.png]
Figure 20
Figure 20. Figure 20: Accurately detected Stable Diffusion XL images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p020_20.png]
Figure 21
Figure 21. Figure 21: Accurately classified real images from COCO. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p020_21.png]
Figure 22
Figure 22. Figure 22: Accurately classified real images from FODB. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p020_22.png]
Figure 23
Figure 23. Figure 23: Accurately classified real images from ImageNet. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p020_23.png]
Figure 24
Figure 24. Figure 24: Accurately classified real images from Open Images. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p021_24.png]
Figure 25
Figure 25. Figure 25: Accurately classified real images from RAISE-1k. For illustration purposes cropped to a square aspect ratio. [PITH_FULL_IMAGE:figures/full_fig_p021_25.png]

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Reference graph

Works this paper leans on

89 extracted references · 47 canonical work pages

  1. [1]

    Synthbuster: Towards detection of diffu- sion model generated images

    Quentin Bammey. Synthbuster: Towards detection of diffu- sion model generated images. IEEE Open Journal of Signal Processing, 2023. 1, 2, 3, 5, 6

    2023

  2. [2]

    Improving image generation with better captions

    James Betker, Gabriel Goh, Li Jing, Tim Brooks, Jianfeng Wang, Linjie Li, Long Ouyang, Juntang Zhuang, Joyce Lee, Yufei Guo, et al. Improving image generation with better captions. Computer Science. https://cdn. openai. com/papers/dall-e-3. pdf, 2(3):8, 2023. 2, 6

    2023

  3. [3]

    A geometric and photo- metric exploration of gan and diffusion synthesized faces

    Matyáš Bohá ˇcek and Hany Farid. A geometric and photo- metric exploration of gan and diffusion synthesized faces. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 874–883, 2023. 2

    2023

  4. [4]

    Controllable generation with text-to-image diffusion models: A survey

    Pu Cao, Feng Zhou, Qing Song, and Lu Yang. Controllable generation with text-to-image diffusion models: A survey. arXiv preprint arXiv:2403.04279, 2024. 1

    arXiv 2024

  5. [5]

    Fakeinversion: Learning to detect images from un- seen text-to-image models by inverting stable diffusion

    George Cazenavette, Avneesh Sud, Thomas Leung, and Ben Usman. Fakeinversion: Learning to detect images from un- seen text-to-image models by inverting stable diffusion. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 10759–10769, 2024. 3

    2024

  6. [6]

    Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation

    Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo. Stargan: Unified genera- tive adversarial networks for multi-domain image-to-image translation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8789–8797,

  7. [7]

    On the detection of synthetic images generated by diffusion mod- els

    Riccardo Corvi, Davide Cozzolino, Giada Zingarini, Gio- vanni Poggi, Koki Nagano, and Luisa Verdoliva. On the detection of synthetic images generated by diffusion mod- els. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1–5. IEEE, 2023. 2, 3, 5, 6, 1

    2023

  8. [8]

    Raising the bar of ai-generated image detection with clip

    Davide Cozzolino, Giovanni Poggi, Riccardo Corvi, Matthias Nießner, and Luisa Verdoliva. Raising the bar of ai-generated image detection with clip. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4356–4366, 2024. 3

    2024

Show all 89 references
  1. [9]

    Zero-shot detection of ai-generated images

    Davide Cozzolino, Giovanni Poggi, Matthias Nießner, and Luisa Verdoliva. Zero-shot detection of ai-generated images. In European Conference on Computer Vision, pages 54–72. Springer, 2025. 1, 2, 3

    2025

  2. [10]

    Diffusion models in vision: A survey

    Florinel-Alin Croitoru, Vlad Hondru, Radu Tudor Ionescu, and Mubarak Shah. Diffusion models in vision: A survey. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 45(9):10850–10869, 2023. 1

    2023

  3. [11]

    Raise: A raw images dataset for digital image forensics

    Duc-Tien Dang-Nguyen, Cecilia Pasquini, Valentina Conot- ter, and Giulia Boato. Raise: A raw images dataset for digital image forensics. In Proceedings of the 6th ACM multimedia systems conference, pages 219–224, 2015. 6, 1

    2015

  4. [12]

    Flashattention: Fast and memory-efficient exact at- tention with io-awareness

    Tri Dao, Dan Fu, Stefano Ermon, Atri Rudra, and Christo- pher Ré. Flashattention: Fast and memory-efficient exact at- tention with io-awareness. Advances in Neural Information Processing Systems, 35:16344–16359, 2022. 2

    2022

  5. [13]

    A re- view on generative adversarial networks for image genera- tion

    Vinicius Luis Trevisan De Souza, Bruno Augusto Dorta Mar- ques, Harlen Costa Batagelo, and João Paulo Gois. A re- view on generative adversarial networks for image genera- tion. Computers & Graphics, 114:13–25, 2023. 1

    2023

  6. [14]

    Imagenet: A large-scale hierarchical image database

    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition, pages 248–255. Ieee, 2009. 5, 6, 1

    2009

  7. [15]

    Fre- quency masking for universal deepfake detection

    Chandler Timm Doloriel and Ngai-Man Cheung. Fre- quency masking for universal deepfake detection. InICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , pages 13466– 13470. IEEE, 2024. 2

    2024

  8. [16]

    An image is worth 16x16 words: Transformers for image recognition at scale

    Alexey Dosovitskiy. An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929, 2020. 4, 5, 1, 2

    2010 arXiv

  9. [17]

    Ammeba: A large-scale survey and dataset of media-based misinformation in-the-wild

    Nicholas Dufour, Arkanath Pathak, Pouya Samangouei, Nikki Hariri, Shashi Deshetti, Andrew Dudfield, Christo- pher Guess, Pablo Hernández Escayola, Bobby Tran, Mevan Babakar, et al. Ammeba: A large-scale survey and dataset of media-based misinformation in-the-wild. arXiv prepri...

    2024 arXiv

  10. [18]

    Watch your up-convolution: Cnn based generative deep neural net- works are failing to reproduce spectral distributions

    Ricard Durall, Margret Keuper, and Janis Keuper. Watch your up-convolution: Cnn based generative deep neural net- works are failing to reproduce spectral distributions. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 7890–7899, 2020. 2

    2020

  11. [19]

    Image generation: A review

    Mohamed Elasri, Omar Elharrouss, Somaya Al-Maadeed, and Hamid Tairi. Image generation: A review. Neural Pro- cessing Letters, 54(5):4609–4646, 2022. 1

    2022

  12. [20]

    Scaling recti- fied flow transformers for high-resolution image synthesis

    Patrick Esser, Sumith Kulal, Andreas Blattmann, Rahim Entezari, Jonas Müller, Harry Saini, Yam Levi, Dominik Lorenz, Axel Sauer, Frederic Boesel, et al. Scaling recti- fied flow transformers for high-resolution image synthesis. In Forty-first International Conference on Machin...

    2024

  13. [21]

    Lighting (in) consistency of paint by text

    Hany Farid. Lighting (in) consistency of paint by text. arXiv preprint arXiv:2207.13744, 2022. 2

    2022 arXiv

  14. [22]

    Perspective (in) consistency of paint by text

    Hany Farid. Perspective (in) consistency of paint by text. arXiv preprint arXiv:2206.14617, 2022. 2

    2022 arXiv

  15. [23]

    Computer vision: a mod- ern approach

    David A Forsyth and Jean Ponce. Computer vision: a mod- ern approach. prentice hall professional technical reference,

  16. [24]

    Leveraging fre- quency analysis for deep fake image recognition

    Joel Frank, Thorsten Eisenhofer, Lea Schönherr, Asja Fis- cher, Dorothea Kolossa, and Thorsten Holz. Leveraging fre- quency analysis for deep fake image recognition. In Inter- national conference on machine learning, pages 3247–3258. PMLR, 2020. 6, 3

    2020

  17. [25]

    Huggingface: Geroldmeisinger / laion2b- en-a65 cogvlm2-4bit captions, 2024

    GeroldMeisinger. Huggingface: Geroldmeisinger / laion2b- en-a65 cogvlm2-4bit captions, 2024. accessed 11th Nov

    2024

  18. [26]

    Generative adversarial nets

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and 9 Yoshua Bengio. Generative adversarial nets. Advances in neural information processing systems, 27, 2014. 2

    2014

  19. [27]

    Generative adversarial networks

    Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. Generative adversarial networks. Commu- nications of the ACM, 63(11):139–144, 2020. 2

    2020

  20. [28]

    Draw: A recurrent neural network for image generation

    Karol Gregor, Ivo Danihelka, Alex Graves, Danilo Rezende, and Daan Wierstra. Draw: A recurrent neural network for image generation. In International conference on machine learning, pages 1462–1471. PMLR, 2015. 2

    2015

  21. [29]

    Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models

    Yuchao Gu, Xintao Wang, Jay Zhangjie Wu, Yujun Shi, Yun- peng Chen, Zihan Fan, Wuyou Xiao, Rui Zhao, Shuning Chang, Weijia Wu, et al. Mix-of-show: Decentralized low- rank adaptation for multi-concept customization of diffusion models. Advances in Neural Information Processing ...

    2024

  22. [30]

    The forchheim im- age database for camera identification in the wild

    Benjamin Hadwiger and Christian Riess. The forchheim im- age database for camera identification in the wild. In Pat- tern Recognition. ICPR International Workshops and Chal- lenges: Virtual Event, January 10–15, 2021, Proceedings, Part VI, pages 500–515. Springer, 2021. 6, 1

    2021

  23. [31]

    Rigid: A training-free and model-agnostic framework for ro- bust ai-generated image detection

    Zhiyuan He, Pin-Yu Chen, and Tsung-Yi Ho. Rigid: A training-free and model-agnostic framework for ro- bust ai-generated image detection. arXiv preprint arXiv:2405.20112, 2024. 2, 3

    2024 arXiv

  24. [32]

    Gaussian error linear units (gelus)

    Dan Hendrycks and Kevin Gimpel. Gaussian error linear units (gelus). arXiv preprint arXiv:1606.08415, 2016. 4, 1

    2016 arXiv

  25. [33]

    Denoising dif- fusion probabilistic models

    Jonathan Ho, Ajay Jain, and Pieter Abbeel. Denoising dif- fusion probabilistic models. Advances in neural information processing systems, 33:6840–6851, 2020. 2

    2020

  26. [34]

    Lora: Low-rank adaptation of large language models

    Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen- Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. Lora: Low-rank adaptation of large language models. arXiv preprint arXiv:2106.09685, 2021. 1, 2

    2021 arXiv

  27. [35]

    Fusing global and local features for gen- eralized ai-synthesized image detection

    Yan Ju, Shan Jia, Lipeng Ke, Hongfei Xue, Koki Nagano, and Siwei Lyu. Fusing global and local features for gen- eralized ai-synthesized image detection. In 2022 IEEE In- ternational Conference on Image Processing (ICIP) , pages 3465–3469. IEEE, 2022. 2, 6, 3

    2022

  28. [36]

    Scal- ing up gans for text-to-image synthesis

    Minguk Kang, Jun-Yan Zhu, Richard Zhang, Jaesik Park, Eli Shechtman, Sylvain Paris, and Taesung Park. Scal- ing up gans for text-to-image synthesis. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 10124–10134, 2023. 2, 6, 1

    2023

  29. [37]

    Evolution of detection performance through- out the online lifespan of synthetic images

    Dimitrios Karageorgiou, Quentin Bammey, Valentin Por- cellini, Bertrand Goupil, Denis Teyssou, and Symeon Pa- padopoulos. Evolution of detection performance through- out the online lifespan of synthetic images. arXiv preprint arXiv:2408.11541, 2024. 1

    2024 arXiv

  30. [38]

    Autolora: Autoguid- ance meets low-rank adaptation for diffusion models

    Artur Kasymov, Marcin Sendera, Michał Stypułkowski, Ma- ciej Zi˛ eba, and Przemysław Spurek. Autolora: Autoguid- ance meets low-rank adaptation for diffusion models. arXiv preprint arXiv:2410.03941, 2024. 1

    2024 arXiv

  31. [39]

    Texturecrop: Enhancing synthetic image de- tection through texture-based cropping

    Despina Konstantinidou, Christos Koutlis, and Symeon Pa- padopoulos. Texturecrop: Enhancing synthetic image de- tection through texture-based cropping. arXiv preprint arXiv:2407.15500, 2024. 2

    2024 arXiv

  32. [40]

    Leveraging rep- resentations from intermediate encoder-blocks for synthetic image detection

    Christos Koutlis and Symeon Papadopoulos. Leveraging rep- resentations from intermediate encoder-blocks for synthetic image detection. In Computer Vision – ECCV 2024 , pages 394–411, Cham, 2025. Springer Nature Switzerland. 3, 5, 6

    2024

  33. [41]

    Towards explain- ing image-based distribution shifts

    Sean Kulinski and David I Inouye. Towards explain- ing image-based distribution shifts. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4788–4792, 2022. 3

    2022

  34. [42]

    The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale

    Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Ui- jlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, et al. The open images dataset v4: Unified image classification, object detection, and visual relationship detection...

    1956

  35. [43]

    Civitai: Dataset with 6000+ flux.1 dev images,

    LatentSpacer. Civitai: Dataset with 6000+ flux.1 dev images,

  36. [44]

    Layer normalization

    Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. ArXiv e-prints , pages arXiv–1607,

  37. [45]

    Art: Automatic red-teaming for text- to-image models to protect benign users

    Guanlin Li, Kangjie Chen, Shudong Zhang, Jie Zhang, and Tianwei Zhang. Art: Automatic red-teaming for text- to-image models to protect benign users. arXiv preprint arXiv:2405.19360, 2024. 1

    2024 arXiv

  38. [46]

    Masksim: Detection of syn- thetic images by masked spectrum similarity analysis

    Yanhao Li, Quentin Bammey, Marina Gardella, Tina Nikoukhah, Jean-Michel Morel, Miguel Colom, and Rafael Grompone V on Gioi. Masksim: Detection of syn- thetic images by masked spectrum similarity analysis. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Patte...

    2024

  39. [47]

    Detecting multimedia generated by large ai models: A survey

    Li Lin, Neeraj Gupta, Yue Zhang, Hainan Ren, Chun-Hao Liu, Feng Ding, Xin Wang, Xin Li, Luisa Verdoliva, and Shu Hu. Detecting multimedia generated by large ai models: A survey. arXiv preprint arXiv:2402.00045, 2024. 1

    2024 arXiv

  40. [48]

    Microsoft coco: Common objects in context

    Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. Microsoft coco: Common objects in context. In Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings...

    2014

  41. [49]

    Global tex- ture enhancement for fake face detection in the wild

    Zhengzhe Liu, Xiaojuan Qi, and Philip HS Torr. Global tex- ture enhancement for fake face detection in the wild. In Pro- ceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8060–8069, 2020. 2, 6, 3

    2020

  42. [50]

    Decoupled weight decay regularization

    I Loshchilov. Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101, 2017. 5, 1

    2017 arXiv

  43. [51]

    Misalignment-robust frequency distribution loss for image transformation

    Zhangkai Ni, Juncheng Wu, Zian Wang, Wenhan Yang, Hanli Wang, and Lin Ma. Misalignment-robust frequency distribution loss for image transformation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 2910–2919, 2024. 3

    2024

  44. [52]

    Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021

    Alex Nichol, Prafulla Dhariwal, Aditya Ramesh, Pranav Shyam, Pamela Mishkin, Bob McGrew, Ilya Sutskever, and Mark Chen. Glide: Towards photorealistic image generation and editing with text-guided diffusion models.arXiv preprint arXiv:2112.10741, 2021. 5 10

    2021 arXiv

  45. [53]

    Towards uni- versal fake image detectors that generalize across genera- tive models

    Utkarsh Ojha, Yuheng Li, and Yong Jae Lee. Towards uni- versal fake image detectors that generalize across genera- tive models. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 24480– 24489, 2023. 3, 6

    2023

  46. [54]

    Dinov2: Learning robust visual features without supervision

    Maxime Oquab, Timothée Darcet, Théo Moutakanni, Huy V o, Marc Szafraniec, Vasil Khalidov, Pierre Fernandez, Daniel Haziza, Francisco Massa, Alaaeldin El-Nouby, et al. Dinov2: Learning robust visual features without supervision. arXiv preprint arXiv:2304.07193, 2023. 6, 7

    2023 arXiv

  47. [55]

    Semantic image synthesis with spatially-adaptive nor- malization

    Taesung Park, Ming-Yu Liu, Ting-Chun Wang, and Jun-Yan Zhu. Semantic image synthesis with spatially-adaptive nor- malization. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 2337–2346,

  48. [56]

    Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis

    Dustin Podell, Zion English, Kyle Lacey, Andreas Blattmann, Tim Dockhorn, Jonas Müller, Joe Penna, and Robin Rombach. Sdxl: Improving latent diffusion mod- els for high-resolution image synthesis. arXiv preprint arXiv:2307.01952, 2023. 2, 5

    2023 arXiv

  49. [57]

    Learning transferable visual models from natural language supervi- sion

    Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al. Learning transferable visual models from natural language supervi- sion. In International conference on machine learning, ...

    2021

  50. [58]

    Hierarchical text-conditional image gener- ation with clip latents

    Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen. Hierarchical text-conditional image gener- ation with clip latents. arXiv preprint arXiv:2204.06125, 1 (2):3, 2022. 5

    2022 arXiv

  51. [59]

    Aer- oblade: Training-free detection of latent diffusion images using autoencoder reconstruction error

    Jonas Ricker, Denis Lukovnikov, and Asja Fischer. Aer- oblade: Training-free detection of latent diffusion images using autoencoder reconstruction error. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 9130–9140, 2024. 3

    2024

  52. [60]

    High-resolution image synthesis with latent diffusion models

    Robin Rombach, Andreas Blattmann, Dominik Lorenz, Patrick Esser, and Björn Ommer. High-resolution image synthesis with latent diffusion models. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 10684–10695, 2022. 2, 5

    2022

  53. [61]

    Huggingface: saq1b/midjourney-v6.1, 2024

    saq1b. Huggingface: saq1b/midjourney-v6.1, 2024. ac- cessed 11th Nov. 2024. 6, 1

    2024

  54. [62]

    Shad- ows don’t lie and lines can’t bend! generative models don’t know projective geometry

    Ayush Sarkar, Hanlin Mai, Amitabh Mahapatra, Svetlana Lazebnik, David A Forsyth, and Anand Bhattad. Shad- ows don’t lie and lines can’t bend! generative models don’t know projective geometry... for now. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern R...

    2024

  55. [63]

    Sidbench: A python framework for reliably assessing synthetic image de- tection methods

    Manos Schinas and Symeon Papadopoulos. Sidbench: A python framework for reliably assessing synthetic image de- tection methods. arXiv preprint arXiv:2404.18552, 2024. 6

    2024 arXiv

  56. [64]

    De-fake: Detection and attribution of fake images generated by text- to-image generation models

    Zeyang Sha, Zheng Li, Ning Yu, and Yang Zhang. De-fake: Detection and attribution of fake images generated by text- to-image generation models. In Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communica- tions Security, pages 3418–3432, 2023. 3, 6

    2023

  57. [65]

    Deep unsupervised learning using nonequilibrium thermodynamics

    Jascha Sohl-Dickstein, Eric Weiss, Niru Maheswaranathan, and Surya Ganguli. Deep unsupervised learning using nonequilibrium thermodynamics. In International confer- ence on machine learning, pages 2256–2265. PMLR, 2015. 2

    2015

  58. [66]

    Learning on gradients: Generalized arti- facts representation for gan-generated images detection

    Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, and Yunchao Wei. Learning on gradients: Generalized arti- facts representation for gan-generated images detection. In Proceedings of the IEEE/CVF Conference on Computer Vi- sion and Pattern Recognition, pages 12105–12114, 20...

    2023

  59. [67]

    Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection

    Chuangchuang Tan, Yao Zhao, Shikui Wei, Guanghua Gu, Ping Liu, and Yunchao Wei. Rethinking the up-sampling op- erations in cnn-based generative network for generalizable deepfake detection. In Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition,...

    2024

  60. [68]

    Df-gan: Deep fusion gener- ative adversarial networks for text-to-image synthesis

    Ming Tao, Hao Tang, Songsong Wu, Nicu Sebe, Xiao-Yuan Jing, Fei Wu, and Bingkun Bao. Df-gan: Deep fusion gener- ative adversarial networks for text-to-image synthesis. arXiv preprint arXiv:2008.05865, 2(6), 2020. 2

    2008 arXiv

  61. [69]

    Synthetic image verification in the era of generative ai: What works and what isn’t there yet

    Diangarti Tariang, Riccardo Corvi, Davide Cozzolino, Gio- vanni Poggi, Koki Nagano, and Luisa Verdoliva. Synthetic image verification in the era of generative ai: What works and what isn’t there yet. arXiv preprint arXiv:2405.00196,

    arXiv

  62. [70]

    Visualizing data using t-sne

    Laurens Van der Maaten and Geoffrey Hinton. Visualizing data using t-sne. Journal of machine learning research , 9 (11), 2008. 2

    2008

  63. [71]

    Attention is all you need

    A Vaswani. Attention is all you need. Advances in Neural Information Processing Systems, 2017. 4, 5, 1

    2017

  64. [72]

    Cnn-generated images are surprisingly easy to spot

    Sheng-Yu Wang, Oliver Wang, Richard Zhang, Andrew Owens, and Alexei A Efros. Cnn-generated images are surprisingly easy to spot... for now. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pages 8695–8704, 2020. 1, 2, 6, 3

    2020

  65. [73]

    Dire for diffusion-generated image detection

    Zhendong Wang, Jianmin Bao, Wengang Zhou, Weilun Wang, Hezhen Hu, Hong Chen, and Houqiang Li. Dire for diffusion-generated image detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision , pages 22445–22455, 2023. 3, 6

    2023

  66. [74]

    Masked frequency model- ing for self-supervised visual pre-training

    Jiahao Xie, Wei Li, Xiaohang Zhan, Ziwei Liu, Yew Soon Ong, and Chen Change Loy. Masked frequency model- ing for self-supervised visual pre-training. arXiv preprint arXiv:2206.07706, 2022. 3, 4, 5, 6

    2022 arXiv

  67. [75]

    Attngan: Fine- grained text to image generation with attentional generative adversarial networks

    Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He. Attngan: Fine- grained text to image generation with attentional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition , page...

    2018

  68. [76]

    A sanity check for ai-generated image detection

    Shilin Yan, Ouxiang Li, Jiayin Cai, Yanbin Hao, Xi- aolong Jiang, Yao Hu, and Weidi Xie. A sanity check for ai-generated image detection. arXiv preprint arXiv:2406.19435, 2024. 3

    2024 arXiv

  69. [77]

    Hiri-vit: Scaling vision transformer with high resolution inputs.IEEE Transactions on Pattern Analysis and Machine Intelligence,

    Ting Yao, Yehao Li, Yingwei Pan, and Tao Mei. Hiri-vit: Scaling vision transformer with high resolution inputs.IEEE Transactions on Pattern Analysis and Machine Intelligence,

  70. [78]

    Text-to-image diffusion models in gener- ative ai: A survey

    Chenshuang Zhang, Chaoning Zhang, Mengchun Zhang, and In So Kweon. Text-to-image diffusion models in gener- ative ai: A survey. arXiv preprint arXiv:2303.07909, 2023. 2

    2023 arXiv

  71. [79]

    Patchcraft: Exploring texture patch for efficient ai-generated image detection

    Nan Zhong, Yiran Xu, Sheng Li, Zhenxing Qian, and Xinpeng Zhang. Patchcraft: Exploring texture patch for efficient ai-generated image detection. arXiv preprint arXiv:2311.12397, pages 1–18, 2024. 2, 6, 3

    2024 arXiv

  72. [80]

    Unpaired image-to-image translation using cycle- consistent adversarial networks

    Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros. Unpaired image-to-image translation using cycle- consistent adversarial networks. In Proceedings of the IEEE international conference on computer vision , pages 2223– 2232, 2017. 2

    2017

  73. [81]

    Dm-gan: Dynamic memory generative adversarial networks for text- to-image synthesis

    Minfeng Zhu, Pingbo Pan, Wei Chen, and Yi Yang. Dm-gan: Dynamic memory generative adversarial networks for text- to-image synthesis. In Proceedings of the IEEE/CVF con- ference on computer vision and pattern recognition , pages 5802–5810, 2019. 2 12 Any-Resolution AI-Generated...

    2019

  74. [83]

    Evaluation Setup in Details To better facilitate the reproducibility of our results, in this paragraph, we provide a more extensive description of the implementation of our evaluation setup. 6.1. Projection Operators Pn: In our architecture we employed N projection opera- tors...

  75. [84]

    Runtime comparison between vision transformer’s self- attention and spectral context attention for different image resolu- tions

    Runtime Analysis To evaluate the computational performance of our approach we analyze its runtime using the proposed spectral con- text attention as well as by solely relying on the scaled dot 1 Figure 6. Runtime comparison between vision transformer’s self- attention and spec...

  76. [85]

    Feature Space Analysis To study the effect of our key architectural components in the feature space we embed the spectral context vector (SCV) zC, the spectral reconstruction similarity (SRS) val- ues zλ and the image-level spectral vector zS generated by spectral context atte...

  77. [86]

    We report the results across our test set of 5 sources of real images and 13 generative models in Tab

    Additional Metrics To study the calibration of our approach with respect to the state-of-the-art detectors as well as to facilitate comparison across popular metrics in the field, we expand the analy- sis of the main paper by computing the balanced accuracy on the 0.5 threshol...

  78. [87]

    Ethical Considerations Introducing an approach for distinguishing AI-generated content from real one intends to prevent the malicious ex- ploitation of generative AI. However, any detection method, will inevitably fail to correctly predict some cases, allowing malicious actors...

  79. [88]

    8 to 20 as well as for the 5 sources of real images in Figs

    Qualitative Evaluation We perform a qualitative evaluation of our approach across all the considered datasets and present samples for the 13 generative models in Figs. 8 to 20 as well as for the 5 sources of real images in Figs. 21 to 25. As we see, our ap- proach accurately d...

  80. [89]

    3 Stable Diffusion 1.4 (a) SCV (b) SRS (c) SCA Stable Diffusion XL (a) SCV (b) SRS (c) SCA MidJourney-v5 (a) SCV (b) SRS (c) SCA Figure 7

    Source Code To facilitate the reproduction of our results as well as fur- ther research in the field we make publicly available our source code, data and trained models on https://mever- team.github.io/spai. 3 Stable Diffusion 1.4 (a) SCV (b) SRS (c) SCA Stable Diffusion XL (a...

  81. [2024]

    accessed 11th Nov. 2024. 5, 1

    2024

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.