pith. sign in

arxiv: 2606.25297 · v1 · pith:TY6E55LNnew · submitted 2026-06-24 · 💻 cs.CV

Minimalist Preprocessing Approach for Image Synthesis Detection

Hoai-Danh Vo , Trung-Nghia Le This is my paper

Pith reviewed 2026-06-25 21:39 UTC · model grok-4.3

classification 💻 cs.CV
keywords image synthesis detectiondeepfake detectiongrayscale gradienthigh-pass filterlow-resource detectionpixel fluctuationminimalist preprocessing
0
0 comments X

The pith

Computing the gradient of grayscale intensity between neighboring pixels detects synthesized images at accuracy levels comparable to complex methods but with far lower computational cost.

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

The paper presents a minimalist preprocessing step for distinguishing real images from those produced by generative models. It computes the gradient of grayscale intensity values between adjacent pixels to capture local fluctuations, which the authors treat as a high-pass filter that reduces the influence of color and semantic content. The central goal is to achieve detection performance on par with state-of-the-art techniques while using minimal resources, thereby enabling deployment on smartphones and other low-end hardware. Experiments across multiple datasets are offered as evidence that the approach generalizes sufficiently for practical use.

Core claim

The central claim is that calculating the gradient of grayscale intensity between neighboring pixels produces discriminative features sufficient to distinguish synthesized images from authentic ones. This computation acts as a high-pass filter that emphasizes pixel variations while minimizing color influence, allowing the method to reach accuracy levels comparable to state-of-the-art techniques across multiple datasets and generative models while requiring only minimal computational resources.

What carries the argument

The gradient of grayscale intensity between neighboring pixels, used as a high-pass filter to highlight local fluctuations and reduce color influence.

If this is right

  • Detection becomes feasible on smartphones and other resource-constrained devices.
  • The preprocessing step can be applied before feeding images into lightweight classifiers.
  • Color channels can be ignored without major loss of detection performance.
  • The approach scales to new generative models if local pixel fluctuations remain a reliable signal.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Mobile apps could integrate the filter for on-device screening of shared images before upload.
  • The finding implies that many synthesis artifacts manifest in local intensity changes rather than global statistics.
  • Similar gradient-based filters might be tested on video frames or other modalities for forgery detection.

Load-bearing premise

That the gradient of grayscale intensity between neighboring pixels alone yields features that remain discriminative across datasets and different generative models without color or semantic information.

What would settle it

A new dataset of images generated by advanced models that preserve local grayscale gradients closely, where the method's reported accuracy falls substantially below current state-of-the-art detectors.

Figures

Figures reproduced from arXiv: 2606.25297 by Hoai-Danh Vo, Trung-Nghia Le.

Figure 1
Figure 1. Figure 1: Comparison of different synthetic image detection methods on the Ojha dataset [28]. Our proposed method is simple yet efficient, significantly reduces FLOPs, total parameters, while achieving comparable accuracy state-of-the-art methods. deepfake technology, especially for harmful content like revenge and celebrity pornography. To address the threats posed by synthetic images on digital com￾munication plat… view at source ↗
Figure 2
Figure 2. Figure 2: The left image represents the original image, the middle shows the gradient calculation applied, and the right image illustrates the resulting gradient map. investigate and evaluate the impact of noise on adjacent pixels, we designed a simple yet effective filter to capture these variations. This filter aids the CNN in learning the differences in noise distribution between real and synthetic im￾ages. The o… view at source ↗
Figure 3
Figure 3. Figure 3: Architecture of our lightweight model. vertical direction. To determine the gradient magnitude and orientation, these values are computed from Dx and Dy. Gm(x, y) = q Dx(x, y) 2 + Dy(x, y) 2, (3) Go(x, y) = arctan  Dy(x, y) Dx(x, y)  , (4) where Dx(x, y) and Dy(x, y) are as previously defined. The gradient orientation Go, which represents the overall angle of gray-level changes at a pixel and indi￾cates … view at source ↗
Figure 4
Figure 4. Figure 4: Standard pipeline for Image Synthesis Methods [PITH_FULL_IMAGE:figures/full_fig_p009_4.png] view at source ↗
read the original abstract

Generative models have significantly advanced image generation, resulting in synthesized images that are increasingly indistinguishable from authentic ones. However, the creation of fake images with malicious intent is a growing concern. Low-configured smart devices have become highly popular, making it easier for deceptive images to reach users. Consequently, the demand for effective detection methods is increasingly urgent. In this paper, we introduce a simple yet efficient method that captures pixel fluctuations between neighboring pixels by calculating the gradient, which highlights variations in grayscale intensity. This approach functions as a high-pass filter, emphasizing key features for accurate image distinction while minimizing color influence. Our experiments on multiple datasets demonstrate that our method achieves accuracy levels comparable to state-of-the-art techniques while requiring minimal computational resources. Therefore, it is suitable for deployment on low-end devices such as smartphones. The code is available at https://github.com/vohoaidanh/adof.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper proposes a minimalist preprocessing technique for image synthesis detection that computes the gradient of grayscale intensity between neighboring pixels, functioning as a high-pass filter to highlight fluctuations while reducing color influence. It asserts that experiments on multiple datasets achieve accuracy comparable to state-of-the-art methods with low computational cost, making the approach suitable for deployment on low-end devices such as smartphones. Code is provided via GitHub.

Significance. If the performance claims are substantiated, the work could contribute a practical, resource-efficient preprocessing step for on-device synthetic image detection, addressing accessibility needs in combating deepfakes on consumer hardware. The focus on simplicity and the public code release support reproducibility.

major comments (2)
  1. [Abstract] Abstract: The central claim that experiments 'achieve accuracy levels comparable to state-of-the-art techniques' on 'multiple datasets' is unsupported by any reported quantitative metrics, baseline comparisons, dataset names, or error analysis, rendering the empirical generalization claim unevaluable.
  2. [Method] Method section: The grayscale-only gradient is presented as sufficient without any ablation restoring color channels or testing generators with known chromatic artifacts (e.g., hue/saturation inconsistencies), directly undermining the sufficiency assumption for the claimed cross-generator performance.
minor comments (1)
  1. [Abstract] The abstract references 'our experiments' without even high-level details on datasets or models, which should be summarized for clarity even if full results appear later.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: The central claim that experiments 'achieve accuracy levels comparable to state-of-the-art techniques' on 'multiple datasets' is unsupported by any reported quantitative metrics, baseline comparisons, dataset names, or error analysis, rendering the empirical generalization claim unevaluable.

    Authors: We agree that the abstract would be strengthened by including specific quantitative support for the claim. The manuscript reports results across multiple datasets with accuracy figures comparable to referenced SOTA methods; we will revise the abstract to explicitly state key accuracy values, name the datasets, and reference the baseline comparisons from the experimental section. revision: yes

  2. Referee: [Method] Method section: The grayscale-only gradient is presented as sufficient without any ablation restoring color channels or testing generators with known chromatic artifacts (e.g., hue/saturation inconsistencies), directly undermining the sufficiency assumption for the claimed cross-generator performance.

    Authors: The method is intentionally minimalist and uses grayscale conversion to minimize color influence, as color statistics can differ across generators; the gradient computation is argued to capture intensity fluctuations that are generator-agnostic. We acknowledge that an explicit ablation would provide stronger evidence for sufficiency. We will add an ablation study comparing grayscale versus full-color input in the revised manuscript. revision: yes

Circularity Check

0 steps flagged

No circularity: fixed preprocessing with external empirical support

full rationale

The paper describes a fixed, non-learned preprocessing step (grayscale gradient computation) without equations, parameter fitting, or derivations. Claims of comparable accuracy rest on reported experiments across datasets rather than any self-referential reduction. No self-citations, ansatzes, or uniqueness theorems are invoked to justify the method. This is the common case of an empirical method whose validity is tested externally.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

Review performed on abstract alone; no explicit free parameters, invented entities, or non-standard axioms are stated. The central premise is treated as a domain assumption about gradient utility.

axioms (1)
  • domain assumption Gradient of grayscale intensity between neighboring pixels produces features that distinguish synthesized from authentic images
    This premise underpins the entire preprocessing claim and is invoked without further justification in the abstract.

pith-pipeline@v0.9.1-grok · 5676 in / 1036 out tokens · 26279 ms · 2026-06-25T21:39:09.868032+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

40 extracted references · 7 canonical work pages · 3 internal anchors

  1. [1]

    New J Chem35(5) (2013)

    Arunachalam, S., Khairnar, S., Desale, B.: The fast fourier transform algorithm and its application in digital image processing. New J Chem35(5) (2013)

    2013

  2. [2]

    arXiv:2107.10139 (2021)

    Bauer, L.A., Bindschaedler, V.: Generative models for security: Attacks, defenses, and opportunities. arXiv:2107.10139 (2021)

    work page arXiv 2021

  3. [3]

    Cara Curtis: California makes deepfakes illegal to curb revenge porn and doctored political videos (2019),https://bit.ly/4f40oaX, accessed: 2024-09-24

    2019

  4. [4]

    Digital4(2), 316– 332 (2024)

    Casteleiro-Pitrez, J.: Generative artificial intelligence image tools among future designers: A usability, user experience, and emotional analysis. Digital4(2), 316– 332 (2024)

    2024

  5. [5]

    Chai, L., Bau, D., Lim, S.N., Isola, P.: What makes fake images detectable? un- derstanding properties that generalize. In: Eur. Conf. on Computer Vision. (2020)

    2020

  6. [6]

    Chen, Z., Wang, X.: Application of ai technology in interior design179(2020)

    2020

  7. [7]

    An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

    Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., Houlsby, N.: An image is worth 16x16 words: Transformers for image recognition at scale. CoRR abs/2010.11929(2020)

    work page internal anchor Pith review Pith/arXiv arXiv 2010

  8. [8]

    ArXivabs/1911.00686(2019)

    Durall, R., Keuper, M., Pfreundt, F.J., Keuper, J.: Unmasking deepfakes with simple features. ArXivabs/1911.00686(2019)

    work page arXiv 1911

  9. [9]

    7890–7899 (2020)

    Durall, R., Keuper, M., Keuper, J.: Watch your up-convolution: Cnn based gen- erative deep neural networks are failing to reproduce spectral distributions pp. 7890–7899 (2020)

    2020

  10. [10]

    ArXiv (2020)

    Frank, J.C., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., Holz, T.: Lever- aging frequency analysis for deep fake image recognition. ArXiv (2020)

    2020

  11. [11]

    Communications of the ACM63, 139–144 (2014)

    Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A.C., Bengio, Y.: Generative adversarial networks. Communications of the ACM63, 139–144 (2014)

    2014

  12. [12]

    Gu, S., Chen, D., Bao, J., Wen, F., Zhang, B., Chen, D., Yuan, L., Guo, B.: Vector quantized diffusion model for text-to-image synthesis. CVPR pp. 10696– 10706 (2022)

    2022

  13. [13]

    770–778 (2016)

    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition pp. 770–778 (2016)

    2016

  14. [14]

    Denoising Diffusion Probabilistic Models

    Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. ArXiv abs/2006.11239(2020)

    work page internal anchor Pith review Pith/arXiv arXiv 2006

  15. [15]

    Jeong, Y., Kim, D., Ro, Y., Choi, J.: Frepgan: Robust deepfake detection using frequency-levelperturbations.In:AAAIConferenceonArtificialIntelligence(2022)

    2022

  16. [16]

    Jeong, Y., Kim, D., Min, S., Joe, S., Gwon, Y., Choi, J.: Bihpf: Bilateral high-pass filters for robust deepfake detection. WACV pp. 48–57 (2022)

    2022

  17. [17]

    In: ICIP

    Ju, Y., Jia, S., Ke, L., Xue, H., Nagano, K., Lyu, S.: Fusing global and local features for generalized ai-synthesized image detection. In: ICIP. pp. 3465–3469 (2022)

    2022

  18. [18]

    Progressive Growing of GANs for Improved Quality, Stability, and Variation

    Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for im- proved quality, stability, and variation. ArXivabs/1710.10196(2017) Minimalist Preprocessing Approach for Image Synthesis Detection 11

    work page internal anchor Pith review Pith/arXiv arXiv 2017

  19. [19]

    8110–8119 (2020)

    Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan pp. 8110–8119 (2020)

    2020

  20. [20]

    Korosec, K.: Deepfake revenge porn is now illegal in virginia (2019),https://techcrunch.com/2019/07/01/ deepfake-revenge-porn-is-now-illegal-in-virginia/, accessed: 24 Sep

    2019

  21. [21]

    arXiv:2402.19091 (2024)

    Koutlis, C., Papadopoulos, S.: Leveraging representations from intermediate encoder-blocks for synthetic image detection. arXiv:2402.19091 (2024)

    work page arXiv 2024

  22. [22]

    Communications of the ACM60, 84 – 90 (2012)

    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep con- volutional neural networks. Communications of the ACM60, 84 – 90 (2012)

    2012

  23. [23]

    In: CVPR

    Liu, H., Tan, Z., Tan, C., Wei, Y., Wang, J., Zhao, Y.: Forgery-aware adaptive transformer for generalizable synthetic image detection. In: CVPR. pp. 10770– 10780 (2024)

    2024

  24. [24]

    In: CVPR

    Liu, Z., Qi, X., Torr, P.H.: Global texture enhancement for fake face detection in the wild. In: CVPR. pp. 8060–8069 (2020)

    2020

  25. [25]

    In: ICCAIS

    Lomov, I., Makarov, I.: Generative models for fashion industry using deep neural networks. In: ICCAIS. pp. 1–6. IEEE (2019)

    2019

  26. [26]

    3091–3095 (2022)

    Mandelli, S., Bonettini, N., Bestagini, P., Tubaro, S.: Detecting gan-generated im- ages by orthogonal training of multiple cnns pp. 3091–3095 (2022)

    2022

  27. [27]

    Mickens,R.E.:Differenceequations:theory,applicationsandadvancedtopics.CRC Press (2015)

    2015

  28. [28]

    24480–24489 (2023)

    Ojha, U., Li, Y., Lee, Y.J.: Towards universal fake image detectors that generalize across generative models pp. 24480–24489 (2023)

    2023

  29. [29]

    ArXivabs/2007.09355(2020)

    Qian,Y.,Yin,G.,Sheng,L.,Chen,Z.,Shao,J.:Thinkinginfrequency:Faceforgery detection by mining frequency-aware clues. ArXivabs/2007.09355(2020)

    work page arXiv 2007

  30. [30]

    8748–8763 (2021)

    Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision pp. 8748–8763 (2021)

    2021

  31. [31]

    for Schools, B.: Spotting ai: Knowing how to recognise real vs ai images.https: //elearn.eb.com/real-vs-ai-images/(2024), accessed: 2024-08-21

    2024

  32. [32]

    18720–18729 (2022)

    Shiohara, K., Yamasaki, T.: Detecting deepfakes with self-blended images pp. 18720–18729 (2022)

    2022

  33. [33]

    Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., Wei, Y.: Frequency-aware deepfake de- tection: Improving generalizability through frequency space domain learning38(5), 5052–5060 (2024)

    2024

  34. [34]

    28130–28139 (2024)

    Tan, C., Zhao, Y., Wei, S., Gu, G., Liu, P., Wei, Y.: Rethinking the up-sampling operations in cnn-based generative network for generalizable deepfake detection pp. 28130–28139 (2024)

    2024

  35. [35]

    12105–12114 (2023)

    Tan, C., Zhao, Y., Wei, S., Gu, G., Wei, Y.: Learning on gradients: Generalized artifacts representation for gan-generated images detection pp. 12105–12114 (2023)

    2023

  36. [36]

    for now pp

    Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A.: Cnn-generated images are surprisingly easy to spot... for now pp. 8695–8704 (2020)

    2020

  37. [37]

    22445–22455 (2023)

    Wang, Z., Bao, J., Zhou, W., Wang, W., Hu, H., Chen, H., Li, H.: Dire for diffusion- generated image detection pp. 22445–22455 (2023)

    2023

  38. [38]

    Wikipedia Contributors: Finite difference (2024),https://en.wikipedia.org/ wiki/Finite_difference, accessed: 2024-08-21

    2024

  39. [39]

    arXiv e-prints (2024)

    Wootaek Shin, P., Ahn, J.J., Yin, W., Sampson, J., Narayanan, V.: Can prompt modifiers control bias? a comparative analysis of text-to-image generative models. arXiv e-prints (2024)

    2024

  40. [40]

    Zhong, N., Xu, Y., Li, S., Qian, Z., Zhang, X.: Patchcraft: Exploring texture patch for efficient ai-generated image detection (2024)

    2024