Skip to main content
SpringerLink
Log in

Discovering Transferable Forensic Features for CNN-Generated Images Detection

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13675))

Included in the following conference series:

Abstract

Visual counterfeits (We refer to CNN-generated images as counterfeits throughout this paper.) are increasingly causing an existential conundrum in mainstream media with rapid evolution in neural image synthesis methods. Though detection of such counterfeits has been a taxing problem in the image forensics community, a recent class of forensic detectors – universal detectors – are able to surprisingly spot counterfeit images regardless of generator architectures, loss functions, training datasets, and resolutions [61]. This intriguing property suggests the possible existence of transferable forensic features (T-FF) in universal detectors. In this work, we conduct the first analytical study to discover and understand T-FF in universal detectors. Our contributions are 2-fold: 1) We propose a novel forensic feature relevance statistic (FF-RS) to quantify and discover T-FF in universal detectors and, 2) Our qualitative and quantitative investigations uncover an unexpected finding: color is a critical T-FF in universal detectors. Code and models are available at https://keshik6.github.io/transferable-forensic-features/.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 89.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 119.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein GAN (2017)

    Google Scholar 

  2. Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.R., Samek, W.: On pixel-wise explanations for non-linear classifier decisions by layer-wise relevance propagation. PLoS ONE 10(7), e0130140 (2015)

    Article  Google Scholar 

  3. Bau, D., Zhou, B., Khosla, A., Oliva, A., Torralba, A.: Network dissection: quantifying interpretability of deep visual representations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6541–6549 (2017)

    Google Scholar 

  4. Bau, D., et al.: GAN dissection: visualizing and understanding generative adversarial networks. In: International Conference on Learning Representations (2018)

    Google Scholar 

  5. Binder, A., Montavon, G., Lapuschkin, S., Müller, K.-R., Samek, W.: Layer-wise relevance propagation for neural networks with local renormalization layers. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 63–71. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44781-0_8

    Chapter  MATH  Google Scholar 

  6. Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=B1xsqj09Fm

  7. Chai, L., Bau, D., Lim, S.-N., Isola, P.: What makes fake images detectable? Understanding properties that generalize. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12371, pp. 103–120. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58574-7_7

    Chapter  Google Scholar 

  8. Chandrasegaran, K., Tran, N.T., Cheung, N.M.: A closer look at Fourier spectrum discrepancies for CNN-generated images detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7200–7209, June 2021

    Google Scholar 

  9. Chen, C., Li, O., Tao, C., Barnett, A.J., Su, J., Rudin, C.: This looks like that: deep learning for interpretable image recognition. Curran Associates Inc., Red Hook (2019)

    Google Scholar 

  10. Chen, J., Song, L., Wainwright, M.J., Jordan, M.I.: L-shapley and C-shapley: efficient model interpretation for structured data. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=S1E3Ko09F7

  11. Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

  12. Choi, Y., Uh, Y., Yoo, J., Ha, J.W.: StarGAN v2: diverse image synthesis for multiple domains. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    Google Scholar 

  13. Cozzolino, D., Thies, J., Rössler, A., Riess, C., Nießner, M., Verdoliva, L.: ForensicTransfer: weakly-supervised domain adaptation for forgery detection. arXiv preprint arXiv:1812.02510 (2018)

  14. Desai, S.S., Ramaswamy, H.G.: Ablation-CAM: visual explanations for deep convolutional network via gradient-free localization. In: 2020 IEEE Winter Conference on Applications of Computer Vision (WACV), pp. 972–980 (2020)

    Google Scholar 

  15. Dhurandhar, A., et al.: Explanations based on the missing: towards contrastive explanations with pertinent negatives. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 590–601. Curran Associates Inc., Red Hook (2018)

    Google Scholar 

  16. Durall, R., Keuper, M., Keuper, J.: Watch your up-convolution: CNN based generative deep neural networks are failing to reproduce spectral distributions. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    Google Scholar 

  17. Dzanic, T., Shah, K., Witherden, F.: Fourier spectrum discrepancies in deep network generated images. In: Thirty-Fourth Annual Conference on Neural Information Processing Systems (NeurIPS), December 2020

    Google Scholar 

  18. Frank, J., Eisenhofer, T., Schönherr, L., Fischer, A., Kolossa, D., Holz, T.: Leveraging frequency analysis for deep fake image recognition. In: International Conference on Machine Learning, pp. 3247–3258. PMLR (2020)

    Google Scholar 

  19. Fu, R., Hu, Q., Dong, X., Guo, Y., Gao, Y., Li, B.: Axiom-based grad-CAM: towards accurate visualization and explanation of CNNs. In: BMVC (2020)

    Google Scholar 

  20. Gal, Y., Ghahramani, Z.: Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: Balcan, M.F., Weinberger, K.Q. (eds.) Proceedings of the 33rd International Conference on Machine Learning. Proceedings of Machine Learning Research, New York, USA, 20–22 June 2016, vol. 48, pp. 1050–1059. PMLR (2016). https://proceedings.mlr.press/v48/gal16.html

  21. Goodfellow, I., et al.: Generative adversarial nets. In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 27, pp. 2672–2680. Curran Associates, Inc. (2014). https://proceedings.neurips.cc/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf

  22. Hao, K., Heaven, W.D.: The year deepfakes went mainstream, December 2020. https://www.technologyreview.com/2020/12/24/1015380/best-ai-deepfakes-of-2020/

  23. Harrison, E.: Shockingly realistic Tom Cruise deepfakes go viral on TikTok, February 2021. https://www.independent.co.uk/arts-entertainment/films/news/tom-cruise-deepfake-tiktok-video-b1808000.html

  24. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2016

    Google Scholar 

  25. Jiang, P.T., Zhang, C.B., Hou, Q., Cheng, M.M., Wei, Y.: LayerCAM: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021)

    Article  Google Scholar 

  26. Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=Hk99zCeAb

  27. Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M.F., Lin, H. (eds.) Advances in Neural Information Processing Systems, vol. 33, pp. 12104–12114. Curran Associates, Inc. (2020). https://proceedings.neurips.cc/paper/2020/file/8d30aa96e72440759f74bd2306c1fa3d-Paper.pdf

  28. Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

    Google Scholar 

  29. Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of StyleGAN. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    Google Scholar 

  30. Khayatkhoei, M., Elgammal, A.: Spatial frequency bias in convolutional generative adversarial networks, October 2020

    Google Scholar 

  31. Ledig, C., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 105–114 (2017). https://doi.org/10.1109/CVPR.2017.19

  32. Lim, S.K., Loo, Y., Tran, N.T., Cheung, N.M., Roig, G., Elovici, Y.: DOPING: generative data augmentation for unsupervised anomaly detection with GAN. In: 18th IEEE International Conference on Data Mining, ICDM 2018, pp. 1122–1127. Institute of Electrical and Electronics Engineers Inc. (2018)

    Google Scholar 

  33. Lloyd, S.: Least squares quantization in PCM. IEEE Trans. Inf. Theory 28(2), 129–137 (1982). https://doi.org/10.1109/TIT.1982.1056489

    Article  MathSciNet  MATH  Google Scholar 

  34. Lundberg, S.M., Lee, S.I.: A unified approach to interpreting model predictions. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates, Inc. (2017). https://proceedings.neurips.cc/paper/2017/file/8a20a8621978632d76c43dfd28b67767-Paper.pdf

  35. Luo, Y., Zhang, Y., Yan, J., Liu, W.: Generalizing face forgery detection with high-frequency features. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16317–16326 (2021)

    Google Scholar 

  36. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(86), 2579–2605 (2008). http://jmlr.org/papers/v9/vandermaaten08a.html

  37. Mahmud, A.H.: Deep dive into deepfakes: frighteningly real and sometimes used for the wrong things, October 2021. https://www.channelnewsasia.com/singapore/deepfakes-ai-security-threat-face-swapping-2252161

  38. Marra, F., Gragnaniello, D., Cozzolino, D., Verdoliva, L.: Detection of GAN-generated fake images over social networks. In: 2018 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 384–389 (2018). https://doi.org/10.1109/MIPR.2018.00084

  39. Marra, F., Gragnaniello, D., Verdoliva, L., Poggi, G.: Do GANs leave artificial fingerprints? In: 2019 IEEE Conference on Multimedia Information Processing and Retrieval (MIPR), pp. 506–511. IEEE (2019)

    Google Scholar 

  40. McCloskey, S., Albright, M.: Detecting GAN-generated imagery using saturation cues. In: 2019 IEEE International Conference on Image Processing (ICIP), pp. 4584–4588. IEEE (2019)

    Google Scholar 

  41. Muhammad, M.B., Yeasin, M.: Eigen-CAM: class activation map using principal components. In: 2020 International Joint Conference on Neural Networks (IJCNN), pp. 1–7. IEEE (2020)

    Google Scholar 

  42. Nataraj, L., et al.: Detecting GAN generated fake images using co-occurrence matrices. Electron. Imaging 2019(5), 532-1 (2019)

    Google Scholar 

  43. News, C.: Synthetic media: how deepfakes could soon change our world, October 2021. https://www.cbsnews.com/news/deepfake-artificial-intelligence-60-minutes-2021-10-10/

  44. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 2337–2346 (2019)

    Google Scholar 

  45. Park, T., Liu, M.Y., Wang, T.C., Zhu, J.Y.: Semantic image synthesis with spatially-adaptive normalization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2019

    Google Scholar 

  46. Pearson, K.: LIII. on lines and planes of closest fit to systems of points in space. Lond. Edinb. Dublin Philos. Mag. J. Sci. 2(11), 559–572 (1901). https://doi.org/10.1080/14786440109462720

  47. Razavi, A., van den Oord, A., Vinyals, O.: Generating diverse high-fidelity images with VQ-VAE-2. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32, pp. 14866–14876. Curran Associates, Inc. (2019). https://proceedings.neurips.cc/paper/2019/file/5f8e2fa1718d1bbcadf1cd9c7a54fb8c-Paper.pdf

  48. Rossler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: FaceForensics++: learning to detect manipulated facial images. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1–11 (2019)

    Google Scholar 

  49. Schwarz, K., Liao, Y., Geiger, A.: On the frequency bias of generative models. In: Advances in Neural Information Processing Systems, vol. 34 (2021)

    Google Scholar 

  50. Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618–626 (2017)

    Google Scholar 

  51. Simonite, T.: What happened to the deepfake threat to the election? November 2020. https://www.wired.com/story/what-happened-deepfake-threat-election/

  52. Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7–9 May 2015, Workshop Track Proceedings (2015). arxiv.org/abs/1412.6806

  53. Srinivas, S., Fleuret, F.: Full-gradient representation for neural network visualization. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

    Google Scholar 

  54. Strumbelj, E., Kononenko, I.: An efficient explanation of individual classifications using game theory. J. Mach. Learn. Res. 11, 1–18 (2010)

    MathSciNet  MATH  Google Scholar 

  55. Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)

    Google Scholar 

  56. Tran, N.-T., Bui, T.-A., Cheung, N.-M.: Dist-GAN: an improved GAN using distance constraints. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 387–401. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_23

    Chapter  Google Scholar 

  57. Tran, N.T., Tran, V.H., Nguyen, B.N., Yang, L., Cheung, N.M.M.: Self-supervised GAN: analysis and improvement with multi-class minimax game. In: Wallach, H., Larochelle, H., Beygelzimer, A., d’ Alché-Buc, F., Fox, E., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 32. Curran Associates, Inc. (2019). https://proceedings.neurips.cc/paper/2019/file/d04cb95ba2bea9fd2f0daa8945d70f11-Paper.pdf

  58. Tran, N.T., Tran, V.H., Nguyen, N.B., Nguyen, T.K., Cheung, N.M.: On data augmentation for GAN training. IEEE Trans. Image Process. 30, 1882–1897 (2021)

    Article  MathSciNet  Google Scholar 

  59. Wang, H., et al.: Score-CAM: score-weighted visual explanations for convolutional neural networks. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 24–25 (2020)

    Google Scholar 

  60. Wang, R., et al.: FakeSpotter: a simple yet robust baseline for spotting AI-synthesized fake faces. In: Proceedings of the Twenty-Ninth International Conference on International Joint Conferences on Artificial Intelligence, pp. 3444–3451 (2021)

    Google Scholar 

  61. Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A.: CNN-generated images are surprisingly easy to spot... for now. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2020

    Google Scholar 

  62. Yu, N., Davis, L.S., Fritz, M.: Attributing fake images to GANs: learning and analyzing GAN fingerprints. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 7556–7566 (2019)

    Google Scholar 

  63. Zhang, X., Karaman, S., Chang, S.: Detecting and simulating artifacts in GAN fake images. In: 2019 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–6 (2019). https://doi.org/10.1109/WIFS47025.2019.9035107

  64. Zhao, S., Liu, Z., Lin, J., Zhu, J.Y., Han, S.: Differentiable augmentation for data-efficient GAN training. In: Conference on Neural Information Processing Systems (NeurIPS) (2020)

    Google Scholar 

  65. Zhao, Y., Ding, H., Huang, H., Cheung, N.M.: A closer look at few-shot image generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 9140–9150 (2022)

    Google Scholar 

  66. Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)

    Google Scholar 

Download references

Acknowledgements

This research is supported by the National Research Foundation, Singapore under its AI Singapore Programmes (AISG Award No.: AISG2-RP-2021-021; AISG Award No.: AISG-100E2018-005). This project is also supported by SUTD project PIE-SGP-AI-2018-01. Alexander Binder was supported by the SFI Visual Intelligence, project no. 309439 of the Research Council of Norway.

Author information

Authors and Affiliations

  1. Singapore University of Technology and Design (SUTD), Singapore, Singapore

    Keshigeyan Chandrasegaran, Ngoc-Trung Tran & Ngai-Man Cheung

  2. Singapore Institute of Technology (SIT), Singapore, Singapore

    Alexander Binder

  3. University of Oslo (UIO), Oslo, Norway

    Alexander Binder

Authors
  1. Keshigeyan Chandrasegaran
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ngoc-Trung Tran
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alexander Binder
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ngai-Man Cheung
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Keshigeyan Chandrasegaran .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 19088 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Chandrasegaran, K., Tran, NT., Binder, A., Cheung, NM. (2022). Discovering Transferable Forensic Features for CNN-Generated Images Detection. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13675. Springer, Cham. https://doi.org/10.1007/978-3-031-19784-0_39

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-19784-0_39

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-19783-3

  • Online ISBN: 978-3-031-19784-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation