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SE_EDNet: A Robust Manipulated Faces Detection Algorithm

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Advances in Computer Graphics (CGI 2021)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 13002))

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Abstract

Face manipulation techniques have raised concern over potential threats, which demand effective images forensic methods. Various approaches have been proposed, but when detecting higher-quality manipulated faces, the performance of previous method is not good enough. To prevent the abuse of these techniques and improve the detection ability, this paper proposes a new algorithm named Squeeze-Excitation Euclidean Distance Network (SE_EDNet) to detect manipulated faces, which is suitable for Deepfakes and GANs detection. SE_EDNet use Euclidean distance to describe similaity of vectors, which gives higher weights to important areas than traditional self-attention mechanism. Further, we take frequency into account and extract residuals information, which are obtained by a second-order filter. Then residuals are combined with original images as the input features for the network. Comparison experiment shows SE_EDNet performs better than existing algorithms. Extensive robustness experiments on Celeb-DF and DFFD demonstrate that proposed algorithm is robust against attacking on AUC scores and Recalls.

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References

  1. Cao, J., Hu, Y., Yu, B., He, R., Sun, Z.: 3d aided duet gans for multi-view face image synthesis. IEEE Trans. Inf. Forensics Secur. 14, 2028–2042 (2019)

    Article  Google Scholar 

  2. 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, pp. 8188–8197 (2020)

    Google Scholar 

  3. Fernandes, S., et al.: Predicting heart rate variations of deepfake videos using neural ode. In: 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW), pp. 1721–1729 (2019)

    Google Scholar 

  4. Li, Y., Lyu, S.: Exposing deepfake videos by detecting face warping artifacts. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 46–52 (2019)

    Google Scholar 

  5. Nataraj, L., Mohammed, T.M., Chandrasekaran, S., Flenner, A., Bappy, J.H., Roy-Chowdhury, A.K., Manjunath, B.S.: Detecting gan generated fake images using co-occurrence matrices. Electron. Imaging 5, 1–7 (2019)

    Google Scholar 

  6. Li, H., Li, B., Tan, S., Huang, J.: Identification of deep network generated images using disparities in color components. Signal Process. 174, 107616 (2020)

    Google Scholar 

  7. Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. In: 2018 IEEE International Workshop on Information Forensics and Security (WIFS), pp. 1–7 (2018)

    Google Scholar 

  8. Nguyen, H., Yamagishi, J., Echizen, I.: Capsule-forensics: Using capsule networks to detect forged images and videos. In: 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 2307–2311 (2019)

    Google Scholar 

  9. Kumar, A., Bhavsar, A., Verma, R.: Detecting deepfakes with metric learning. In: 2020 8th International Workshop on Biometrics and Forensics (IWBF), pp. 1–6 (2020)

    Google Scholar 

  10. Wang, X., Girshick, R., Gupta, A., He, K.: Non-local neural networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7794–7803 (2018)

    Google Scholar 

  11. Li, Y., Yang, X., Sun, P., Qi, H., Lyu, S.: Celeb-df: a large-scale challenging dataset for deepfake forensics. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3207–3216 (2020)

    Google Scholar 

  12. Zhang, H., et al.: Context encoding for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7151–7160 (2018)

    Google Scholar 

  13. Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132–7141 (2018)

    Google Scholar 

  14. Fridrich, J., Kodovsky, J.: Rich models for steganalysis of digital images. IEEE Trans. Inf. Forensics Secur. 7, 868–882 (2012)

    Article  Google Scholar 

  15. Dang, H., Liu, F., Stehouwer, J., Liu, X., Jain, A.K.: On the detection of digital face manipulation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 5781–5790 (2020)

    Google Scholar 

  16. Nguyen, H.H., Fang, F., Yamagishi, J., Echizen, I.: Multi-task learning for detecting and segmenting manipulated facial images and videos. In: IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS), pp. 1–8 (2019)

    Google Scholar 

  17. Yang, X., Li, Y., Lyu, S.: Exposing deep fakes using inconsistent head poses. In: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 8261–8265 (2019)

    Google Scholar 

  18. Dsp-fwa. https://github.com/yuezunli/DSP-FWA. Accessed 7 Oct 2020

  19. Güera, D., Delp, E.J.: Deepfake video detection using recurrent neural networks. In: 2018 15th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1–6 (2018)

    Google Scholar 

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Acknowledgement

The work is suppoted by the BAIDU supports Ministry of Education’s Education Cooperation Program(No. 2012115PCK00690).

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Correspondence to Tanfeng Sun .

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Peng, C., Yao, L., Sun, T., Jiang, X., Mi, Z. (2021). SE_EDNet: A Robust Manipulated Faces Detection Algorithm. In: Magnenat-Thalmann, N., et al. Advances in Computer Graphics. CGI 2021. Lecture Notes in Computer Science(), vol 13002. Springer, Cham. https://doi.org/10.1007/978-3-030-89029-2_6

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  • DOI: https://doi.org/10.1007/978-3-030-89029-2_6

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-89028-5

  • Online ISBN: 978-3-030-89029-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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