Abstract
Deep-learning-based face-swap videos, also known as deepfakes, are becoming more and more realistic and deceiving. The malicious usage of these face-swap videos has caused wide concerns. The research community has been focusing on the automatic detection of these fake videos, but the assessment of their visual realism, as perceived by human eyes, is still an unexplored dimension. Visual realism assessment, or VRA, is essential for assessing the potential impact that may be brought by a specific face-swap video, and it is also important as a quality assessment metric to compare different face-swap methods. In this paper, we make a small step towards this new VRA direction by building a benchmark for evaluating the effectiveness of different automatic VRA models, which range from using traditional handcrafted features to different kinds of deep-learning features. The evaluations are based on a recent competition dataset named DFGC-2022, which contains 1400 diverse face-swap videos that are annotated with Mean Opinion Scores (MOS) on visual realism. Comprehensive experiment results using 11 models and 3 protocols are shown and discussed. We demonstrate the feasibility of devising effective VRA models for assessing face-swap videos and methods. The particular usefulness of existing deepfake detection features for VRA is also noted. The code can be found at https://github.com/XianyunSun/VRA.git.
This work is done while Xianyun Sun is an intern at CASIA.
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References
Dfgc-2022 first-place solution of the detection track. https://github.com/chenhanch/DFGC-2022-1st-place
Bonettini, N., Cannas, E.D., Mandelli, S., Bondi, L., Bestagini, P., Tubaro, S.: Video face manipulation detection through ensemble of CNNs. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5012–5019. IEEE (2021)
Chen, R., Chen, X., Ni, B., Ge, Y.: Simswap: an efficient framework for high fidelity face swapping. In: Proceedings of the 28th ACM International Conference on Multimedia, pp. 2003–2011 (2020)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Dolhansky, B., et al.: The deepfake detection challenge (dfdc) dataset. arXiv preprint arXiv:2006.07397 (2020)
Ghadiyaram, D., Bovik, A.C.: Perceptual quality prediction on authentically distorted images using a bag of features approach. J. Vis. 17(1), 32–32 (2017)
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), pp. 770–778 (2016)
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (2017)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Juefei-Xu, F., Wang, R., Huang, Y., Guo, Q., Ma, L., Liu, Y.: Countering malicious deepfakes: Survey, battleground, and horizon. Int. J. Comput. Vis., 1–57 (2022)
Korhonen, J.: Two-level approach for no-reference consumer video quality assessment. IEEE Trans. Image Process. 28(12), 5923–5938 (2019)
Korshunov, P., Marcel, S.: Deepfake detection: humans vs. machines. arXiv preprint arXiv:2009.03155 (2020)
Li, L., Bao, J., Yang, H., Chen, D., Wen, F.: Advancing high fidelity identity swapping for forgery detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5074–5083 (2020)
Lo, C.C., Fu, S.W., Huang, W.C., Wang, X., Yamagishi, J., Tsao, Y., Wang, H.M.: MOSNet: deep learning-based objective assessment for voice conversion. In: Proceedings of Interspeech 2019, pp. 1541–1545 (2019). 10.21437/Interspeech. 2019–2003
Mittal, A., Moorthy, A.K., Bovik, A.C.: No-reference image quality assessment in the spatial domain. IEEE Trans. Image Process. 21(12), 4695–4708 (2012)
Nightingale, S., Agarwal, S., Härkönen, E., Lehtinen, J., Farid, H.: Synthetic faces: how perceptually convincing are they? J. Vis. 21(9), 2015–2015 (2021)
Nightingale, S.J., Farid, H.: Ai-synthesized faces are indistinguishable from real faces and more trustworthy. Proc. Natl. Acad. Sci. 119(8), e2120481119 (2022)
Parkhi, O., Vedaldi, A., Zisserman, A.: Deep face recognition. In: Proceedings of the British Machine Vision Conference, pp. 1–12 (2015)
Peng, B., Xiang, W., Jiang, Y., Wang, W., Dong, J., Sun, Z., Lei, Z., Lyu, S.: Dfgc 2022: the second deepfake game competition. In: 2022 IEEE International Joint Conference on Biometrics (IJCB), pp. 1–10 (2022)
Perov, I., et al.: Deepfacelab: Integrated, flexible and extensible face-swapping framework. arXiv preprint arXiv:2005.05535 (2020)
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 (ICCV), pp. 1–11 (2019)
Saad, M.A., Bovik, A.C., Charrier, C.: Blind prediction of natural video quality. IEEE Trans. Image Process. 23(3), 1352–1365 (2014)
Seshadrinathan, K., Soundararajan, R., Bovik, A.C., Cormack, L.K.: Study of subjective and objective quality assessment of video. IEEE Trans. Image Process. 19(6), 1427–1441 (2010)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the 3rd International Conference on Learning Representations (2015). https://arxiv.org/abs/1409.1556
Tian, Y., Ni, Z., Chen, B., Wang, S., Wang, H., Kwong, S.: Generalized visual quality assessment of gan-generated face images. arXiv preprint arXiv:2201.11975 (2022)
Tu, Z., Wang, Y., Birkbeck, N., Adsumilli, B., Bovik, A.C.: Ugc-vqa: benchmarking blind video quality assessment for user generated content. IEEE Trans. Image Process. 30, 4449–4464 (2021)
Tu, Z., Yu, X., Wang, Y., Birkbeck, N., Adsumilli, B., Bovik, A.C.: Rapique: rapid and accurate video quality prediction of user generated content. IEEE Open J. Signal Process. 2, 425–440 (2021)
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE Trans. Image Process. 13(4), 600–612 (2004)
Wu, H., Chen, C., Hou, J., Liao, L., Wang, A., Sun, W., Yan, Q., Lin, W.: Fast-vqa: efficient end-to-end video quality assessment with fragment sampling. Proceedings of European Conference of Computer Vision (ECCV) (2022)
Xue, W., Mou, X., Zhang, L., Bovik, A.C., Feng, X.: Blind image quality assessment using joint statistics of gradient magnitude and Laplacian features. IEEE Trans. Image Process. 23(11), 4850–4862 (2014)
Zezario, R.E., Fu, S.W., Chen, F., Fuh, C.S., Wang, H.M., Tsao, Y.: Deep learning-based non-intrusive multi-objective speech assessment model with cross-domain features. IEEE/ACM Trans. Audio Speech Lang. Process. 31, 54–70 (2023). https://doi.org/10.1109/TASLP.2022.3205757
Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)
Acknowledgment
This work is supported by Beijing Natural Science Foundation under Grant No. 4232037, the National Natural Science Foundation of China (NSFC) under Grants 62272460, U19B2038, 62106015, a grant from Young Elite Scientists Sponsorship Program by CAST (YESS), CAAI-Huawei MindSpore Open Fund, the Pyramid Talent Training Project of Beijing University of Civil Engineering and Architecture (JDYC20220819), the 2023-2025 Young Elite Scientist Sponsorship Program by BAST (BYESS2023130), and the BUCEA Post Graduate Innovation Project (PG2023090).
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Sun, X., Dong, B., Wang, C., Peng, B., Dong, J. (2023). Visual Realism Assessment for Face-Swap Videos. In: Lu, H., et al. Image and Graphics. ICIG 2023. Lecture Notes in Computer Science, vol 14355. Springer, Cham. https://doi.org/10.1007/978-3-031-46305-1_34
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DOI: https://doi.org/10.1007/978-3-031-46305-1_34
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