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Deepfake videos present an increasing threat to society with potentially negative impact on criminal justice, democracy, and personal safety and privacy.
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Chugh, K., Gupta, P., Dhall, A., Subramanian, R.: Not made for each other- audio-visual dissonance-based deepfake detection and localization. In: Proceedings of the 28th ACM International Conference on Multimedia. p. 439–447. MM ’20, Association for Computing Machinery, New York, NY, USA (2020). https://doi.org/10.1145/3394171.3413700, https://doi.org/10.1145/3394171.3413700
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Huang, Y., Juefei-Xu, F., Wang, R., Guo, Q., Ma, L., Xie, X., Li, J., Miao, W., Liu, Y., Pu, G.: Fakepolisher: Making deepfakes more detection-evasive by shallow reconstruction. In: Proceedings of the 28th ACM international conference on multimedia. pp. 1217–1226 (2020)
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Mittal, T., Bhattacharya, U., Chandra, R., Bera, A., Manocha, D.: Emotions don’t lie: An audio-visual deepfake detection method using affective cues. In: Proceedings of the 28th ACM international conference on multimedia. pp. 2823–2832 (2020)
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Shahzad, S., Hashmi, A., Khan, S., Peng, Y.T., Tsao, Y., Wang, H.m.: Lip sync matters: A novel multimodal forgery detector (12 2022)
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Cozzolino, D., Pianese, A., Niesner, M., Verdoliva, L.: Audio-visual person-of-interest deepfake detection. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 943–952. IEEE Computer Society, Los Alamitos, CA, USA (jun 2023). https://doi.org/10.1109/CVPRW59228.2023.00101, https://doi.ieeecomputersociety.org/10.1109/CVPRW59228.2023.00101
2023
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Feng, C., Chen, Z., Owens, A.: Self-supervised video forensics by audio-visual anomaly detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 10491–10503 (June 2023)
2023
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