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Cited by 13

Videoforensicshq: Detecting High-Quality Manipulated Face Videos

Fox, Gereon, Liu, Wentao, Kim, Hyeongwoo, Seidel, Hans-Peter, Elgharib, Mohamed, Theobalt, Christian·2021·2021 IEEE International Conference on Multimedia and Expo (ICME)
DOI 10.1109/icme51207.2021.9428101
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  • VideoForensicsHQ contains 1,737 videos with 1,666,816 frames, including 1,339,843 real frames and 326,973 fake frames generated using Deep Video Portraits.

    “VideoForensicsHQ (Fox et al., 2021): This dataset contains 1,737 videos with 1,666,816 frames, including 1,339,843 real frames and 326,973 fake frames generated using Deep Video Portraits (DVP) (Kim et al., 2018)”

    From Deepfake: definitions, performance metrics and standards, datasets, and a meta-review (2024) · cites this paper as Fox et al., 2021

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Research works printed but not linked: 39

  1. Afchar, D., Nozick, V., Yamagishi, J., Echizen, I.: Mesonet: a compact facial video forgery detection network. International Workshop on Information Forensics and Security (WIFS) pp. 1-7 (2018)
  2. Agarwal, S., Farid, H., Gu, Y., He, M., Nagano, K., Li, H.: Protecting world leaders against deep fakes. In: Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (June 2019)
  3. Amerini, I., Galteri, L., Caldelli, R., Del Bimbo, A.: Deepfake video detection through optical flow based cnn. In: The IEEE International Conference on Com- puter Vision (ICCV) Workshops (Oct 2019)
  4. Bayar, B., Stamm, M.C.: Constrained convolutional neural networks: A new approach towards general purpose image manipulation detection. IEEE Trans- actions on Information Forensics and Security 13(11), 2691-2706 (Nov 2018). https://doi.org/10.1109/TIFS.2018.2825953 · DOI
  5. Bayar, B., Stamm, M.C.: A deep learning approach to universal image manipula- tion detection using a new convolutional layer. In: ACM Workshop on Information Hiding and Multimedia Security. pp. 5-10 (2016)
  6. Chollet, F.: Xception: Deep learning with depthwise separable convolutions. In: Conference on Computer Vision and Pattern Recognition (CVPR). pp. 1800-1807 (July 2017)
  7. Cozzolino, D., Gragnaniello, D., Verdoliva, L.: Image forgery detection through residual-based local descriptors and block-matching. In: International Conference on Image Processing (ICIP). pp. 5297-5301 (Oct 2014)
  8. Cozzolino, D., Poggi, G., Verdoliva, L.: Recasting residual-based local descrip- tors as convolutional neural networks: An application to image forgery detection. In: ACM Workshop on Information Hiding and Multimedia Security. pp. 159-164 (2017)
  9. Garrido, P., Valgaerts, L., Rehmsen, O., Thormaehlen, T., Perez, P., Theobalt, C.: Automatic face reenactment. In: Computer Vision and Pattern Recognition (CVPR) (2014)
  10. Garrido, P., Valgaerts, L., Sarmadi, H., Steiner, I., Varanasi, K., Perez, P., Theobalt, C.: Vdub -modifying face video of actors for plausible visual alignment to a dubbed audio track. Computer Graphics Forum (Proceedings of Eurographics) (2015)
  11. Garrido, P., Zollhöfer, M., Casas, D., Valgaerts, L., Varanasi, K., Pérez, P., Theobalt, C.: Reconstruction of personalized 3d face rigs from monocular video. ACM Transactions on Graphics (TOG) 35(3), 28 (2016)
  12. Geng, J., Shao, T., Zheng, Y., Weng, Y., Zhou, K.: Warp-guided GANs for single- photo facial animation. ACM Transactions on Graphics (TOG) 37(6), 231:1-231:12 (2018). https://doi.org/10.1145/3272127.3275043, http://doi.acm.org/10.1145/ 3272127.3275043 · DOI
  13. Kim, H., Elgharib, M., Zollhöfer, M., Seidel, H.P., Beeler, T., Richardt, C., Theobalt, C.: Neural style-preserving visual dubbing. ACM Transactions on Graph- ics (TOG) (2019)
  14. Kim, H., Garrido, P., Tewari, A., Xu, W., Thies, J., Niessner, M., Pérez, P., Richardt, C., Zollhöfer, M., Theobalt, C.: Deep Video Portraits. ACM Transac- tions on Graphics (TOG) (2018)
  15. King, D.E.: Dlib-ml: A machine learning toolkit. Journal of Machine Learning Research 10, 1755-1758 (2009)
  16. Korshunov, P., Marcel, S.: Deepfakes: a new threat to face recognition? As- sessment and detection. arXiv abs/1812.08685 (2018), http://arxiv.org/abs/ 1812.08685 · arXiv
  17. Li, H., Huang, J.: Localization of deep inpainting using high-pass fully convolu- tional network. In: 2019 IEEE/CVF International Conference on Computer Vision (ICCV). pp. 8300-8309 (Oct 2019). https://doi.org/10.1109/ICCV.2019.00839 · DOI
  18. Li, Y., Lyu, S.: Exposing deepfake videos by detecting face warping artifacts. In: IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) (2019)
  19. Nagano, K., Seo, J., Xing, J., Wei, L., Li, Z., Saito, S., Agarwal, A., Fursund, J., Li, H.: paGAN: Real-time avatars using dynamic tex- tures. ACM Transactions on Graphics (TOG) 37(6), 258:1-258:12 (2018). https://doi.org/10.1145/3272127.3275075, http://doi.acm.org/10.1145/ 3272127.3275075 · DOI
  20. Nguyen, H.H., Yamagishi, J., Echizen, I.: Capsule-forensics: Using capsule networks to detect forged images and videos. In: ICASSP 2019 -2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). pp. 2307-2311 (May 2019). https://doi.org/10.1109/ICASSP.2019.8682602 · DOI · Paper page
  21. Raghavendra, R., Raja, K.B., Venkatesh, S., Busch, C.: Transferable deep-cnn features for detecting digital and print-scanned morphed face images. In: Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 1822-1830 (July 2017)
  22. Rahmouni, N., Nozick, V., Yamagishi, J., Echizen, I.: Distinguishing computer graphics from natural images using convolution neural networks. In: Workshop on Information Forensics and Security (WIFS). pp. 1-6 (Dec 2017)
  23. Rössler, A., Cozzolino, D., Verdoliva, L., Riess, C., Thies, J., Nießner, M.: Face- forensics++: Learning to detect manipulated facial images. arXiv (2019)
  24. Sabir, E., Cheng, J., Jaiswal, A., AbdAlmageed, W., Masi, I., Natarajan, P.: Recur- rent convolutional strategies for face manipulation detection in videos. In: Work- shop on Applications of Computer Vision and Pattern Recognition to Media Foren- sics with CVPR (2019)
  25. Saragih, J.M., Lucey, S., Cohn, J.F.: Face alignment through subspace constrained mean-shifts. In: International Conference on Computer Vision (ICCV). pp. 1034- 1041 (2009)
  26. Saragih, J.M., Lucey, S., Cohn, J.F.: Deformable model fitting by regularized land- mark mean-shift. IJCV 91(2) (2011)
  27. Suwajanakorn, S., Seitz, S.M., Kemelmacher-Shlizerman, I.: Synthesizing obama: Learning lip sync from audio. ACM Transactions on Graphics (TOG) 36(4) (Jul 2017)
  28. Szegedy, C., Ioffe, S., Vanhoucke, V., Alemi, A.A.: Inception-v4, inception-resnet and the impact of residual connections on learning. In: AAAI Conference on Arti- ficial Intelligence (AAAI). pp. 4278-4284 (2017)
  29. Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., Nießner, M.: Face2Face: Real-time Face Capture and Reenactment of RGB Videos. In: Computer Vision and Pattern Recognition (CVPR) (2016)
  30. Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics (TOG) (2019)
  31. Thies, J., Zollhöfer, M., Nießner, M.: Deferred neural rendering: Image synthesis using neural textures. ACM Transactions on Graphics 2019 (TOG) (2019)
  32. Thies, J., Zollhöfer, M., Stamminger, M., Theobalt, C., Niessner, M.: Facevr: Real-time gaze-aware facial reenactment in virtual reality. ACM Transactions on Graphics (TOG) 37(2), 25:1-25:15 (Jun 2018). https://doi.org/10.1145/3182644, http://doi.acm.org/10.1145/3182644 · DOI
  33. Wang, S.Y., Wang, O., Owens, A., Zhang, R., Efros, A.A.: Detecting photoshopped faces by scripting photoshop. arXiv preprint arXiv:1906.05856 (2019) · arXiv
  34. Wang, S.Y., Wang, O., Zhang, R., Owens, A., Efros, A.A.: Cnn-generated images are surprisingly easy to spot...for now. In: CVPR (2020)
  35. Wiles, O., Sophia Koepke, A., Zisserman, A.: X2face: A network for controlling face generation by using images, audio, and pose codes. In: European Conference on Computer Vision (ECCV). pp. 690-706 (2018)
  36. Wu, W., Zhang, Y., Li, C., Qian, C., Loy, C.C.: Reenactgan: Learning to reen- act faces via boundary transfer. In: European Conference on Computer Vision (ECCV). pp. 622-638 (2018)
  37. Wu, Y., AbdAlmageed, W., Natarajan, P.: Mantra-net: Manipulation tracing net- work for detection and localization of image forgeries with anomalous features. In: 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9535-9544 (June 2019). https://doi.org/10.1109/CVPR.2019.00977 · DOI
  38. Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Learning rich features for image manipulation detection. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1053-1061 (June 2018). https://doi.org/10.1109/CVPR.2018.00116 · DOI
  39. Zhou, P., Han, X., Morariu, V.I., Davis, L.S.: Two-stream neural networks for tampered face detection. In: Computer Vision and Pattern Recognition Workshops (CVPRW). pp. 1831-1839 (July 2017)

Cited by

Sorted by citations

Authors
Fox, G., Liu, W., Kim, H., Seidel, H.-P., Elgharib, M., & Theobalt, C.
Affiliations
Saarland Informatics Campus,Max Planck Institute for Informatics
Venue
2021 IEEE International Conference on Multimedia and Expo (ICME)
Publisher
IEEE
Published
2021-07-05
Pages
1-6
DOI
10.1109/icme51207.2021.9428101
Cited by
13
References
7
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