Capsule: Nguyen H H, Yamagishi J, Echizen I, et al. Use of a Capsule Network to Detect Fake Images and Videos.[J]. arXiv: Computer Vision and Pattern Recognition, 2019. [Github] [Paper]

ClassNSeg: Nguyen, Huy H., et al. "Multi-task learning for detecting and segmenting manipulated facial images and videos." arXiv preprint arXiv:1906.06876 (2019). [Github] [Paper]

DSP-FWA: Li Y, Lyu S. Exposing DeepFake Videos By Detecting Face Warping Artifacts[J]. arXiv: Computer Vision and Pattern Recognition, 2018. [Github] [Paper]

FWA: Li, Yuezun, and Siwei Lyu. "Exposing deepfake videos by detecting face warping artifacts." arXiv preprint arXiv:1811.00656 (2018). [Github] [ Paper]

MesoNet: Yang, Xin, Yuezun Li, and Siwei Lyu. "Exposing deep fakes using inconsistent head poses." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019. [Github] [Paper]

Upconv: Durall, Ricard, Margret Keuper, and Janis Keuper. "Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020. [Github] [Paper]

VA: Matern, Falko, Christian Riess, and Marc Stamminger. "Exploiting visual artifacts to expose deepfakes and face manipulations." 2019 IEEE Winter Applications of Computer Vision Workshops (WACVW). IEEE, 2019. [Github] [Paper]

XceptionNet: Rossler, Andreas, et al. "Faceforensics++: Learning to detect manipulated facial images." Proceedings of the IEEE International Conference on Computer Vision. 2019. [Github] [Paper]

CNNDetection: Wang S, Wang O, Zhang R, et al. CNN-generated images are surprisingly easy to spot... for now.[J]. arXiv: Computer Vision and Pattern Recognition, 2019. [Github] [Paper]

Selim: 1st of the DFDC competition [Github]

WM: 2nd of the DFDC competition [Github]