Detecting DeepFake, FaceSwap and Face2Face facial forgeries using frequency CNN

摘要

The face of a person plays a vital role in any communication or visual content. To enhance this visual content, popular and easy accessible editing tools are used. However, there malicious usage is spreading disharmony in the society, by tampering video evidences, defaming a person's image etc. Therefore a robust detection method is required to authenticate the visual content. Thus, a novel method is proposed to detect facial forgeries. The proposed method extracts faces from a target video and convert them into frequency domain using two dimensional global discrete Cosine transform (2D- GDCT). Thereafter, a 3 layered frequency convolutional neural network (fCNN) is employed to detect forged facial image. The proposed method is trained and tested on FaceForensics++ dataset and Celeb-DF(v2) dataset. In addition, its robustness is evaluated on standardized benchmark dataset and compared with the state-of-the-art methods to prove its effectiveness.