TVN: Detect Deepfakes Images using Texture Variation Network

Abstract

Face manipulation technology is rapidly developing, making it impossible for human eyes to recognize fake face photos. Convolutional Neural Network (CNN) discriminators, on the other hand, can fast achieve high accuracy in distinguishing fake/real face photos. In this paper, we look at how CNN models discern between fake and real faces. Face forgery detection relies heavily on Texture Variation Network (TVN) information, according to our findings. We propose a new model, TVN, for robust face fraud detection, based on Convolution and pyramid pooling (PP), as a result of the aforesaid discovery. To produce a stationary representation of composition difference information, Convolution combines pixel intensity and pixel gradient information. Simultaneously, multi-scale information fusion based on the PP can prevent the texture features from being destroyed. Our TVN beats previous techniques on numerous databases, including Faceforensics++, DeeperForensics-1.0, Celeb-DF, and DFDC. The TVN is more resistant to image distortion, such as JPEG compression and blur, which is critical in the wild.

Keywords

• Texture Variation Network, CNN, forgery