Can I read Learning Self-Consistency for Deepfake Detection on EtoBox?

Learning Self-Consistency for Deepfake Detection by Tianchen Zhao; Xiang Xu; Mingze Xu; Hui Ding; Yuanjun Xiong; Wei Xia is a scholarly article available to read on EtoBox.

What is Learning Self-Consistency for Deepfake Detection about?

We propose a new method to detect deepfake images using the cue of the source feature inconsistency within the forged images. It is based on the hypothesis that images' distinct source features can be preserved and extracted after going through state-of-the-art deepfake generation processes. We introduce a novel representation learning approach, called pair-wise self-consistency learning (PCL), for training ConvNets to extract these source features and detect deepfake images. It is accompanied by a new image synthesis approach, called inconsistency image generator (I2G), to provide richly annotated training data for PCL. Experimental results on seven popular datasets show that our models improve averaged AUC over the state of the art from 96.45% to 98.

Author

Tianchen Zhao; Xiang Xu; Mingze Xu; Hui Ding; Yuanjun Xiong; Wei Xia

Publisher

IEEE

Published

2021

Language

EN