Detecting fake images by identifying potential texture difference

Fake detection has become an urgent task. Generative adversarial networks (GANs) extended to deep learning has shown its extraordinary ability in the fields of image, audio, and speech. But advanced technology benefits us, it also poses a threat to us when used in Cyber Crime. The Deepfake (common name for face manipulation methods) based on GANs can realize the replacement of different faces. Due to the development of GANs, faces generated by Deepfake can already be visually real. Deepfake can purposely replace any face to a different person, so that a fabricated event may be widely spread because of the convenience of the Internet, causing serious impacts such as personal attacks and cyber crime. Based on cutting-edge research, this paper proposes a intelligence forensic method of Deepfake detection. We first discover the subtle texture differences between real and fake image in image saliency, which shows difference in the texture of faces. To amplify this difference, we exploit guided filter with saliency map as guide map to enhance the texture artifacts caused by the post-processing and display the potential features of forgery. Resnetl8 classification network efficiently learns the exposed difference and finally realizes the real and fake detection of face images. We evaluate the performance of the method and experiments verify that the proposed method can achieve the state-of-the-art detection accuracy. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

Fake detection has become an urgent task. The rapid development of Deepfake technology poses a threat to network security. This paper proposes an intelligent forensic method for Deepfake detection, achieving state-of-the-art detection accuracy.