Selective Domain-Invariant Feature for Generalizable Deepfake Detection

arXiv:2403.12707v1 Announce Type: new
Abstract: With diverse presentation forgery methods emerging continually, detecting the authenticity of images has drawn growing attention. Although existing methods have achieved impressive accuracy in training dataset detection, they still perform poorly in the unseen domain and suffer from forgery of irrelevant information such as background and identity, affecting generalizability. To solve this problem, we proposed a novel framework Selective Domain-Invariant Feature (SDIF), which reduces the sensitivity to face forgery by fusing content features and styles. …

abstract accuracy arxiv attention authenticity cs.cv dataset deepfake detection diverse domain feature forgery identity images information presentation training type