A two-stage fake face image detection algorithm with expanded attention

Shuai Wang, Hanling Zhang, Gaobo Yang, Zhiqing Guo, Jiyou Chen

Abstract: Convolutional neural networks (CNNs) have achieved impressive successes in fake face image detection. However, CNNs ignore tampering traces outside their attention scope. Moreover, example forgetting events can also pose negative impacts on the face forgery detection accuracy. To address these issues, this paper proposes an attention-expanded two-stage face forgery detector, named Attention-expanded Deepfake Spotter (ADS). In the first stage, the manipulated regions are preliminarily located by utilizing the Region Score Maps (RSMs) generated by the modified CNN. In the second stage, the Expanding and Undetectable Regions (EUR) loss function is designed to encourage another modified CNN to mine manipulation traces outside the manipulated areas exposed in the first stage. To fuse the manipulation traces extracted from different regions in the two stages and mitigate the problems caused by example forgetting events, RSM-weighted accumulation is adopted to integrate the detection information from both stages and obtain the final detection result. The proposed algorithm’s effectiveness for each component is analyzed through ablation experiments, and the method is evaluated on four publicly available datasets: FF++, HFF, DFDC, and Celeb-DF. The experimental results demonstrate that the proposed method has high detection rates and superior transferability, outperforming most existing algorithms.