Zhipeng Bin¹, Huihuang Zhao^1,2,*, Xiaoman Liang^1,2, Wenli Chen¹

Intelligent Automation & Soft Computing, Vol.37, No.1, pp. 971-983, 2023, DOI:10.32604/iasc.2023.037270

Abstract The main challenges in face swapping are the preservation and adaptive superimposition of attributes of two images. In this study, the Face Swapping Attention Network (FSA-Net) is proposed to generate photorealistic face swapping. The existing face-swapping methods ignore the blending attributes or mismatch the facial keypoint (cheek, mouth, eye, nose, etc.), which causes artifacts and makes the generated face silhouette non-realistic. To address this problem, a novel reinforced multi-aware attention module, referred to as RMAA, is proposed for handling facial fusion and expression occlusion flaws. The framework includes two stages. In the first stage, a novel attribute encoder is proposed… More >

Xingwang Ju^*

Journal of Cyber Security, Vol.2, No.4, pp. 197-207, 2020, DOI:10.32604/jcs.2020.014310

Abstract Due to the power of editing tools, new types of fake faces are being created and synthesized, which has attracted great attention on social media. It is reasonable to acknowledge that one human cannot distinguish whether the face is manipulated from the real faces. Therefore, the detection of face manipulation becomes a critical issue in digital media forensics. This paper provides an overview of recent deep learning detection models for face manipulation. Some public dataset used for face manipulation detection is introduced. On this basis, the challenges for the research and the potential future directions are analyzed and discussed. More >