Forgery face detection method based on multi-domain temporal features mining - Chuntao ZHU; Chengxi YIN; Bolin ZHANG; Qilin YIN; Wei LU (网络与信息安全学报)
Financial technology has greatly facilitated people’s daily life with the continuous development of computer technology in the financial services industry.However, digital finance is accompanied by security problems that can be extremely harmful.Face biometrics, as an important part of identity information, is widely used in payment systems, account registration, and many other aspects of the financial industry.The emergence of face forgery technology constantly impacts the digital financial security system, posing a threat to national asset security and social stability.To address the security problems caused by fake faces, a forgery face detection method based on multi-domain temporal features mining was proposed.The tampering features were distinguished and enhanced based on the consistency of statistical feature data distribution and temporal action trend in the temporal features of videos existing in the spatial domain and frequency domain.Temporal information was mined in the spatial domain using an improved LSTM, while in the frequency domain, temporal information existing in different frequency bands of the spectrum was mined using 3D convolution layers.The information was then fused with the tampering features extracted from the backbone network, thus effectively distinguishing forged faces from real ones.The effectiveness of the proposed method was demonstrated on mainstream datasets.