Face tampering detection based on spatiotemporal attention residual network

Zhi Cai; Weimin Wei; Fanxing Meng; Chang Liu

doi:10.1117/12.2644654

12 October 2022 Face tampering detection based on spatiotemporal attention residual network

Zhi Cai, Weimin Wei, Fanxing Meng, Chang Liu

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123420G (2022) https://doi.org/10.1117/12.2644654
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

Fake technology has evolved to the point where fake faces are increasingly difficult to distinguish from real ones. If the forged face videos spread wildly on social media, social unrest or personal reputation damage may lead to social unrest. A face tampering detection method (RALNet) with spatiotemporal attention residual network is designed to reduce the misuse of face data due to malicious dissemination. Firstly, we propose a process to extract video face data, which reduces the interference of irrelevant information and improves the utilization of data processing. Then, based on the characteristics of incoherence and inconsistency in spatial and temporal information of tampered videos, the spatial domain features and temporal domain features of the target face video are extracted by introducing an attention mechanism of residual network and long short-term memory network to classify the targets as true or fake. The experimental results show that the method can effectively detect whether the face data is tampered, and its detection accuracy is better than other methods. In addition, it also achieves good performance in terms of recall, precision, and F1 score.

Citation Download Citation

Zhi Cai, Weimin Wei, Fanxing Meng, and Chang Liu "Face tampering detection based on spatiotemporal attention residual network", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123420G (12 October 2022); https://doi.org/10.1117/12.2644654

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KEYWORDS

Video

Facial recognition systems

Data modeling

Convolutional neural networks

Feature extraction

Performance modeling

Machine learning

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