A bidirectional-LSTM method based on temporal features for deep fake face detection in videos

Shengqing Pei; Yu Wang; Bende Xiao; Shengchang Pei; Yaqi Xu; Yifan Gao; Jianbin Zheng

doi:10.1117/12.2653461

27 September 2022 A bidirectional-LSTM method based on temporal features for deep fake face detection in videos

Shengqing Pei, Yu Wang, Bende Xiao, Shengchang Pei, Yaqi Xu, Yifan Gao, Jianbin Zheng

Proceedings Volume 12346, 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022); 123461B (2022) https://doi.org/10.1117/12.2653461
Event: 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), 2022, Kunming, China

Abstract

The technology of deep fake face detection in videos is particularly important in the field of internet and finance. In order to solve the significant problem of the spread of misinformation, it is essential that deep fake face detection methods robust manipulation. This paper proposes a Bidirectional-LSTM method based on temporal features for deep fake face detection in videos. Bidirectional-LSTM method is one of deep learning methods, which has positive effect on exploiting the temporal information in videos. Facial expressions and muscle movements play an important role in an individual’s speaking style. The continuity features of temporality suffer from the working process of the fake face in videos so that deep fake detection can be used for the fake face videos. Compared with previous methods such as SVM, BP neural network, the proposed method takes less time to train the model and have a good performance on accuracy. The experimental results show that the accuracy reaches 82.65% on DFDC data set. The proposed method has an excellent effect on the robustness in detecting compressed videos with noise interference.

Citation Download Citation

Shengqing Pei, Yu Wang, Bende Xiao, Shengchang Pei, Yaqi Xu, Yifan Gao, and Jianbin Zheng "A bidirectional-LSTM method based on temporal features for deep fake face detection in videos", Proc. SPIE 12346, 2nd International Conference on Information Technology and Intelligent Control (CITIC 2022), 123461B (27 September 2022); https://doi.org/10.1117/12.2653461

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KEYWORDS

Video

Facial recognition systems

Convolution

Neural networks

Data modeling

Feature extraction

Detection and tracking algorithms

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