Abstract
Facial processing technology's ability to generate realistic human faces poses significant societal risks when exploited maliciously. Deep face fraud detection relies on deep learning to meticulously scrutinize the manipulation sequence of fake faces, uncovering deceptive traces. This study focuses on Detecting Sequential DeepFake Operations (Seq-DeepFake), transforming the face detection task into an image-to-sequence exploration.To enhance detection accuracy, this paper introduces a Seq-DeepFake detection method. The Seq-DeepFake Transformer model's activation function is refined, incorporating the Rectified Randomized Leaky Unit (RReLU) to address learning rate challenges associated with negative input values. Furthermore, diverse attention mechanism modules are integrated into the backbone network, forming the innovative CLSP-Resnet-50 model. Experimental results demonstrate the efficacy of the enhanced Seq-DeepFake model, employing two evaluation metrics on a deepfake dataset, showcasing improved accuracy. Comparative analysis against other real and fake face detection methods substantiates the effectiveness of the Seq-DeepFake model.
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Acknowledgments
This work was partially supported by the National Natural Science Foundation of China (No. 6202780103), by the Guangxi Science and Technology Project (No. AB22035052), Guangxi Key Laboratory of Image and Graphic Intelligent Processing Project (Nos. GIIP2211, GIIP2308), by the Innovation Project of GUET Gurduate Education (No. 2023YCXB09).
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Deng, Z., You, K., Yang, R., Hu, X., Chen, Y. (2024). An Improved Seq-Deepfake Detection Method. In: Huang, DS., Premaratne, P., Yuan, C. (eds) Applied Intelligence. ICAI 2023. Communications in Computer and Information Science, vol 2014. Springer, Singapore. https://doi.org/10.1007/978-981-97-0903-8_21
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