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DFDM - A DeepFakes Detection Model Based on Steganography Forensic Network

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Artificial Intelligence and Security (ICAIS 2020)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1253))

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Abstract

AI image tampering technology - DeepFakes has a huge impact on the authenticity of video information in the network, making “seeing is believing” no longer credible. Finding effective methods for detecting DeepFakes is very important. The existing detection methods mostly rely on artificially designed features. This paper first proposes an adaptive deep learning detection model - DFDM for DeepFakes. DFDM uses deep learning networks to adaptively extract features of forgery images, which is efficient when facing a large number of samples and avoid the limitations of traditional detection methods. DFDM refers to the structure of steganalysis and forensic networks and pays attention to the anomaly of pixel distribution in images. DFDM can identify the anomaly of image texture features better than the general classification network. DFDM abandons the residual processing in the steganographic detection network, and designs a custom convolution kernel suitable for identifying image texture features to improve the accuracy of model detection. In the model network structure, DFDM replaces the mean pooling layer in steganalysis networks with the max-pooling layer, maximally retaining the image texture features. The simulation results show that the detection accuracy of DFDM for full-face replacement reaches 0.99, and the detection accuracy for partial-face replacement reaches 0.98.

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Acknowledgements

This work is supported by the National Key R&D Program of China (2017YFB0802703), Research on the education mode for complicate skill students in new media with cross specialty integration (22150117092), Major Scientific and Technological Special Project of Guizhou Province (20183001), Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data (2018BDKFJJ014), Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data (2018BDKFJJ019) and Open Foundation of Guizhou Provincial Key Laboratory of Public Big Data (2018BDKFJJ022).

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Authors and Affiliations

  1. Beijing University of Posts and Telecommunications, Beijing, 100876, China

    Yang Zeng, Yu Yang & Rui Zhan

  2. College of New Media, Beijing Institute of Graphic Communication, Beijing, 102600, China

    Xiaochun Guo

Authors
  1. Yang Zeng
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  2. Xiaochun Guo
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  3. Yu Yang
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  4. Rui Zhan
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Corresponding author

Correspondence to Yu Yang .

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Editors and Affiliations

  1. Nanjing University of Information Science and Technology, Nanjing, China

    Xingming Sun

  2. Nanjing University of Information Science and Technology, Nanjing, China

    Jinwei Wang

  3. Purdue University, West Lafayette, IN, USA

    Elisa Bertino

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Zeng, Y., Guo, X., Yang, Y., Zhan, R. (2020). DFDM - A DeepFakes Detection Model Based on Steganography Forensic Network. In: Sun, X., Wang, J., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2020. Communications in Computer and Information Science, vol 1253. Springer, Singapore. https://doi.org/10.1007/978-981-15-8086-4_51

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  • DOI: https://doi.org/10.1007/978-981-15-8086-4_51

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-8085-7

  • Online ISBN: 978-981-15-8086-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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