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A framework of generative adversarial networks with novel loss for JPEG restoration and anti-forensics

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Abstract

Both JPEG restoration and anti-forensics remove the artifacts left by JPEG compression, and recover the JPEG compressed image. However, how to restore the high-frequency details of a JPEG compressed image for JPEG restoration and how to deceive the existing JPEG compression detectors without sacrificing visual quality in JPEG anti-forensics remain challenging. To address these issues, a framework of generative adversarial networks (GAN) with novel loss functions for JPEG restoration and anti-forensics (JRA-GAN) is proposed to allow a JPEG compressed image to be translated into a reconstructed one. Since JPEG compression causes impairment to high-frequency components, an alternating current (AC)-component loss function that measures the loss of AC components is proposed in JRA-GAN to recover these components. To prevent forensic detection, a calibration loss function is also introduced in JRA-GAN to mitigate the variance gap in the high-frequency subbands between a generated image and its calibrated version. Our experimental results demonstrate that the proposed JPEG restoration method outperforms existing methods in terms of image quality, and the JPEG anti-forensic scheme achieves better visual quality and anti-forensic performance that is comparable to the existing state-of-the-art anti-forensic methods. Our code is available in this page: https://github.com/wujianyuan/JRG-GAN.

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Acknowledgements

This work was supported by NSFC under Grant 62072484, 61772571 and Chinese national key research and development project under Grant 2019QY2203. This work was also supported by China Postdoctoral Science Foundation (Grant no. 2020M683051) and the Fundamental Research Funds for the Central Universities, Sun Yat-sen University (Grant no. 67000-31610131).

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Authors and Affiliations

  1. Guangdong Key Laboratory of Information Security Technology, Sun Yat-sen University, Guangzhou, 510006, China

    Jianyuan Wu, Xiangui Kang & Jianhua Yang

  2. Information Technology Key Laboratory of the Ministry of Education, School of Electronic and Information Engineering, Sun Yat-sen University, Guangzhou, 510006, China

    Wei Sun

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  1. Jianyuan Wu
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  2. Xiangui Kang
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  3. Jianhua Yang
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  4. Wei Sun
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Correspondence to Xiangui Kang or Jianhua Yang.

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Communicated by J. Dittmann.

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Wu, J., Kang, X., Yang, J. et al. A framework of generative adversarial networks with novel loss for JPEG restoration and anti-forensics. Multimedia Systems 27, 1075–1089 (2021). https://doi.org/10.1007/s00530-021-00778-6

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