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

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: .