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.

JPEG恢复和取证都可以消除JPEG压缩留下的伪像，并恢复JPEG压缩图像。然而，如何恢复JPEG压缩图像的高频细节以用于JPEG恢复以及如何在不牺牲JPEG反取证的视觉质量的情况下欺骗现有的JPEG压缩检测器仍然具有挑战性。为了解决这些问题，提出了一种具有新型损失功能的生成对抗网络（GAN）框架，用于JPEG恢复和反取证（JRA-GAN），以将JPEG压缩图像转换为重建图像。由于JPEG压缩会损害高频分量，因此在JRA-GAN中提出了测量交流分量损失的交流分量损失函数以恢复这些分量。为了防止法医检测，在JRA-GAN中还引入了校准损耗函数，以减轻生成的图像与其校准版本之间的高频子带中的方差。我们的实验结果表明，所提出的JPEG恢复方法在图像质量方面优于现有方法，并且JPEG反取证方案可实现更好的视觉质量和反取证性能，可与现有的最新反取证技术相媲美。法医方法。我们的代码可在以下页面中找到：https：//github.com/wujianyuan/JRG-GAN。JPEG的取证方案可实现更好的视觉质量和取证性能，可与现有的最新取证方法相提并论。我们的代码可在以下页面中找到：https：//github.com/wujianyuan/JRG-GAN。JPEG的取证方案可实现更好的视觉质量和取证性能，可与现有的最新取证方法相提并论。我们的代码可在以下页面中找到：https：//github.com/wujianyuan/JRG-GAN。