An adversarial learning framework with cross-domain loss for median filtered image restoration and anti-forensics

Abstract

As an ill-posed problem, median filtered (MF) image restoration and anti-forensics still face a challenge in improving the visual quality and deceiving the forensic detectors simultaneously. MF image restoration mainly aims at image visual quality recovery, while MF image anti-forensics primarily focuses on deceiving the existing forensic algorithms. Median filtering is popularly used as an operation for image denoising and smoothing. It is also employed by image anti-forensics to conceal or remove the traces of other image manipulating operations, such as JPEG blocking artifacts and the periodicity introduced by image resampling. This work proposes an adversarial learning framework that introduces loss terms spanning three domains for MF image restoration and anti-forensics. The differences of high-frequency coefficients in the discrete wavelet transform (DWT) domain are introduced as a term of loss term to restore high-frequency components. Moreover, high-frequency subband variance loss in the discrete cosine transform (DCT) domain is proposed to make the statistical characteristics of the generated image closer to that of the original image. Together with the Huber loss in the spatial domain, a novel generator network is developed and trained to enhance information flow and feature reuse. A capsule network is applied as a discriminator for differentiating the generated image from the original version. We explore a solution through adversarial learning in an infinite set of possible solutions. Experimental results demonstrate that the proposed method is able to restore the visual quality of MF images with salt and pepper noise previously more effectively than the existing methods. What is more, our method achieves the best anti-forensic performance in comparison with the existing state-of-the-art MF image anti-forensic methods, while enhancing the quality of anti-forensically restored images.