Steganalysis using learned denoising kernels

Abstract

Steganalysis is the science for detecting steganographic traces in innocent-looking digital media like images, videos, etc. In recent literature, it has been observed that state-of-the-art image steganographic techniques such as S-UNIWARD, HUGO, WOW, etc. still remain undetected even with considerable embedding payload. Recently, the deep learning framework has been hugely successful in different computer vision applications like object detection, image classification, event detection, etc. Some recent deep learning-based works also show promising results for image steganalysis and have opened a new avenue for research. The current literature reveals that the steganalytic detector becomes more precise if trained on the residual error (embedding noise) domain. To get an accurate noise residual, it is required to predict the cover image precisely from the corresponding stego image. In this work, a denoising kernel has been learned to obtain a more precise noise residual. After that, a CNN based steganalytic detector is devised, which is trained using the noise residual to get a more precise detection. Experimental results show that the proposed scheme outperforms the state-of-the-art steganalysis schemes against the state-of-the-art steganographic approaches.