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.

隐写分析是一种用于在看起来无害的数字媒体（例如图像，视频等）中检测隐写痕迹的科学。在最近的文献中，已经观察到最先进的图像隐写技术，例如S-UNIWARD，HUGO，WOW，即使有相当大的嵌入负载，仍然无法检测到此类错误。最近，深度学习框架在不同的计算机视觉应用程序中取得了巨大的成功，例如对象检测，图像分类，事件检测等。一些最近的基于深度学习的工作也显示了图像隐写分析的有希望的结果，并开辟了新的研究途径。当前文献表明，如果对残差（嵌入噪声）域进行训练，隐写检测器将变得更加精确。为了获得准确的噪声残留，需要根据相应的隐身图像精确地预测封面图像。在这项工作中，已经学习了去噪内核以获得更精确的噪声残留。之后，设计了基于CNN的隐身检测器，使用残留的噪声对其进行训练，以实现更精确的检测。实验结果表明，相对于最新的隐写方法，该方案优于最新的隐写分析方案。