Abstract More and more convolution neural network (CNN) models are used for image steganalysis, which show superior performances than traditional steganalytic methods. However, no researches on halftone image steganalysis by CNN have yet been carried. In this paper, a novel residual CNN model with stego-signal diffusion for halftone image steganalysis is proposed and achieves state-of-the-art detection accuracy. Considering inverse halftoning can reconstruct the gray-scale image from the halftone image, inverse halftoning is used to preprocess the halftone image, which can diffuse the stego-signal to neighboring pixels. As a result, the difference between the cover and stego image is magnified on the texture. Then, the residual block is utilized to construct the CNN model, since it could preserve the stego-signal better than plain network, and the magnified difference allows the network to better identify cover and stego images. A series of experiments are conducted on a large-scale dataset. The detection accuracy is improved by the magnified difference, and our proposed model outperforms the previous methods.

中文翻译：

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具有隐写信号扩散的半色调图像隐写分析的深度残差网络

摘要 越来越多的卷积神经网络（CNN）模型被用于图像隐写分析，其表现出优于传统隐写分析方法的性能。然而，目前还没有进行CNN对半色调图像隐写分析的研究。在本文中，提出了一种用于半色调图像隐写分析的具有隐写信号扩散的新型残差 CNN 模型，并实现了最先进的检测精度。考虑到反半色调可以从半色调图像重建灰度图像，反半色调用于对半色调图像进行预处理，可以将隐写信号扩散到相邻像素。结果，封面和隐写图像之间的差异在纹理上被放大了。然后，利用残差块来构建 CNN 模型，因为它比普通网络更好地保留了隐写信号，放大的差异使网络能够更好地识别封面和隐写图像。在大规模数据集上进行了一系列实验。通过放大的差异提高了检测精度，我们提出的模型优于以前的方法。