IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel
IAS-CNN: Image adaptive steganalysis via convolutional neural network combined with selection channel
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DOI：
10.1177/1550147720911002
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发表时间：
2020-03-01
影响因子：
2.3
通讯作者：
Chen, Yuwei
中科院分区：
文献类型：
--
作者：
Jin, Zhujun;Yang, Yu;Chen, Yuwei
隐写术有利于通信安全，但滥用隐写术会带来许多潜在的危险。因此，隐写分析在防止隐写术的滥用方面起着重要的作用。目前基于深度学习的隐写分析一般参数较多，与自适应隐写算法的针对性较弱。在这篇文章中，我们提出了一个名为IAS-CNN的轻量级卷积神经网络，其目标是图像自适应隐写分析。针对人工设计残差提取滤波器的局限性，采用自学习滤波器的方法。该方法首先利用空间丰富模型中的高通滤波器初始化第一层的权值，然后通过网络的反向传播更新权值。此外，将选择通道的知识融入IAS-CNN中，通过将嵌入概率图输入IAS-CNN中，增强隐写概率高的区域中的残差。此外，IAS-CNN被设计为轻量级网络，以减少资源消耗并提高处理速度。实验结果表明，IAS-CNN在隐写分析中表现良好。IAS-CNN不仅在S-UNIWARD隐写分析中具有与YedroudjNet相似的性能，而且具有更少的参数和卷积计算。
Steganography is conducive to communication security, but the abuse of steganography brings many potential dangers. And then, steganalysis plays an important role in preventing the abuse of steganography. Nowadays, steganalysis based on deep learning generally has a large number of parameters, and its pertinence to adaptive steganography algorithms is weak. In this article, we propose a lightweight convolutional neural network named IAS-CNN which targets to image adaptive steganalysis. To solve the limitation of manually designing residual extraction filters, we adopt the method of self-learning filter in the network. That is, a high-pass filter in spatial rich model is applied to initialize the weights of the first layer and then these weights are updated through the backpropagation of the network. In addition, the knowledge of selection channel is incorporated into IAS-CNN to enhance residuals in regions that have a high probability for steganography by inputting embedding probability maps into IAS-CNN. Also, IAS-CNN is designed as a lightweight network to reduce the consumption of resources and improve the speed of processing. Experimental results show that IAS-CNN performs well in steganalysis. IAS-CNN not only has similar performance with YedroudjNet in S-UNIWARD steganalysis but also has fewer parameters and convolutional computations.