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首页> 外文期刊>International Journal of Pattern Recognition and Artificial Intelligence >Convolutional Neural Networks for Steganalysis via Transfer Learning
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Convolutional Neural Networks for Steganalysis via Transfer Learning

机译：通过转移学习进行卷积分析的卷积神经网络

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摘要

Recently, a large number of studies have shown that Convolutional Neural Networks are effective for learning features automatically for steganalysis. This paper uses the transfer learning method to help the training of CNNs for steganalysis. First, a Gaussian high-pass filter is designed for pretreatment of the images, that can enhance the weak stego noise in the stegos. Then, the classical Inception-V3 model is improved, and the improved network is used for steganalysis through the method of transfer learning. In order to test the effectiveness of the developed model, two spatial domain content-adaptive steganographic algorithms WOW and S-UNIWARD are used. The results imply that the proposed CNN achieves a better performance at low embedding rates compared with the SRM with ensemble classifiers and the SPAM implemented with a Gaussian SVM on BOSSbase. Finally, a steganalysis system based on the trained model was designed. Through experiments, the generalization ability of the system was tested and discussed.
机译：最近，大量研究表明，卷积神经网络可有效地自动学习特征以进行隐写分析。本文使用转移学习方法来帮助CNN进行隐写分析训练。首先，高斯高通滤波器被设计用于图像的预处理，这可以增强隐身中的隐身噪声。然后，改进了经典的Inception-V3模型，并通过转移学习的方法将改进后的网络用于隐写分析。为了测试所开发模型的有效性，使用了两种空间域内容自适应隐写算法WOW和S-UNIWARD。结果表明，与具有集成分类器的SRM和在BOSSbase上使用高斯SVM实现的SPAM相比，所提出的CNN在低嵌入率下可获得更好的性能。最后，设计了基于训练模型的隐写分析系统。通过实验，对系统的泛化能力进行了测试和讨论。

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