Deep residual learning for image steganalysis
Deep residual learning for image steganalysis
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DOI：
10.1007/s11042-017-4440-4
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发表时间：
2018-05-01
影响因子：
3.6
通讯作者：
Liu, Yan
中科院分区：
文献类型：
--
作者：
Wu, Songtao;Zhong, Shenghua;Liu, Yan
图像隐写分析是区分无辜图像和那些隐藏信息的可疑图像。这项任务对于现代自适应隐写术来说非常具有挑战性，因为消息隐藏引起的修改非常小。最近的研究表明，卷积神经网络（CNN）表现出比传统隐写分析方法更优越的性能。遵循这个想法，我们提出了一种基于残差学习的新型 CNN 图像隐写分析模型。与现有基于 CNN 的方法相比，所提出的基于深度残差学习的网络 (DRN) 显示出两个有吸引力的特性。首先，该模型通常包含大量网络层，这被证明可以有效捕获数字图像的复杂统计数据。其次，DRN中的残差学习保留了来自秘密消息的隐写信号，这对于区分封面图像和隐写图像非常有利。对标准数据集的综合实验表明，DRN 模型可以高精度检测最先进的隐写算法。它还优于经典的富模型方法和最近提出的几种基于 CNN 的方法。
Image steganalysis is to discriminate innocent images and those suspected images with hidden messages. This task is very challenging for modern adaptive steganography, since modifications due to message hiding are extremely small. Recent studies show that Convolutional Neural Networks (CNN) have demonstrated superior performances than traditional steganalytic methods. Following this idea, we propose a novel CNN model for image steganalysis based on residual learning. The proposed Deep Residual learning based Network (DRN) shows two attractive properties than existing CNN based methods. First, the model usually contains a large number of network layers, which proves to be effective to capture the complex statistics of digital images. Second, the residual learning in DRN preserves the stego signal coming from secret messages, which is extremely beneficial for the discrimination of cover images and stego images. Comprehensive experiments on standard dataset show that the DRN model can detect the state of arts steganographic algorithms at a high accuracy. It also outperforms the classical rich model method and several recently proposed CNN based methods.