AG-Net: an Advanced General CNN model for Steganalysis
AG-Net: an Advanced General CNN model for Steganalysis
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DOI：
10.1109/access.2022.3150276
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Han Zhang;Fuxian Liu;Zhihua Song;Xiaofeng Zhang;Yongmei Zhao
中科院分区：
文献类型：
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作者：
Han Zhang;Fuxian Liu;Zhihua Song;Xiaofeng Zhang;Yongmei Zhao
由于深度卷积神经网络（DCNN）的进步，隐写术在过去几年中取得了很大的进展，DCNN已成功用于多域。相应地，隐写分析模型的性能不可避免地遇到了瓶颈，因为基于CNN的隐写模型表现得更好。在本文中，我们提出了一种用于隐写分析的高级通用卷积神经网络（AG-Net）来解决这个问题。我们首先设计了一个对抗模块来提取和比较隐藏图像的特征，这是从一个未知的隐写网络捕获。然后，根据前一个模块的特征比较，构建相邻两个对抗模块之间的关联，积累隐藏图像与隐藏图像之间的中高层特征差异。第三，将最后一个对抗模块的丢失信息经过批量归一化和标量化后传递到softmax层，对隐写图像进行分类和检测。大量的实验和评估表明，所提出的AG-Net可以实现有前途的性能，以应对不同的具有挑战性的隐写算法。
Steganography has made great progress over the past few years due to the advancement of deep convolutional neural networks (DCNNs), which have been successfully used to multi-domains. Correspondingly the performance of steganalysis models inevitably encounters a bottleneck since the CNN based steganography models perform better. In this paper, we propose an Advanced General convolutional neural Network for steganalysis (AG-Net) to address this problem. We firstly design a confrontation module to extract and compare features of cover and stego images, which are captured from an unknown steganography network. Then, we construct the association between two adjacent confrontation modules according to the feature comparison of the previous module, to accumulate the differences of mid- and high-level features between the cover and stego images. Thirdly, we deliver the loss of the last confrontation module to a softmax layer after batch normalization and scalarization, to classify and detect stego images. Extensive experiments and evaluations demonstrate that the proposed AG-Net can achieve promising performance in response to different challenging steganographic algorithms.