摘要:近年来，基于深度学习的空域隐写分析研究在高嵌入率下已经取得了较好的成果，但是对低嵌入率的检测效果还不太理想.因此设计了一种卷积神经网络结构，使用SRM滤波器进行预处理来获取隐写噪声残差，采用3个卷积层并对卷积核大小进行合理设计，通过适当选择批量归一化操作和激活函数来提升网络的性能.实验结果表明：与现有方法相比，所提出的网络结构对WOW，S-UNIWARD和HILL这3种常见的空域内容自适应隐写算法取得了更好的检测效果，且在低嵌入率0.2bpp，0.1bpp和0.05bpp下的检测效果有非常明显的提升.还提出了逐步迁移（step by step）的迁移学习方法，进一步提升低嵌入率条件下的隐写分析效果.



Abstract:In recent years, the research of spatial steganalysis based on deep learning has achieved sound results under high embedding rate, but the detection performance under low embedding rate is still not ideal. Therefore, a convolutional neural network structure is proposed, which uses the SRM filter for preprocessing to obtain implicit noise residuals, adopts three convolution layers and designs the size of convolution kernel reasonably, and selects appropriate batch normalization operations and activation functions to improve the network performance. The experimental results show that compared with the existing methods, the proposed network can achieve better detection performance for WOW, S-UNIWARD, and HILL, three common adaptive steganographic algorithms in spatial domain, and significant improvement in detection performance at low embedding rates of 0.2 bpp, 0.1 bpp, and 0.05 bpp. A step-by-step transfer learning method is also designed to further improve the steganalysis effect under low embedding rate conditions.



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