深度学习空域隐写分析的预处理层

Preprocessing Layer in Spatial Steganalysis Based on Deep Learning

在一种具有预处理层的卷积神经网络模型基础上,对其高通滤波器预处理层进行改进,采用一组导数滤波器以获得线性及非线性残差图像,并对残差图像进行量化和截断操作,从而更加有效地提取图像特征.实验结果表明,与已有方法相比,尽管所提的3种方法对各空域隐写算法及各嵌入率下的性能表现并不一致,但这些方法均能显著提升隐写分析检测率.对于检测嵌入率为0.4 bpp的S-UNIWARD隐写算法,检测正确率提高了6%.

In this paper, we propose some preprocessing methods to improve the perfor- mance of a well-designed convolution neural network based on the preprocessed layer. In the proposed methods, linear and nonlinear residuals are obtained by employing a set of derivative filters, and then quantized and truncated for the effective extraction. Experi- mental results show that the detection performances with the three proposed preprocessing methods are all improved. Although the improvements are not consistence under different spatial steganographic algorithms and different embedding rates. The detection perfor- mance is 6% better than the prior work for S-UNIWARD at 0.4bpp.