基于深度残差网络的语音隐写分析方法

针对目前以WAV格式语音为载体的最低有效位(LSB)隐写方法的检测性能较低的问题,提出了一种基于深度残差网络的语音隐写分析方法.首先,通过多组高通滤波器组成的固定卷积层来计算输入语音信号的残差信号,并利用截断线性激活单元对得到的残差信号进行截断操作;然后,通过卷积层与设计的残差块的堆叠来构建深度网络,以提取深层次的隐写...

Speech steganalysis method based on deep residual network

Concerning the low detection performance of the Least Significant Bit (LSB) steganography method on WAV-format speech, a speech steganalysis method based on deep residual network was proposed. First, the residual signal of the input speech signal was calculated through a fixed convolutional layer composed of multiple sets of high-pass filters, and a truncated linear unit was adopted to perform truncation to the obtained residual signal. Then, a deep network was constructed by stacking the convolutional layer and the designed residual block to extract the deep feature information of steganography. Finally, the final classification result was output by the classifier composed of the fully connected layer and Softmax layer. Experimental results under the different secret information embedding rates of two steganography methods,Hide4PGP (Hide 4 Pretty Good Privacy) and LSBmatching (Least Significant Bit matching), show that compared with the exising Convolutional Neural Network (CNN)-based steganalysis methods, the proposed method can achieve better performance, and compared with LinNet, the proposed method has the detection accuracy increased by 7 percentage points on detecting Hide4PGP with the embedding rate of 0.1 bps (bit per sample).