Image Steganography Detection Based on Multi-Scale Feature Fusion and Global Covariance Pooling
A steganalysis model based on multi-scale feature fusion and global covariance pooling is proposed in response to the feature map information loss caused by pooling and other operations in current image steganography deep learning models,particularly the high-order statistic loss by the global average pooling.First,the multi-layer perceptual convolution's large-scale convolution kernel is changed to a small-scale convolution kernel,improving the capacity to express features while lowering the number of parameters.Second,to miti-gate the detailed information loss brought on by pooling and other operations,a multi-scale feature fusion module based on the dilated convolution is employed.Finally,to improve the capacity to describe precise features,a global covariance pooling is used,which outputs the second-order statistical covariance.In comparison to established models like Xu-Net,Yedroudj-Net,and Zhu-Net,the suggested model increases detection accuracy.It even surpasses the newly proposed Zhu-Net by 2.4%to 7.30%for various steganographic tech-niques and embedding rates.
万方数据
维普
