Embedding Probability Guided Network for Image Steganalysis
Qiangjie Li, Guorui Feng, Yanli Ren, Xinpeng Zhang
Abstract
Content-adaptive steganography embeds information into the cover image adaptively under the guidance of embedding probabilities (also known as selection channel) of the image elements, which can also be obtained by the steganalyzer. Therefore, some adaptive steganalysis schemes with selection channel have been proposed to detect content-adaptive steganography. Existing selection-channel-aware CNN-based steganalyzers usually incorporate the embedding probability into the first convolutional layer. They may fail to make full use of embedding probability information because this information incorporated into the first layer may disappear when it propagates to deep convolutional layers. To address this issue, we propose embedding probability guided module to adaptively enhance the features from different levels of network. Moreover, these embedding probability guided features from different levels are progressively integrated to jointly make final decisions. Experimental results prove that our approach can outperform existing selection-channel-aware approaches in both the spatial domain and JPEG domain.