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| Current image steganalysis models usually extract global features to distinguish between natural image and steganographic image. However, the global features that represent the differences between images are closely related to image properties, such as embedding payload, which will mostly lead to severe performance deterioration if there is a mismatch between training and detecting sources. On the contrary, the local feature changes of the moving window in an image are mainly affected by data hiding, but not by the image properties. Therefore, a detection method based on local difference analysis is proposed in this paper. By analyzing the smoothness changes of local moving blocks, the impact of payload mismatch on steganalysis accuracy is reduced. In addition, this paper also introduces long short-term memory network technique into image steganalysis, and proposes a new detection model called ILDA-Net (Image Local Difference Analysis Net), which analyzes the changes in the local residual sequence to achieve steganalysis. Experiments on LSB and WOW steganography algorithms show that ILDA-Net can effectively reduce the impact of payload mismatch on network detection performance. |
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| Keywords:deep learning; image steganalysis; payload mismatch; long short-term memory; local difference analysis |
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