Non-blind steganalysis

Non-blind steganalysis

연구 분야: Strategies

학회: ARES '20: Proceedings of the 15th International Conference on Availability, Reliability and Security

The increasing digitization offers new ways, possibilities and needs for a secure transmission of information. Steganography and its analysis constitute an essential part of IT-Security. In this work we show how methods of blind-steganalysis can be improved to work in non-blind scenarios. The main objective was to examine how to take advantage of the knowledge of reference images to maximize the accuracy-rate of the analysis. Therefore we evaluated common stego-tools and their embedding algorithms and established a dataset of 353110 images. The images have been applied to test the potency of the improved methods of the non-blind steganalysis. The results show that the accuray can be significantly improved by using cover-images to produce reference images. Also the aggregation of the outcomes has shown to have a positive impact on the accuracy. Particularly noteworthy is the correlation between the qualities of the stego- and cover-images. Only by consindering both, the accuracy could strongly be improved. Interestingly the difference between both qualities also has a deep impact on the results.