Abstract
In this paper, a feature-level fusion based approach is proposed for blind image steganalysis. We choose three types of typical higher-order statistics as the candidate features for fusion and make use of the Boosting Feature Selection (BFS) algorithm as the fusion tool to select a subset of these candidate features as the new fusion feature vector for blind image steganalysis. Support vector machines are then used as the classifier. Experimental results show that the fusion based approach increases the blind detection accuracy and also provides a good generality by identifying an untrained stego-algorithm. Moreover, we evaluate the performance of our candidate features for fusion by making some analysis of the components of the fusion feature vector in our experiments.
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Dong, J., Chen, X., Guo, L., Tan, T. (2008). Fusion Based Blind Image Steganalysis by Boosting Feature Selection. In: Shi, Y.Q., Kim, HJ., Katzenbeisser, S. (eds) Digital Watermarking. IWDW 2007. Lecture Notes in Computer Science, vol 5041. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92238-4_8
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DOI: https://doi.org/10.1007/978-3-540-92238-4_8
Publisher Name: Springer, Berlin, Heidelberg
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