Abstract
Multi-class steganalysis utilizes multi-class classification methods to predict the category of steganographic schemes used for generating stego files. In this paper we propose a novel multi-class approach towards more efficiently classifying JPEG stego-images with CC-JRM features. Because CC-JRM has successfully cooperates with ensemble classifier in detecting the presence of stego images, we modified ensemble classifier for multi-class steganalysis. The ideas of performing ensemble in different steps results in two schemes in our proposed method. These two schemes are based on different multi-class ensemble strategies, and utilize linear SVM as base classifier. The experimental results shows our methods received better results with less computing cost compared to other multi-class steganalysis method.
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Notes
- 1.
In our experiments, training 5-classes classifiers consumes about 10G memory.
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Acknowledgements
This work was supported by the NSFC under 61170281, 61303259 and 61303254, the Strategic Priority Research Program of CAS under XDA06030600, and the Project of IIE, CAS, under Y4Z0031102 and Y3Z0071502.
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Zhu, J., Guan, Q., Zhao, X. (2015). Multi-class JPEG Image Steganalysis by Ensemble Linear SVM Classifier. In: Shi, YQ., Kim, H., Pérez-González, F., Yang, CN. (eds) Digital-Forensics and Watermarking. IWDW 2014. Lecture Notes in Computer Science(), vol 9023. Springer, Cham. https://doi.org/10.1007/978-3-319-19321-2_36
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