Abstract
Cover source mismatch (CSM) occurs when a detection classifier for steganalysis trained on objects from one cover source is tested on another source. However, it is very hard to find the same sources as suspicious images in real-world applications. Therefore, the CSM is one of the biggest stumbling blocks to hinder current classifier based steganalysis methods from becoming practical. On the other hand, the texture complexity (of digital images) also plays an important role in affecting the detection accuracy of steganalysis. Previous work seldom conduct research on the interaction between the two factors of the CSM and the texture complexity. This paper studies the interaction between the two factors, aiming to improve the steganalysis accuracy. We propose a effective method to measure the texture complexity via image filtering, and use the two-way analysis of variance to study the interaction between the two factors. The experimental results have shown that the interaction between the two factors affects the detection accuracy significantly. We also design a method to improve the detection accuracy of steganalysis by utilizing the interaction of the two factors.
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Acknowledgment
This work was supported in part by the National Natural Science Foundation of China (No. 61272540, No. U1536204), the National Key Technology R&D Program (No. 2014BAH41B00, No. 2015AA016004), and in part by the Natural Science Foundation of Anhui province (No. 1508085MF115, No. 1608085MF142).
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Hu, D., Ma, Z., Fan, Y., Wang, L. (2017). A Study of the Two-Way Effects of Cover Source Mismatch and Texture Complexity in Steganalysis. In: Shi, Y., Kim, H., Perez-Gonzalez, F., Liu, F. (eds) Digital Forensics and Watermarking. IWDW 2016. Lecture Notes in Computer Science(), vol 10082. Springer, Cham. https://doi.org/10.1007/978-3-319-53465-7_45
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DOI: https://doi.org/10.1007/978-3-319-53465-7_45
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