Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation

Jie Zhu, Xianfeng Zhao, Qingxiao Guan

Source Title: International Journal of Digital Crime and Forensics (IJDCF)11(1)

ISSN: 1941-6210|EISSN: 1941-6229|EISBN13: 9781522565147|DOI: 10.4018/IJDCF.2019010105

MLA

Zhu, Jie, et al. "Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation." IJDCF vol.11, no.1 2019: pp.62-77. http://doi.org/10.4018/IJDCF.2019010105

APA

Zhu, J., Zhao, X., & Guan, Q. (2019). Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation. International Journal of Digital Crime and Forensics (IJDCF), 11(1), 62-77. http://doi.org/10.4018/IJDCF.2019010105

Chicago

Zhu, Jie, Xianfeng Zhao, and Qingxiao Guan. "Detecting and Distinguishing Adaptive and Non-Adaptive Steganography by Image Segmentation," International Journal of Digital Crime and Forensics (IJDCF) 11, no.1: 62-77. http://doi.org/10.4018/IJDCF.2019010105

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Abstract

This article describes how blind steganalysis aiming at uncovering the existence of hidden data in digital images remains an open problem. Conventional spatial image steganographic algorithms hide data into pixels spreading evenly in the entire cover image, while the content-adaptive algorithms prefer the textural areas and edge regions. In this article, the impact of image content on blind steganalysis is discussed and a practical and extensible approach to distinguish the different types of steganography and construct blind steganalytic detector is proposed. Through the technique of image segmentation, the images are segmented into sub-images with different levels of texture. The classifier only cares for the sub-images which can help modeling the statistical detectability and is trained on sub-images instead of the entire image. Experimental results show the authors' scheme can recognize the type of steganographic methods reliably. The further steps to improve capacity of blind steganalysis based on image segmentation are also mentioned and achieve better performance than ordinary blind steganalysis.