Toward Steganographic Payload Location via Neighboring Weight Algorithm. (25th February 2022)

• Record Type: Journal Article
• Authors: Qiao, Tong; Luo, Xiangyang; Pan, Binmin; Chen, Yuxing; Wu, Xiaoshuai
• Other Names: Chen Beijing Academic Editor.
• Is Part Of: Security and communication networks. Volume 2022(2022)
• Publication Date: 2022-02-25
• Subjects: Computer networks -- Security measures -- Periodicals; Computer security -- Periodicals; Cryptography -- Periodicals; 005.805
• DOI: 10.1155/2022/1400708
• Languages: English
• ISSNs: 1939-0114

Abstract

Abstract : Modern steganalysis has been widely investigated, most of which mainly focus on dealing with the problem of detecting whether an inquiry image contains hidden information. However, few articles in the literature study the location of secret bits hidden by modern adaptive steganography. In this paper, we propose a novel algorithm for locating steganographic payload in the spatial domain. We first predict the steganographic scheme and its payload, which is used for generating a random bitstream. Then, the random bits are embedded in the stego image based on the cost matrix in the framework of Syndrome-Trellis Codes (STCs). Next, relying on the differences between two stego images, the extended modification map in couple with the neighboring weight algorithm can be acquired, leading to the location of the hidden bits. Compared with the prior art, the extensive experiments verify that our proposed locating algorithm performs better, in terms of locating accuracy and efficiency.