Toward Steganographic Payload Location via Neighboring Weight Algorithm
Tong Qiao, Xiangyang Luo, Binmin Pan, Yuxing Chen, Xiaoshuai Wu · Security and Communication Networks · 2022
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.