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This paper proposes a novel steganalyzer for detecting one of the most popular steganography, LSB matching (also known as “±1 embedding”). The histogram of difference image (the differences of adjacent pixels), which is usually a generalized Gaussian distribution centered at 0, is exploited for deriving statistical features. We have proved theoretically that the peak-value of the histogram would decrease...
In this paper, a content-adaptive steganographic scheme is proposed. The novel scheme can be viewed as an improvement of the conventional LSB matching. In this scheme, we take advantage of embedding redundancy in LSB matching to select modification direction (i.e., increasing or decreasing the pixel value by 1), and the dependency of neighboring pixels is taken into consideration. More specifically,...
Embedding efficiency, which is defined as the average number of secret data bits embedded per one embedding change, is an important attribute directly influencing the security of steganography. Higher embedding efficiency is usually expected to get more secure steganographic method. In this paper, by incorporating two previously introduced techniques: the ldquosum and difference covering setrdquo...
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