Abstract
The excellent cover estimation is very important to the payload location of JPEG image steganography. But it is still hard to exactly estimate the quantized DCT coefficients in cover JPEG image. Therefore, this paper proposes a JPEG image steganography payload location method based on optimal estimation of cover co-frequency sub-image, which estimates the cover JPEG image based on the Markov model of co-frequency sub-image. The proposed method combines the coefficients of the same position in each 8 × 8 block in the JPEG image to obtain 64 co-frequency sub-images and then uses the maximum a posterior (MAP) probability algorithm to find the optimal estimations of cover co-frequency sub-images by the Markov model. Then, the residual of each DCT coefficient is obtained by computing the absolute difference between it and the estimated cover version of it, and the average residual over coefficients in the same position of multiple stego images embedded along the same path is used to estimate the stego position. The experimental results show that the proposed payload location method can significantly improve the locating accuracy of the stego positions in low frequencies.
Highlights
Digital steganography is the technique that embeds information, known as the payload, into the redundant parts of multimedia data such as digital images, video, audio, and text, termed the cover, to conceal secret communications
Activated by the optimal cover estimation method proposed by Quach in [22] for spatial image steganography, this paper proposes a payload location method for Joint photographic experts group (JPEG) image steganography based on the optimal estimation of cover co-frequency subimage
6 Conclusion This paper proposes a payload location method based on optimal estimation of cover co-frequency sub-image
Summary
Digital steganography is the technique that embeds information, known as the payload, into the redundant parts of multimedia data such as digital images, video, audio, and text, termed the cover, to conceal secret communications. A series of steganographic algorithms have been proposed with image, text, audio, or video as cover [1,2,3,4,5,6,7,8]. Many steganalysis algorithms have been proposed to detect the stego object [9,10,11,12,13,14]. Compared with the detection of the stego objects, the extraction of hidden information is much more difficult and requires more clues, such as the stego key space, the stego positions, and the selection scheme of stego positions. In [15, 16], Yang et al and Liu et al have reported that when the selection
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