Abstract
With the development of the Internet, social network platforms (SNPs) have become the most common channel for image sharing. As a result, transmitting stego images in the public channels gives steganographers the best opportunity to transmit secret messages with behavioral security preserved. However, the SNPs typically compress uploaded images and damage the weak signal of steganography. In this study, a robust JPEG steganographic scheme based on robustness measurement and cover block selection (CBSRS) is proposed. We first design a deep learning-based model to fit the blockwise change rate of coefficients after JPEG recompression. Then, a cover block selection strategy is proposed to improve the robustness by optimizing the joint distortion function of transmission costs and classic costs. Moreover, by embedding indicator of cover block selection in chrominance channels of JPEG images, a shareable cover construction scheme is designed to solve the problem of auxiliary information transmission. The experimental results show that our proposed framework improves robustness while maintaining statistical security. Comparing with state-of-the-art methods, the framework achieves better performance under given recompression channels.
Highlights
Steganography is an advanced art of covert communication encoding secret messages into natural-looking covers while transmitting data without arousing attention of the others [1]
social network platforms (SNPs) generally recompress uploaded multimedia files to review their content and optimize storage spaces into a uniform format. e JPEG recompression operation, especially with the fixed quantization table, is the most common procedure of the lossy channel of JPEG image transmission. e studying of anticompression steganographic schemes is the key to solving this problem
We propose a robust steganographic framework with the strategy of cover block selection, which are capable of resisting JPEG recompression while maintaining high steganographic security
Summary
Steganography is an advanced art of covert communication encoding secret messages into natural-looking covers while transmitting data without arousing attention of the others [1]. Steganographic frameworks in the “General Robustness” category are designed to improve comprehensive robustness by resisting general lossy properties of JPEG recompression and to preserve the integrity of secret message without considering the specific properties of JPEG recompression channel. As the quantization steps adopted in the recompression procedure are fixed in most SNP channels, the quantization tables of the cover JPEG image can be adjusted to the same as those of the recompression channel before steganographic embedding to reduce the error rate of stego image transmission, which shows that the problem of anticompression robust steganography under the same quantization table is more worthy of study.
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