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Multi-Channel HEVC Steganography by Minimizing IPM Steganographic Distortions | IEEE Journals & Magazine | IEEE Xplore

Multi-Channel HEVC Steganography by Minimizing IPM Steganographic Distortions


Abstract:

Calibration is a common method for steganalysis, and Intra Prediction Mode (IPM) shift is a typical phenomenon used in calibration to detect video steganography. The curr...Show More

Abstract:

Calibration is a common method for steganalysis, and Intra Prediction Mode (IPM) shift is a typical phenomenon used in calibration to detect video steganography. The current HEVC steganography lacks resistance to steganalysis based on this phenomenon because the new technology of HEVC introduces steganographic distortion in addition to providing more potential steganographic space. In this paper, an HEVC steganographic algorithm that resists IPM shift is proposed. First, we introduce the IPM shift in HEVC, and the previous H.264 steganalytic IPM shift feature is modeled and improved. By analyzing the HEVC encoding process, we found that modifying large-size blocks has a more significant impact on compression efficiency, while small ones are more sensitive to IPM optimality. Therefore, we perform the embedding channel division based on the block size and design the distortion function separately. In addition, we discover a unique IPM transition probability distribution in HEVC. According to our analysis, this unique distribution arises due to HEVC’s MPM rules and the regularity of IPM direction. Modifying IPM in HEVC will change such distribution, thus, a mapping rule is designed based on this distribution to achieve a better embedding effect. Experimental results show that the channel division and proposed distortion function can effectively improve the overall performance. The proposed steganography outperforms the state-of-the-art steganography in resisting steganalysis, bitrate controlling, and visual quality.
Published in: IEEE Transactions on Multimedia ( Volume: 25)
Page(s): 2698 - 2709
Date of Publication: 10 February 2022

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