Most state-of-the-art binary image steganographic techniques only consider the flipping distortion according to the human visual system (HVS), which will be not secure when they are attacked by steganalyzers. A binary image steganographic scheme that aims to minimize the embedding distortion on the texture is presented. We extract the complement, rotation, and mirroring-invariant local texture patterns (crmiLTPs) from the binary image first. The weighted sum of crmiLTP changes when flipping one pixel is then employed to measure the flipping distortion corresponding to that pixel. By testing on both simple binary images and the constructed image dataset, we show that the proposed measurement can well describe the distortions on both visual quality and statistics. Based on the proposed measurement, a practical steganographic scheme is developed. Experimental results have demonstrated that the proposed steganographic scheme can achieve statistical security without degrading the image quality or the embedding capacity.
Wei Lu received the B.S. degree in Automation from Northeast University, China in 2002, the M.S. degree and the Ph.D. degree in Computer Science from Shanghai Jiao Tong University, China in 2005 and 2007 respectively. He was a research assistant at Hong Kong Polytechnic University from 2006 to 2007. He is the deputy director of the Institute of Cyber Security, the School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China. His research interests include multimedia forensics and security, information hiding, computer vision and machine learning. Prof. Lu served as the Associate Editor in IJDCF, and PC members in IWDCF, ICCCS, SICBS etc. Prof. Lu has published more than 60 SCI journals and 30 international conference papers. According to Google citation, Prof. Lu’s academic results have been cited more than 1500 times.
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