Lossless generalized-LSB data embedding

被引:869
作者
Celik, MU
Sharma, G
Tekalp, AM
Saber, E
机构
[1] Univ Rochester, Dept Elect & Comp Engn, Rochester, NY 14627 USA
[2] Univ Rochester, Dept Biostat & Computat Biol, Rochester, NY 14627 USA
[3] Koc Univ, Coll Engn, Istanbul, Turkey
[4] Xerox Corp, Webster, NY 14580 USA
关键词
arithmetic coding; conditional entropy coding; context modeling; data embedding; data hiding; least significant bit (LSB) modification; watermark;
D O I
10.1109/TIP.2004.840686
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We present a novel lossless (reversible) data-embedding technique, which enables the exact recovery of the original host signal upon extraction of the embedded information. A generalization of the well-known least significant bit (LSB) modification is proposed as the data-embedding method, which introduces additional operating points on the capacity-distortion curve. Lossless recovery of the original is achieved by compressing portions of the signal that are susceptible to embedding distortion and transmitting these compressed descriptions as a part of the embedded payload. A prediction-based conditional entropy coder which utilizes unaltered portions of the host signal as side-information improves the compression efficiency and, thus, the lossless data-embedding capacity.
引用
收藏
页码:253 / 266
页数:14
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