An Efficient Prediction-and-Shifting Embedding Technique for High Quality Reversible Data Hiding

EURASIP Journal on Advances in Signal Processing, Dec 2010

The embedding capacity of a histogram-based reversible data hiding technique is primarily determined by the peak height of the histogram. Recently, some studies have tried to embed data in the histogram of prediction errors by modifying the error values and have better embedding efficiency. However, these methods offer no selective embedment mechanism to exclude the positions where the modification in the embedding operation contributes no capacity but merely degrade the image quality. In this paper, a novel coding method for reversible data hiding is presented. A two-stage prediction algorithm that fully exploits the pixel correlations is employed to create more embeddable spaces, and a selective embedment mechanism is used to enhance the image quality. According to the experimental results, the proposed method achieved the highest payload while maintaining the lowest distortion for most standard test images, comparing to other existing histogram-shifting-based reversible data hiding techniques.

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An Efficient Prediction-and-Shifting Embedding Technique for High Quality Reversible Data Hiding

EURASIP Journal on Advances in Signal Processing Hindawi Publishing Corporation An Efficient Prediction-and-Shifting Embedding Technique for High Quality Reversible Data Hiding Wien Hong 0 Jin-Hua She 0 Department of Information Management, Yu Da University , Miaoli, 361 , Taiwan The embedding capacity of a histogram-based reversible data hiding technique is primarily determined by the peak height of the histogram. Recently, some studies have tried to embed data in the histogram of prediction errors by modifying the error values and have better embedding efficiency. However, these methods offer no selective embedment mechanism to exclude the positions where the modification in the embedding operation contributes no capacity but merely degrade the image quality. In this paper, a novel coding method for reversible data hiding is presented. A two-stage prediction algorithm that fully exploits the pixel correlations is employed to create more embeddable spaces, and a selective embedment mechanism is used to enhance the image quality. According to the experimental results, the proposed method achieved the highest payload while maintaining the lowest distortion for most standard test images, comparing to other existing histogram-shifting-based reversible data hiding techniques. 1. Introduction Data hiding is a technique that embeds data into cover media by slightly modifying their content [ 1 ] and has been used in many applications, such as tamper detection [ 2 ], copyright protection [ 3 ], and finger printing [ 4 ]. When data are embedded into cover media, the content of the media will be inevitably modified and thus distortion introduced. The distortion caused by data embedding is termed embedding distortion [ 5 ]. Although the embedding distortion in many applications is small, the distorted cover media cannot be recovered to their original state [ 6, 7 ]. However, some applications, such as in medical or military usages, allow no permanent embedding distortion in order to preserve content fidelity. This demand has highlighted the needs of reversible data hiding and has drawn much attention in the recent years [ 8–10 ]. The reversible data hiding is a technique that allows extraction of embedded data from the stego media and exactly restores the marked media to their original states [ 11 ]. Many researchers use digital image as the cover media because they are often transmitted throughout the Internet, which is easy to be accessed and may arouse a little suspicious. An image that is used to embed data is called a cover image, and an image with data embedded is called a stego image [ 12 ]. The earliest reversible data hiding technique reported in the literature is Barton’s work [ 13 ]. Afterwards, a number of reversible data hiding techniques have been proposed to fulfill the insatiate demands in this field. In 2003, Tian [ 14 ] proposed a novel reversible data hiding method with high payload. In his method, the difference value between paired pixels is expanded, and a bit can be embedded into the LSB of the expanded difference. In Tian’s method, n bits can be embedded into 2n pixels. Alattar [ 15 ] extended Tian’s work by increasing the payload without introducing a noticeable distortion. In Alattar’s method, n bits can be embedded into n + 1 pixels. Tian and Alattar et al.’s embedding techniques can be classified as the expansionembedding technique. For an expansion-embedding technique, difference values between pixels have to be expanded to conceal data. Therefore, the embedding distortion is relatively large. Besides, the selection of embedding position to avoid the overflow or underflow problem has to pay the overhead cost, which may significantly reduce the payload. In 2006, Ni et al. [ 16 ] proposed a reversible data hiding method and achieved very high image quality. They selected pairs of peak and zero of an image histogram and shift the histogram bins to leave embeddable spaces for data embedding. Ni et al.’s embedding method can be classified as the shifting-embedding technique. In their work, the maximum payload is limited by the peak height of the cover image histogram; therefore, the payload is smaller compared to the expansion-embedding-based reversible data hiding techniques. In 2007, Thodi and Rodr´ıguez combined the expansion-embedding and shifting-embedding techniques and proposed a reversible data hiding method with higher payload and lower distortion [ 5 ]. In their works, the prediction errors are expanded, and data are embedded into the LSBs of the expanded prediction errors. A better performance was achieved in Thodi et al.’s method than that of Tian’s and Ni et al.’s methods. Recently, some researchers [ 17–20 ] adopted the concept of shifting-embedding technique and embedded data into the prediction error histogram. Since the peak height of the prediction error histogram is usually higher than that of the image histogram for most natural images, (...truncated)


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Wien Hong. An Efficient Prediction-and-Shifting Embedding Technique for High Quality Reversible Data Hiding, EURASIP Journal on Advances in Signal Processing, 2010, pp. 104835, Volume 2010, Issue 1, DOI: 10.1155/2010/104835