Dual-image Based Reversible Information Hiding Scheme with Center Folding Strategy

碩士 === 朝陽科技大學 === 資訊管理系 === 103 === In recent years, dual-image techniques have been widely used in reversible data hiding. Most data hiding methods focus on finding the best predictive value to reduce the degree of stego-image distortion. After finding the predictive value, most methods convert...

Full description

Bibliographic Details
Main Authors: Jhih-Huei Wu, 吳致暉
Other Authors: Tzu-Chuen Lu
Format: Others
Language:zh-TW
Published: 2015
Online Access:http://ndltd.ncl.edu.tw/handle/47348505271678450278
Description
Summary:碩士 === 朝陽科技大學 === 資訊管理系 === 103 === In recent years, dual-image techniques have been widely used in reversible data hiding. Most data hiding methods focus on finding the best predictive value to reduce the degree of stego-image distortion. After finding the predictive value, most methods convert k secret bits to a decimal secret symbol, and add (or substract) the secret symbol to the predictive value (or pixel) for completing embedding procedure. However, this study found that the secret data also form a very important factor affecting image quality. If the value of the secret symbols is too large, they may cause larger differences between the stego-pixels and original pixels. Hence, this study employs the center folding strategy to reduce the value of the secret symbols. The reduced symbols are then embedded in two stego-images through an averaging method to maintain image quality. In addition, underflow/overflow pixels are replaced with the original pixels to reduce unnecessary image distortions. The experimental results show that the proposed method has fairly good performance compared to the other dual-image techniques. For the same embedding capacity, its image quality is also better than that of other methods by at least 2 dB on average. Furthermore, the proposed method determines embedding capacity with the k value. The larger the k value, the higher is the embedding capacity. The experimental results show that for the same image quality, the embedding capacity of the proposed method is better than that of other methods by at least 0.5 bits per pixel on average. The proposed method is applicable not only to experimental images, but also works well for general images in terms of embedding capacity, image quality, and execution time.