Reversible Information Hiding Techniques for Medical Images

• Main Authors: Min-Hao Wu, 吳敏豪
• Other Authors: Fu-Hau Hsu
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/b24efd

博士 === 國立中央大學 === 資訊工程學系 === 104 === The real reversible information concealment technique embeds secret information from the image carrier. After the information is embedded in the image carrier, the image is transferred from the Internet. Secret information is extracted with the same algorithm that is used to recover the stego image, which hides the information image. This concealment technique can completely extract the image and return it to its original appearance. Image quality is very important for sensitive imaging applications, such as medical, military, and satellite imaging. These sensitive images must not appear even slightly distorted; the recovered images can be misinterpreted if any distortion is present. In this dissertation, we propose a reversible information concealment technique for sensitive images. Using the proposed method, real reversible information concealment techniques can avoid image distortion while being undetected by application software. In this dissertation, we first discuss four types of information concealment based on a histogram mechanism. In our proposed scheme, both natural and non-natural images can be processed. In the pre-processing phase, images are processed by analysis and overflow (or underflow). In the analysis of these images, natural and non-natural images can be distinguished. The overflow/underflow process can prevent overflowing/underflowing of pixels after the secret information is embedded. Furthermore, various prediction strategies can be used to increase the image capacity and quality. For example, a side-match scheme employs the surrounding pixels or a 3 × 3 block to predict the values of the pixels that are divided into two, four, eight, or more classes in the natural images. From that point, a weight strategy uses the predicted pixel values of each of the proximate sides and doubles or triples these pixels for the respective sides. A threshold strategy is then used to prevent image pixel underflow/overflow. A similar dynamic programming approach is then used after each prediction to select the highest image quality for the next predicted image. Then, a difference image is generated to find the peak and zero points using the histogram method. The selected pixels are situated between the peak and zero points by shifting and embedding. Accordingly, a stego image is obtained. The peak signal-to-noise ratio (PSNR) can be maintained at approximately 48 dB, while human perception is not affected under image visual quality of approximately 30 dB. Based on our experimental results, the capacity of the proposed method is higher than those of existing methods.