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Reversible Data Hiding with Pixel Prediction and Additive Homomorphism for Encrypted Image 时间:2018-09-08 21:48 来源:未知作者:admin 点击: 次 Abstract:Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.
1. Introduction
Data hiding is an important technology for embedding secret data into a meaningful cover medium (such as an image or a video) to generate a stego-medium with a small distortion [1, 2]. Reversible data hiding (RDH) is a branch of data hiding, which can restore the original image from the stego-image after extraction of the embedded data. This restoration property of RDH plays an important role in those data-sensitive applications, such as medical imagery, military imagery, and law forensics, in which the cover image must be accurately restored.
In the past years, various RDH algorithms have been proposed. Generally, these RDH algorithms can be classified into three categories: lossless compression based algorithms, difference expansion (DE) based algorithms, and histogram shifting (HS) based algorithms. Lossless compression based algorithms vacate space for embedding secret message by losslessly compressing the least significant bit (LSB) planes or quantization residuals [3, 4]. They can be applied to image authentication and watermarking, but their embedding capacities are limited. DE based algorithms usually shift the difference of neighboring pixels for creating a vacant least significant bit (LSB) and append one secret bit to the vacated LSB [5, 6]. The HS based algorithms firstly shift the bins of histogram of gray values [7] or the predicted errors [8–12] for generating vacated space and then embed secret data into the vacated space. This kind of algorithms can provide a good trade-off between embedding capacity and visual quality.
Image encryption is a useful technique for protecting image content [13, 14]. It can convert an original image, known as plaintext image, into a meaningless image called the encrypted image. Since it cannot observe any useful information from the encrypted image, image content security is achieved. In some application scenarios, such as cloud storage, there are many encrypted images and people would like to embed secret message into encrypted images for privacy protection. These applications require efficient RDH algorithms for encrypted images.