Efficient Reversible Data Hiding Algorithms Based on Dual Prediction
📝 Abstract
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of prediction error histograms is proposed. The algorithm extends the efficient modification of prediction errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data embedding. The motivation behind using two predictors is driven by the fact that predictors have different prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The key feature of the proposed algorithm lies in using two predictors without the need to communicate additional overhead with the stego image. Basically, the identification of the predictor that is used during embedding is done through a set of rules. The proposed algorithm is further extended to use two and three bins in the prediction errors histogram in order to increase the embedding capacity. Performance evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in boosting the embedding capacity while providing competitive quality for the stego image.
💡 Analysis
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of prediction error histograms is proposed. The algorithm extends the efficient modification of prediction errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data embedding. The motivation behind using two predictors is driven by the fact that predictors have different prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The key feature of the proposed algorithm lies in using two predictors without the need to communicate additional overhead with the stego image. Basically, the identification of the predictor that is used during embedding is done through a set of rules. The proposed algorithm is further extended to use two and three bins in the prediction errors histogram in order to increase the embedding capacity. Performance evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in boosting the embedding capacity while providing competitive quality for the stego image.
📄 Content
Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016 DOI : 10.5121/sipij.2016.7201 1
EFFICIENT REVERSIBLE DATA HIDING ALGORITHMS BASED ON DUAL PREDICTION
Enas N. Jaara and Iyad F. Jafar
Computer Engineering Department, The University of Jordan, Amman, Jordan
ABSTRACT
In this paper, a new reversible data hiding (RDH) algorithm that is based on the concept of shifting of prediction error histograms is proposed. The algorithm extends the efficient modification of prediction errors (MPE) algorithm by incorporating two predictors and using one prediction error value for data embedding. The motivation behind using two predictors is driven by the fact that predictors have different prediction accuracy which is directly related to the embedding capacity and quality of the stego image. The key feature of the proposed algorithm lies in using two predictors without the need to communicate additional overhead with the stego image. Basically, the identification of the predictor that is used during embedding is done through a set of rules. The proposed algorithm is further extended to use two and three bins in the prediction errors histogram in order to increase the embedding capacity. Performance evaluation of the proposed algorithm and its extensions showed the advantage of using two predictors in boosting the embedding capacity while providing competitive quality for the stego image.
KEYWORDS
Prediction, Prediction Error, Histogram Shifting, Reversible Data Hiding, Watermarking.
- INTRODUCTION
Data hiding is an important technology in the areas of information security and multimedia copyright protections as it allows the concealment of data within the digital media for copyright protection and data protection. Many schemes of data hiding have been proposed to address the issues and challenges related to hiding the data, such as embedding capacity, imperceptibility and reversibility.
In this technique, the data is supposed to be seamlessly hidden or embedded into a carrier or cover signal (audio, images, video) in way that makes it hard for unauthorized people to access it [1]. In the digital imaging domain, several data hiding techniques have been proposed [2-4]. Despite the efficiency of these techniques in protecting the data, most of them are not capable of restoring the original cover image upon the extraction of embedded data. This poses a challenge to applications that require the preservation of the cover image after the hidden data is extracted. Accordingly, a great interest has grown in the past few years in the development of reversible data hiding (RDH) techniques that are capable of restoring the original image.
Several RDH techniques have been proposed in the literature and they compete in different aspects which include the embedding capacity, the quality of the stego image, size of overhead information and computational complexity [2]. Generally, they can be grouped into three different classes based on the concept of operation: difference expansion, histogram shifting, and Signal & Image Processing : An International Journal (SIPIJ) Vol.7, No.2, April 2016 2 prediction-based techniques. Difference expansion (DE) algorithms are one popular class of reversible data hiding that are characterized with low distortion and relatively high embedding
c b a x
Figure 1 Context for predicting pixel x
capacity. The first difference expansion technique was proposed by Tian in [5]. In this technique, the cover image is partitioned into a series of non-overlapping pixel pairs. A secret bit is then embedded using the difference expansion of each pixel pair. Several DE-based algorithms were developed based on Tian’s technique [6-9]. Alattar [6] used DE with vectors instead of pixel pairs to extend and improve the performance of Tian’s algorithm. Hu, et al. proposed a DE-based technique that improved the compressibility of the location map [8]. Compared to traditional DE- based algorithm, their technique increased the embedding capacity and performed well with different images.
Another important category of RDH algorithms are those that are based on the idea of histogram shifting (HS) [10-13]. Actually, the basis of these algorithms is the work presented by Ni, et al. [13]. In this algorithm, the histogram of the intensities in the original image is computed. Then, the histogram bins that lie between the peak bin and a zero (or minimum) bin is shifted by one toward the zero bin to open space to embedded data. Afterwards, the secret data bits are embedded by modifying the intensity value that corresponds to the peak only. This technique provided reasonable embedding capacity with minimum peak-signal-to-noise ratio (PSNR) of 48.1 dB. However, the main drawback of thi
This content is AI-processed based on ArXiv data.