Modification Of Prediction Errors Research Articles

Efficient Reversible Data Hiding Algorithms Based on Dual Prediction

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 .

An efficient multi-predictor reversible data hiding algorithm based on performance evaluation of different prediction schemes

With the broad development and evolution of digital data exchange, security has become an important issue in data storage and transmission since digital data can be easily manipulated and modified. Reversible data hiding algorithms are special class of steganography that are capable of recovering the original cover image upon the extraction of the secret data. This issue is of interest in medical and military imaging applications. Many algorithms in this class exploit the idea of prediction in order to increase the embedding capacity as well as the quality of the stego image. However, the performance of these algorithms depends on the type of predictor that is being used. The main goal in this paper is to survey different predictors and evaluate their performance when employed in two classical reversible data hiding algorithms. The evaluation considered plugging 22 predictors in the two algorithms to process 1438 test images. Experimental results validated the varying capabilities of different predictors and showed that the non-causal median predictor had the best performance in the two algorithms. Further more, the paper proposes a new multi-predictor reversible data hiding algorithm. Basically, the algorithm employs multiple predictors in an extended version of the modification of prediction errors (MPE) algorithm. The algorithm takes advantage of the results obtained from the performance evaluation of different predictors to select the best set of predictors. Performance evaluation proved the ability of the proposed algorithm in increasing the embedding capacity while maintaining high stego image quality.

A high payload hybrid data hiding scheme with LSB, EMD and MPE

Data hiding in a cover image can be used to assist in secure message communication on the Internet. In this paper, we proposed a hybrid data hiding scheme that is a combination of least significant bit substitution (LSB), exploiting modification direction (EMD) and modification of prediction errors (MPE). The aim is to maintain a balance between embedding quality and embedding capacity where high payload is the motive. The image is first embedded by EMD, or LSB followed by EMD only if the peak signal-to-noise ratio (PSNR) is greater or equal T1 dB (45 dB). The remainder payload will be embedded by recursive MPE until the PSNR of the stego-image reaches T2 dB (35 dB). Experimental results showed that the proposed scheme has a higher embedding capacity while maintaining high visual quality of the stego-image than any of the individual method.

High capacity reversible watermarking for audio by histogram shifting and predicted error expansion.

Being reversible, the watermarking information embedded in audio signals can be extracted while the original audio data can achieve lossless recovery. Currently, the few reversible audio watermarking algorithms are confronted with following problems: relatively low SNR (signal-to-noise) of embedded audio; a large amount of auxiliary embedded location information; and the absence of accurate capacity control capability. In this paper, we present a novel reversible audio watermarking scheme based on improved prediction error expansion and histogram shifting. First, we use differential evolution algorithm to optimize prediction coefficients and then apply prediction error expansion to output stego data. Second, in order to reduce location map bits length, we introduced histogram shifting scheme. Meanwhile, the prediction error modification threshold according to a given embedding capacity can be computed by our proposed scheme. Experiments show that this algorithm improves the SNR of embedded audio signals and embedding capacity, drastically reduces location map bits length, and enhances capacity control capability.

Adaptive reversible data hiding based on block median preservation and modification of prediction errors

In this paper, two enhanced reversible data hiding methods are proposed; both of them are based on two novel reversible data hiding techniques. A latest predictor is adopted to achieve better data hiding capability for the first predicative reversible data hiding scheme, whereas another scheme utilizes a new approach by considering the nature of different images to classify the smoothness for each piece of image blocking regions such that more secret data can be hidden into the smooth regions rather than the non-smooth ones resulting in a better embedding capability. The experiments verify that these schemes outperform the original reversible data hiding algorithms and some state-of-the-art reversible data hiding schemes.

A high capacity reversible data hiding scheme using orthogonal projection and prediction error modification

The payload of a histogram-shifting based reversible data hiding technique is primarily determined by the peak height of the corresponding image histogram. It is known that the peak height of the prediction error histogram is usually higher than that of image histogram itself. In 2009, Tsai et al. adopted the idea of histogram-shifting technique and embedded message by modifying the prediction errors to achieve a higher payload. They used a block-based approach and leaved some pixel values unmodified to predict other pixel values in a block. However, the unmodified pixels took up some embeddable space, causing a reduction in payload. In this paper, a new reversible data hiding technique based on prediction error modification is proposed. The orthogonal projection technique is employed to estimate the optimal weights of a linear predictor to increase the prediction accuracy and the embedding capacity. The experimental results revealed that the proposed method outperformed the existing histogram-shifting based reversible data hiding technique in terms of payload and image quality.

Reversible data hiding for high quality images using modification of prediction errors

In this paper, a reversible data hiding scheme based on modification of prediction errors (MPE) is proposed. For the existing histogram-shifting based reversible data hiding techniques, though the distortion caused by embedding is low, the embedding capacity is limited by the frequency of the most frequent pixel. To remedy this problem, the proposed method modifies the histogram of prediction errors to prepare vacant positions for data embedding. The PSNR of the stego image produced by MPE is guaranteed to be above 48 dB, while the embedding capacity is, on average, almost five times higher than that of the well-known Ni et al. techniques with the same PSNR. Besides, MPE not only has the capability to control the capacity-PSNR, where fewer data bits need less error modification, but also can be applied to images with flat histogram. Experimental results indicate that MPE, which innovatively exploits the modification of prediction errors, outperforms the prior works not only in terms of larger payload, but also in terms of stego image quality.