In this paper, a new reversible data hiding scheme for binary images is proposed. Hidden data is embedded into the binary image by modifying pixels based on a reference block created around them. The host pixels are processed as horizontally connected pairs. The nearest neighboring pixels to each host pair are considered reference pixels, forming a reference block. The host pairs are classified based on the reference block pixel distribution. This distribution is also used to evaluate the embedding distortion caused by replacing the host pair with two hidden data bits. The proposed approach selects the distribution that provides the required capacity at the lowest embedding distortion.

Pixel value ordering (PVO) was shown to be an efficient technique for high-fidelity reversible data hiding in images. This paper proposes an audio reversible data hiding scheme inspired by PVO, namely sample value ordering (SVO). Large uniform areas are uncommon in natural images, but sections of silence are relatively common in audio samples. In order to properly exploit the nature of the host audio file, a block-based approach is employed. Non-overlapping blocks are classified into three categories: smooth, somewhat complex and complex. All samples from a smooth block are considered as possible hosts using a classic histogram shifting based approach. For somewhat complex blocks, only the maximum and minimum sample values are used for data hiding with the PVO inspired SVO. Complex blocks remain unchanged.

Pixel-value-ordering (PVO) appears as an efficient solution for high-fidelity reversible data hiding. State-of-the-art PVO schemes split the host image into blocks, sort pixels within the blocks based on their graylevel and, finally, embed data into some differences between the sorted values. This paper investigates the use of the prediction error instead of the pixel graylevel both for ordering and embedding. The proposed prediction-error-ordering (PEO) scheme also introduces a two-stage procedure by splitting each block in two sets following a chessboard pattern and by processing set by set each block. The pixels of one set are used to classify and predict the ones of the other set and vice-versa. The proposed PEO approach outperforms the state-of-the-art PVO schemes.

This paper investigates the use of gradient based prediction for pairwise reversible data hiding. Pairwise based approaches split the host image into two pixel sets. The pixels in each set are processed based on the information from the other set. Host pixels from the same set are grouped into pairs based on their prediction error. Hidden data is then embedded into each pair. The proposed approach introduces a five set distribution combined with a twelve pixel rhombus shaped prediction context, improving the prediction precision and maintaining the independent prediction required for pixel pairing. This approach shows promising results on images with large textured regions.

Pairwise reversible data hiding (RDH) restricts the embedding to 3 combinations of bits per pixel pair (”00”, ”01”, ”10”), by eliminating the embedding of ”1” into both pixels. The gain in quality is significant and the loss in embedding bitrate is compensated by embedding into previously shifted pairs. This restriction requires a special coding procedure to format the encrypted hidden data. This paper proposes a new set of embedding equations for pairwise RDH. The proposed approach inserts either one or two data bits into each pair based on its type, bypassing the need for special coding. The proposed equations can be easily integrated in most pairwise reversible data hiding frameworks. They also provide more room for data embedding than their classic counterparts at the low embedding distortion required for high-fidelity RDH.

Pixel-value-ordering (PVO) appears as an efficient technique for high-fidelity reversible data hiding. This paper proposes a reversible data hiding scheme based on the pairwise PVO framework with improved difference equations. Both the pixel pair selection and the embedding algorithms are also streamlined. The proposed scheme uses a block classification approach based on a local complexity metric. Uniform blocks are processed using the proposed improved pairwise PVO algorithm. Slightly noisy blocks are embedded using a classic PVO scheme and noisy blocks are kept unchanged. The optimal embedding parameters for a given capacity are determined by linear programming. The proposed pairwise PVO approach outperforms other state-of-the-art schemes.

This paper proposes a new vacating room after encryption reversible data hiding scheme developed for color images. The proposed scheme uses standard exclusive-or encryption and inherits the main features of vacating room after encryption schemes, namely joint and separate methods for data embedding. The proposed scheme exploits both the correlation between neighboring pixels and the correlation between color channels by predicting the original pixel values on color channel differences. The experimental results show that the proposed scheme can eliminate the main drawback of the vacating room after encryption framework, namely the large embedding distortions.

This paper presents an efficient capacity control algorithm for prediction error expansion based audio reversible data hiding. Current state-of-the-art audio reversible data hiding schemes use a simple capacity control algorithm that was first developed for image reversible data hiding. The performance of this algorithm can be improved by using a simple two threshold based approach. The two threshold approach can be easily integrated into any prediction error expansion based framework. Experimental results are provided for two such frameworks.