This paper presents a reversible or lossless watermarking algorithm for images without using a location map in most cases. This algorithm employs prediction errors to embed data into an image. A sorting technique is used to record the prediction errors based on magnitude of its local variance. Using sorted prediction errors and, if needed, though rarely, a reduced size location map allows us to embed more data into the image with less distortion. The performance of the proposed reversible watermarking scheme is evaluated using different images and compared with four methods: those of Kamstra and Heijmans, Thodi and Rodriguez, and Lee et al. The results clearly indicate that the proposed scheme can embed more data with less distortion.

Reversible Watermarking Algorithm Using Sorting and Prediction

This paper presents a novel reversible data hiding algorithm, which can recover the original image without distortion from the marked image after the hidden data have been extracted. This algorithm utilizes the zero or the minimum point of the histogram and slightly modifies the pixel values to embed data. It can embed more data as compared to most of the existing reversible data hiding algorithms. A theoretical proof and numerous experiments show that the PSNR of the marked image generated by this method is always above 48 dB, which is much higher than other reversible data hiding algorithms. The algorithm has been applied to a wide range of different images successfully. Some experimental results are presented to demonstrate the validity of the algorithm.

Reversible data hiding

The proliferation of digital information in our society has enticed a lot of research into data-embedding techniques that add information to digital content, like images, audio, and video. In this paper, we investigate high-capacity lossless data-embedding methods that allow one to embed large amounts of data into digital images (or video) in such a way that the original image can be reconstructed from the watermarked image. We present two new techniques: one based on least significant bit prediction and Sweldens' lifting scheme and another that is an improvement of Tian's technique of difference expansion. The new techniques are then compared with various existing embedding methods by looking at capacity-distortion behavior and capacity control.

Reversible data embedding into images using wavelet techniques and sorting

This paper proposes a high capacity reversible image watermarking scheme based on integer-to-integer wavelet transforms. The proposed scheme divides an input image into nonoverlapping blocks and embeds a watermark into the high-frequency wavelet coefficients of each block. The conditions to avoid both underflow and overflow in the spatial domain are derived for an arbitrary wavelet and block size. The payload to be embedded includes not only messages but also side information used to reconstruct the exact original image. To minimize the mean-squared distortion between the original and the watermarked images given a payload, the watermark is adaptively embedded into the image. The experimental results show that the proposed scheme achieves higher embedding capacity while maintaining distortion at a lower level than the existing reversible watermarking schemes.

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Reversible Image Watermarking Based on Integer-to-Integer Wavelet Transform

Reversible data embedding theory has marked a new epoch for data hiding and information security. Being reversible, the original data and the embedded data should be completely restored. Difference expansion transform is a remarkable breakthrough in reversible data-hiding schemes. The difference expansion method achieves high embedding capacity and keeps distortion low. This paper shows that the difference expansion method with the simplified location map and new expandability can achieve more embedding capacity while keeping the distortion at the same level as the original expansion method. Performance of the proposed scheme in this paper is shown to be better than the original difference expansion scheme by Tian and its improved version by Kamstra and Heijmans. This improvement can be possible by exploiting the quasi-Laplace distribution of the difference values.

A Novel Difference Expansion Transform for Reversible Data Embedding

A new reversible watermarking algorithm based on the difference expansion of colored images has been developed. Since the watermark is completely reversible, the original image can be recovered exactly. The algorithm uses spatial and spectral triplets of pixels to hide pairs of bits, which allows the algorithm to hide a large amount of data. A spatial triplet is any three pixel values selected from the same spectral component, while a spectral triplet is any three pixel values selected from different spectral components. The algorithm is recursively applied to the rows and columns of the spectral components of the image and across all spectral components to maximize the hiding capacity. Simulation results show that the hiding capacity of the algorithm is very high and the resulting distortion is low.

https://www.digimarc.com/docs/default-source/technology-resources/published-technical-papers/reversible-techniques/dmrc_diff_expansion_triplets.pdf

A.M. Alattar

Papers

Reversible watermark using difference expansion of triplets