Reversible data hiding in encrypted images can simultaneously enhance the image privacy and preserve the message security for the purpose of covert communication and cloud data management. The algorithm extends the multi-MSB prediction and Huffman coding to propose the first reversible data hiding in encrypted HDR images in the Radiance RGBE format. Because of the high similarity among the exponent channel values of neighboring pixels, we directly applied multi-MSB prediction in our proposed preprocessing procedure, yielding considerably increased embedding capacity. We also proposed a novel image encryption method to maintain the characteristics of the images. Subsequently, a distortion-free data hiding algorithm, namely the homogeneity index modification algorithm, was added to further increase embedding capacity. The experimental results demonstrate the feasibility of the proposed algorithm and its ability to increase embedding capacity, embedding rate, and image privacy, support two data hiders, and enable reversibility and separability.

The proposedmethod in this paper is based on the LSB matching function ofMielikainen for Steganography. However, there is a significant change with respect to Mielikainen's method in the use of the LSB matching function to compute secret bits. Melikainen extracts secret bits in pixel pairs: the first one is the LSB of the first pixel in a pixel pair and the other is computed by a LSB matching function applied to both pixels. The proposed method does not make any partition, but makes the sequential image processing: the current secret bit is always computed by a LSB matching function where the LSB matching function is modified using only an XOR operation. To make the data extraction compatible with data embedding, the proposed method modifies sparse pixels by adding/subtracting their value to/from one. In the experimental results, the values of embedding efficiency increase while the involved bits increase. Taking test images in the experiments, the numbers of modified pixels in the proposed method are always lower than those in Mielikainen's method.

The study proposed a vertex-based authentication algorithm based on spatial subdivision. A binary space partitioning tree was employed to subdivide the bounding volume of the input model into voxels. Each vertex could then be encoded into a series of binary digits, denoted as its authentication code, by traversing the constructed tree. Finally, the above authentication code was embedded into the corresponding reference vertex by modulating its position within the located subspace. Extensive experimental results demonstrated that the proposed algorithm provided high embedding capacity and high robustness. Furthermore, the proposed algorithm supported controllable distortion and self-recovery.

Data hiding can be regarded as a type of image processing techniques. Other image processing operations are usually integrated to increase the embedding capacity or decrease the visual distortion. Interpolation is an example of this type of operation. However, previous interpolation-based data hiding algorithms suffered from low and fixed embedding capacity and high visual distortion. This study proposes a more effective two-stage data hiding algorithm based on interpolation, LSB substitution, and histogram shifting. First, the authors modify the formula for embedding capacity calculation and make some adjustments on the sample pixels determination. A threshold is used to obtain the block complexity and each embeddable pixel has a different amount of message embedded. Second, an LSB substitution method and an optimal pixel adjustment process are adopted to raise the image quality. Finally, the authors' proposed algorithm can support adjustable embedding capacity. Compared to the previous algorithm, the experimental results demonstrate the feasibility of the proposed method.

The exponential growth of multimedia communication necessitates advanced techniques for secure data transmission. This paper details a new data hiding method centered on a predictive voting mechanism that leverages neighboring pixels to estimate a pixel’s value. Secret data are concealed within these predictions via a purpose-built lookup table, and the retrieval process involves re-estimating the predicted pixels and applying an inverse mapping function. Experimental results demonstrate that the proposed method achieves an embedding capacity of up to 686,874 bits, significantly outperforming previous approaches while maintaining reliable data recovery. Compared with existing schemes, our approach offers improved performance in terms of both embedding capacity and extraction accuracy, making it an effective solution for robust multimedia steganography.