Reversible Data Hiding in Encrypted Images Based on Hybrid Prediction and Huffman Coding

Abstract

As an interesting technique that allows data extraction and image recovery without any loss, reversible data hiding in encrypted images is an area of great concern in the field of information security. In this paper, a new reversible data hiding method with high embedding capacity is proposed based on hybrid prediction and Huffman coding. The combination of two embedding mechanisms is innovatively designed to improve the embedding capacity according to different parts of the original image, i.e., the most significant bit-plane and the remaining seven bit-planes. In the first part of this method, the prediction value of each pixel is obtained by calculating the average value of its two neighboring pixels, and all of the most significant bits can be vacated to accommodate additional data. In the second part, the prediction value of each pixel is calculated using the median edge detector predictor, on which the tag map is built. Then, Huffman coding is used to compress the tag map so that a large amount of vacant space is obtained. Finally, the secret data can be embedded into the vacated space by directly using bit substitution. Compared with some recently reported methods, experimental results and analysis have demonstrated that an original image with high visual symmetry/quality can be recovered. Also, larger embedding capacity can be achieved, such as 3.3894 bpp and 3.2824 bpp, for BOSSBase and BOWS2 databases, respectively.

Comprehensive Study of Compression and Texture Integration for Digital Imaging and Communications in Medicine Data Analysis

“…Then the generalized probability is expressed as Pr[sj] = Pj, ∑ n j=1 Pj = 1. The entropy from the source can be expanded with Equation (38)…”

“…The prominent techniques under lossy compression include transform coding [22,23], fractal compression [24,25], chroma sampling [26,27], discrete cosine transform (DCT) [28,29], and the vector quantization algorithm (VQA) [30,31]. On the other hand, lossless compression techniques include run-length encoding [32,33], entropy encoding [34,35], Lempel-Ziv-Welch (LZW) [36,37], and DEFLATE, which synergizes LZSS with Huffman coding [38,39]. Lossless compression is ideal for applications like technical drawings, and medical imaging as it preserves the quality of all original data.…”

On the Design of Multi-Party Reversible Data Hiding over Ciphered Overexposed Images

“…The ciphered image is then compressed on the basis of adaptive block coding; therefore, the room used for embedding data is vacated. In [19], Sui et al used a pseudo-random matrix-based symmetric encryption to encrypt a primitive image. The receiver uses the same key matrix to restore the primitive image due to the symmetry of the encryption algorithm.…”