Reversible Data Hiding Based on Varying Radix Numeral System

A novel image reversible data-hiding scheme based on primitive and varying radix numerical model is presented in this article.Using varying radix,variable sum of data may be embedded in various pixels of images.This scheme is made adaptive using the correlation of the neighboring pixels.Messages are embedded as blocks of non-uniform length in the high-frequency regions of the rhombus mean interpolated image.A higher amount of data is embedded in the high-frequency regions and lesser data in the low-frequency regions of the image.The size of the embedded data depends on the statistics of the pixel distribution in the cover image.One of the major issues in reversible data embedding,the location map,is minimized because of the interpolation process.This technique,which is actually LSB matching,embeds only the residuals of modulo radix into the LSBs of each pixel.No attacks on this RDH technique will be able to decode the hidden content in the marked image.The proposed scheme delivers a prominent visual quality despite high embedding capacity.Experimental tests carried out on over 100 natural image data sets and medical images show an improvement in results compared to the existing schemes.Since the algorithm is based on the variable radix number system,it is more resistant to most of the steganographic attacks.The results were compared with a higher embedding capacity of up to 1.5 bpp reversible schemes for parameters like Peak Signal-to-Noise Ratio(PSNR),Embedding Capacity(EC)and Structural Similarity Index Metric(SSIM).

Reversible Data Hiding in Encrypted Images Based on Adaptive Prediction and Labeling

Recently,reversible data hiding in encrypted images(RDHEI)based on pixel prediction has been a hot topic.However,existing schemes still employ a pixel predictor that ignores pixel changes in the diagonal direction during prediction,and the pixel labeling scheme is inflexible.To solve these problems,this paper proposes reversible data hiding in encrypted images based on adaptive prediction and labeling.First,we design an adaptive gradient prediction(AGP),which uses eight adjacent pixels and combines four scanning methods(i.e.,horizontal,vertical,diagonal,and diagonal)for prediction.AGP can adaptively adjust the weight of the linear prediction model according to the weight of the edge attribute of the pixel,which improves the prediction ability of the predictor for complex images.At the same time,we adopt an adaptive huffman coding labeling scheme,which can adaptively generate huffman codes for labeling according to different images,effectively improving the scheme’s embedding performance on the dataset.The experimental results show that the algorithm has a higher embedding rate.The embedding rate on the test image Jetplane is 4.2102 bpp,and the average embedding rate on the image dataset Bossbase is 3.8625 bpp.

High capacity reversible data hiding in encrypted images based on adaptive quadtree partitioning and MSB prediction

To improve the embedding capacity of reversible data hiding in encrypted images(RDH-EI),a new RDH-EI scheme is proposed based on adaptive quadtree partitioning and most significant bit(MSB)prediction.First,according to the smoothness of the image,the image is partitioned into blocks based on adaptive quadtree partitioning,and then blocks of different sizes are encrypted and scrambled at the block level to resist the analysis of the encrypted images.In the data embedding stage,the adaptive MSB prediction method proposed by Wang and He(2022)is improved by taking the upper-left pixel in the block as the target pixel,to predict other pixels to free up more embedding space.To the best of our knowledge,quadtree partitioning is first applied to RDH-EI.Simulation results show that the proposed method is reversible and separable,and that its average embedding capacity is improved.For gray images with a size of 512×512,the average embedding capacity is increased by 25565 bits.For all smooth images with improved embedding capacity,the average embedding capacity is increased by about 35530 bits.