An Improved Reversible Data Hiding with Hierarchical Embedding for Encrypted Images and BBET

This research introduces an enhanced reversible data hiding (RDH) approach incorporating hierarchical embedding for encrypted images and employs a novel technique termed BBET (Best Bits Embedding Technique). RDH involves concealing information within a host sequence, enabling the restoration of both the host sequence and embedded data without loss from the marked sequence. While RDH has traditionally found applications in media annotation and integrity authentication, its utilisation has expanded into diverse fields. Given the rapid advancements in digital communication, computer technologies, and the Internet, ensuring information security poses a formidable challenge in safeguarding valuable data. Various reversible and stenographic techniques exist for covertly embedding or protecting data, spanning text, images, and protocols, and facilitating secure transmission to intended recipients. An influential approach in data security is reversible data hiding in encrypted images (RDHEI). This paper distinguishes between the conventional RDHEI technique, characterised by lower Peak Signal-to-Noise Ratio (PSNR) and higher Mean Squared Error (MSE), and proposes an improved RDHEI technique. As the prevalence of digital techniques for image transmission and storage rises, preserving image confidentiality, integrity, and authenticity becomes paramount. Text associated with an image, such as authentication or author information, can serve as embedded data. The recipient must adeptly recover both the concealed data and the original image. Reversible data-hiding techniques ensure the exact recovery of the original carrier after extracting the encrypted data. Classification of RDHEI techniques is based on the implemented method employed. This paper delves into a comprehensive exploration of techniques applicable to difference expansion, histogram shifting, and compression embedding for reversible data hiding. Emphasis is placed on the necessity for a reversible data-hiding technique that meticulously restores the host image. Furthermore, the study evaluates performance parameters associated with encryption processes, scrutinising their security aspects. The investigation utilises the MATLAB tool to develop the proposed BBET technique, comparing its efficacy in embedding and achieving enhanced security features. The BBET technique is characterised by reliability, high robustness, and secure data hiding, making it a valuable addition to the evolving landscape of reversible data hiding methodologies

Image data hiding

Image data hiding represents a class of processes used to embed data into cover images. Robustness is one of the basic requirements for image data hiding. In the first part of this dissertation, 2D and 3D interleaving techniques associated with error-correction-code (ECC) are proposed to significantly improve the robustness of hidden data against burst errors. In most cases, the cover image cannot be inverted back to the original image after the hidden data are retrieved. In this dissertation, one novel reversible (lossless) data hiding technique is then introduced. This technique is based on the histogram modification, which can embed a large amount of data while keeping a very high visual quality for all images. The performance is hence better than most existing reversible data hiding algorithms. However, most of the existing lossless data hiding algorithms are fragile in the sense that the hidden data cannot be extracted correctly after compression or small alteration. In the last part of this dissertation, we then propose a novel robust lossless data hiding technique based on patchwork idea and spatial domain pixel modification. This technique does not generate annoying salt-pepper noise at all, which is unavoidable in the other existing robust lossless data hiding algorithm. This technique has been successfully applied to many commonly used images, thus demonstrating its generality

Reversible Data Hiding Scheme with High Embedding Capacity Using Semi-Indicator-Free Strategy

A novel reversible data-hiding scheme is proposed to embed secret data into a side-matched-vector-quantization- (SMVQ-) compressed image and achieve lossless reconstruction of a vector-quantization- (VQ-) compressed image. The rather random distributed histogram of a VQ-compressed image can be relocated to locations close to zero by SMVQ prediction. With this strategy, fewer bits can be utilized to encode SMVQ indices with very small values. Moreover, no indicator is required to encode these indices, which yields extrahiding space to hide secret data. Hence, high embedding capacity and low bit rate scenarios are deposited. More specifically, in terms of the embedding rate, the bit rate, and the embedding capacity, experimental results show that the performance of the proposed scheme is superior to those of the former data hiding schemes for VQ-based, VQ/SMVQ-based, and search-order-coding- (SOC-) based compressed images

Reversible Fragile Watermarking based on Difference Expansion Using Manhattan Distances for 2D Vector Map

AbstractThe need for publishing maps in secure digital format, especially guarantees data integrity which motivated us to propose a scheme that detects and locates modification data with high accuracy while ensuring exact recovery of the original content. In particular, using fragile watermarking algorithm based on reversible manner to embed hidden data in 2D vector map for each spatial features. In this paper, a reversible data-hiding scheme is explored based on the idea of difference expansion with Manhattan distances. A set of invertible integer mappings is defined to extract Manhattan distances from coordinates and the hidden data are embedded by modifying the differences between the adjacent distances. Experiments results show that the proposed scheme has good performance in term invisibility and tamper modification ability. The scheme could detect modification data such addition and deletion some features, and exactly recovery the original content of the 2D vector map

Large-capacity and Flexible Video Steganography via Invertible Neural Network

Video steganography is the art of unobtrusively concealing secret data in a cover video and then recovering the secret data through a decoding protocol at the receiver end. Although several attempts have been made, most of them are limited to low-capacity and fixed steganography. To rectify these weaknesses, we propose a Large-capacity and Flexible Video Steganography Network (LF-VSN) in this paper. For large-capacity, we present a reversible pipeline to perform multiple videos hiding and recovering through a single invertible neural network (INN). Our method can hide/recover 7 secret videos in/from 1 cover video with promising performance. For flexibility, we propose a key-controllable scheme, enabling different receivers to recover particular secret videos from the same cover video through specific keys. Moreover, we further improve the flexibility by proposing a scalable strategy in multiple videos hiding, which can hide variable numbers of secret videos in a cover video with a single model and a single training session. Extensive experiments demonstrate that with the significant improvement of the video steganography performance, our proposed LF-VSN has high security, large hiding capacity, and flexibility. The source code is available at https://github.com/MC-E/LF-VSN.Comment: Accepted by CVPR 202