General Framework of Reversible Watermarking Based on Asymmetric Histogram Shifting of Prediction Error

This paper presents a general framework for the reversible watermarking based on asymmetric histogram shifting of prediction error, which is inspired by reversible watermarking of prediction error. Different from the conventional algorithms using single-prediction scheme to create symmetric histogram, the proposed method employs a multi-prediction scheme, which calculates multiple prediction values for the pixels. Then, the suitable value would be selected by two dual asymmetric selection functions to construct two asymmetric error histograms. Finally, the watermark is embedded in the two error histograms separately utilizing a complementary embedding strategy. The proposed framework provides a new perspective for the research of reversible watermarking, which brings about many benefits for the information security

Digital watermarking : applicability for developing trust in medical imaging workflows state of the art review

Medical images can be intentionally or unintentionally manipulated both within the secure medical system environment and outside, as images are viewed, extracted and transmitted. Many organisations have invested heavily in Picture Archiving and Communication Systems (PACS), which are intended to facilitate data security. However, it is common for images, and records, to be extracted from these for a wide range of accepted practices, such as external second opinion, transmission to another care provider, patient data request, etc. Therefore, confirming trust within medical imaging workflows has become essential. Digital watermarking has been recognised as a promising approach for ensuring the authenticity and integrity of medical images. Authenticity refers to the ability to identify the information origin and prove that the data relates to the right patient. Integrity means the capacity to ensure that the information has not been altered without authorisation. This paper presents a survey of medical images watermarking and offers an evident scene for concerned researchers by analysing the robustness and limitations of various existing approaches. This includes studying the security levels of medical images within PACS system, clarifying the requirements of medical images watermarking and defining the purposes of watermarking approaches when applied to medical images

ENHANCED REVERSIBLE IMAGE DATA HIDING BASED ON BLOCK HISTOGRAM SHIFTING AND PADHM

Due to the enhanced digital media on the web, information security and privacy protection issue have attracted the eye of information communication. Information hiding has become a subject of sizable im-portance. Currently each day there's very big drawback of information hacking into the networking space. There is variety of techniques offered within the trade to over-come this drawback. So, information hiding within the encrypted image is one in all the solutions, however the matter is that the original cover can't be losslessly recov-ered by this system. That’s why recently; additional and additional attention is paid to reversible information concealing in encrypted pictures however this technique drawback low hardiness. A completely unique technique is planned by reserving for embedding information be-fore encoding of the image takes place with the offered algorithmic rule. Currently the authentic person will hide the information simply on the image to produce authen-tication. The transmission and exchange of image addi-tionally desires a high security .This is the review paper regarding this reversible information hiding algorithms obtainable. As a result, because of histogram enlarge-ment and bar graph shifting embedded message and also the host image may be recovered dead. The embedding rate is enhanced and PSNR magnitude relation using novel technique

Deep Learning for Reversible Steganography: Principles and Insights

Deep-learning\textendash{centric} reversible steganography has emerged as a promising research paradigm. A direct way of applying deep learning to reversible steganography is to construct a pair of encoder and decoder, whose parameters are trained jointly, thereby learning the steganographic system as a whole. This end-to-end framework, however, falls short of the reversibility requirement because it is difficult for this kind of monolithic system, as a black box, to create or duplicate intricate reversible mechanisms. In response to this issue, a recent approach is to carve up the steganographic system and work on modules independently. In particular, neural networks are deployed in an analytics module to learn the data distribution, while an established mechanism is called upon to handle the remaining tasks. In this paper, we investigate the modular framework and deploy deep neural networks in a reversible steganographic scheme referred to as prediction-error modulation, in which an analytics module serves the purpose of pixel intensity prediction. The primary focus of this study is on deep-learning\textendash{based} context-aware pixel intensity prediction. We address the unsolved issues reported in related literature, including the impact of pixel initialisation on prediction accuracy and the influence of uncertainty propagation in dual-layer embedding. Furthermore, we establish a connection between context-aware pixel intensity prediction and low-level computer vision and analyse the performance of several advanced neural networks

Reversible Data Hiding in Encrypted Text Using Paillier Cryptosystem

Reversible Data Hiding in Encrypted Domain (RDHED) is an innovative method that can keep cover information secret and allows the data hider to insert additional information into it. This article presents a novel data hiding technique in an encrypted text called Reversible Data Hiding in Encrypted Text (RDHET). Initially, the original text is converted into their ASCII values. After that, the Paillier cryptosystem is adopted to encrypt all ASCII values of the original text and send it to the data hider for further processing. At the data hiding phase, the secret data are embedded into homomorphically encrypted text using a technique that does not lose any information, i.e., the homomorphic properties of the Paillier cryptosystem. Finally, the embedded secret data and the original text are recovered at the receiving end without any loss. Experimental results show that the proposed scheme is vital in the context of encrypted text processing at cloud-based services. Moreover, the scheme works well, especially for the embedding phase, text recovery, and performance on different security key sizes

Data hiding using integer lifting wavelet transform and DNA computing

DNA computing widely used in encryption or hiding the data. Many researchers have proposed many developments of encryption and hiding algorithms based on DNA sequence to provide new algorithms. In this paper data hiding using integer lifting wavelet transform based on DNA computing is presented. The transform is applied on blue channel of the cover image. The DNA encoding used to encode the two most significant bits of LL sub-band. The produced DNA sequence used for two purpose, firstly, it use to construct the key for encryption the secret data and secondly to select the pixels in HL, LH, HH sub-bands for hiding in them. Many measurement parameters used to evaluate the performance of the proposed method such PSNR, MSE, and SSIM. The experimental results show high performance with respect to different embedding rate

High capacity data embedding schemes for digital media

High capacity image data hiding methods and robust high capacity digital audio watermarking algorithms are studied in this thesis. The main results of this work are the development of novel algorithms with state-of-the-art performance, high capacity and transparency for image data hiding and robustness, high capacity and low distortion for audio watermarking.En esta tesis se estudian y proponen diversos métodos de data hiding de imágenes y watermarking de audio de alta capacidad. Los principales resultados de este trabajo consisten en la publicación de varios algoritmos novedosos con rendimiento a la altura de los mejores métodos del estado del arte, alta capacidad y transparencia, en el caso de data hiding de imágenes, y robustez, alta capacidad y baja distorsión para el watermarking de audio.En aquesta tesi s'estudien i es proposen diversos mètodes de data hiding d'imatges i watermarking d'àudio d'alta capacitat. Els resultats principals d'aquest treball consisteixen en la publicació de diversos algorismes nous amb rendiment a l'alçada dels millors mètodes de l'estat de l'art, alta capacitat i transparència, en el cas de data hiding d'imatges, i robustesa, alta capacitat i baixa distorsió per al watermarking d'àudio.Societat de la informació i el coneixemen

Reversible difference expansion multi-layer data hiding technique for medical images

Maintaining the privacy and security of confidential information in data communication has always been a major concern. It is because the advancement of information technology is likely to be followed by an increase in cybercrime, such as illegal access to sensitive data. Several techniques were proposed to overcome that issue, for example, by hiding data in digital images. Reversible data hiding is an excellent approach for concealing private data due to its ability to be applied in various fields. However, it yields a limited payload and the quality of the image holding data (Stego image), and consequently, these two factors may not be addressed simultaneously. This paper addresses this problem by introducing a new non-complexity difference expansion (DE) and block-based reversible multi-layer data hiding technique constructed by exploring DE. Sensitive data are embedded into the difference values calculated between the original pixels in each block with relatively low complexity. To improve the payload capacity, confidential data are embedded in multiple layers of grayscale medical images while preserving their quality. The experiment results prove that the proposed technique has increased the payload with an average of 369999 bits and kept the peak signal to noise ratio (PSNR) to the average of 36.506 dB using medical images' adequate security the embedded private data. This proposed method has improved the performance, especially the secret size, without reducing much the quality. Therefore, it is suitable to use for relatively big payloads