Survey on Reversible Data Hiding in Encrypted Images Using POB Histogram Method

This paper describes a survey on reversible data hiding in encrypted images. Data hiding is a process to embed useful data into cover media. Data invisibility is its major requirement. Data hiding can be done in audio, video, image, text, and picture. Here use an image for data hiding especially digital images and existing method (Histogram Block Shift Base Method) HBSBM or POB. Now a day's reversible data hiding in encrypted images is in use due to its excellent property which is original cover image can be recovered with no loss after extraction of the embedded data. Also, it protects the original data. According to the level and kind of application one or more data hiding methods is used. Data hiding can be done in audio, video, text, and image and other forms of information. Some data hiding techniques emphasize on digital image security, some on the robustness of digital image hiding process while other's main focus is on imperceptibility of a digital image. The capacity of digital information which has to hide is also the main concern in some of the applications. The objective of some of the papers mentioned below is to achieve two or more than two parameters i.e. Security, robustness, imperceptibility and capacity but some of the parameters are trade-off which means only one can be achieved on the cost of other. So the data hiding techniques aiming to achieve maximum requirements i.e. security, robustness, capacity, imperceptibility etc. and which can be utilized in the larger domain of applications is desired. Related work for techniques used for data hiding in a digital image is described in this paper

Very High Embedding Capacity Algorithm for Reversible Image Watermarking

Reversible image watermarking enables the embedding of copyright or useful information in a host image without any loss of information. Here a novel technique to improve the embedding capacity i.e. reversible watermarking using an adaptive prediction error expansion & pixel selection is proposed. This work is an improvement in conventional Prediction Error Expansion by adding two new techniques adaptive embedding & pixel selection. Instead of uniform embedding, here one or two bits of watermark are adaptively embed into the expandable pixels as per the regional complexity. Adaptive Prediction Error Expansion can obtain the embedded rate upto 1.3 bits per pixel as compared to the 1 BPP of conventional Prediction Error Expansion. Also an intermediate step of prediction error expansion is proposed to select relatively smooth pixels and ignore the rough ones. In other words, the rough pixels may remain unchanged, and only smooth pixels are expanded or shifted. Therefore compared with conventional Prediction Error Expansion, a more sharply distributed prediction error histogram is obtained i.e. , and a larger proportion of prediction-errors in the histogram are expanded to carry hidden data. So the amount of shifted pixels is diminished, which leads to a better image quality. With these improvements, this method performs better than conventional Prediction Error Expansion. It can embed larger payloads with less distortion (almost 30% greater than the conventional method). DOI: 10.17762/ijritcc2321-8169.150510

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

Ant colony optimization (ACO) based data hiding in image complex region

This paper presents data an Ant colony optimization (ACO) based data hiding technique. ACO is used to detect complex region of cover image and afterward, least significant bits (LSB) substitution is used to hide secret information in the detected complex regions’ pixels. ACO is an algorithm developed inspired by the inborn manners of ant species. The ant leaves pheromone on the ground for searching food and provisions. The proposed ACO-based data hiding in complex region establishes an array of pheromone, also called pheromone matrix, which represents the complex region in sequence at each pixel position of the cover image. The pheromone matrix is developed according to the movements of ants, determined by local differences of the image element’s intensity. The least significant bits of complex region pixels are substituted with message bits, in order to hide secret information. The experimental results, provided, show the significance of the performance of the proposed method

Ant Colony Optimization (ACO) based Data Hiding in Image Complex Region

This paper presents data an Ant colony optimization (ACO) based data hiding technique. ACO is used to detect complex region of cover image and afterward, least significant bits (LSB) substitution is used to hide secret information in the detected complex regions’ pixels. ACO is an algorithm developed inspired by the inborn manners of ant species. The ant leaves pheromone on the ground for searching food and provisions. The proposed ACO-based data hiding in complex region establishes an array of pheromone, also called pheromone matrix, which represents the complex region in sequence at each pixel position of the cover image. The pheromone matrix is developed according to the movements of ants, determined by local differences of the image element’s intensity. The least significant bits of complex region pixels are substituted with message bits, to hide secret information. The experimental results, provided, show the significance of the performance of the proposed method