COMPARATIVE ANALYSIS OF STEGANOGRAPHIC ALGORITHMS INTACTING THE INFORMATION IN THE MEDICAL IMAGES REGARDING THEIR EFFICIENCY

Abstract

Steganography is the art and science of invisible communication. This is accomplished through hiding information in other   information, thus hiding the existence of  the communicated information. The word steganography is derived from the Greek words “stegos” meaning “cover” and  “grafia” meaning “writing” defining it as “covered writing”. In image steganography the information is hidden exclusively  in images. Three different aspects in information-hiding systems contend with each other: capacity, security, and robustness. Capacity refers to the amount of information that can be hidden in the cover medium, security to an  eavesdropper’s inability to detect hidden information, and robustness to the amount of modification the stego medium  can withstand before an adversary can destroy hidden information. Adaptive steganography with a high embedding  capacity and a low distortion is an attractive topic in the area of information hiding. In digital images,  parts with high contrast and noise-like textures have been found to be appropriate locations to hide pseudo-random encrypted messages, due to the statistical similarities between the covert and the selected cover signals .  Digital Steganography is the art of invisibly hiding data within data. It conceals the fact that message exists by hiding the actual message. In this, secret data can be hidden inside the image, text, sound clip which can be represented in binary. Digital steganography is propose to increase medical image security, confidentiality and integrity. Medical image steganography is a special subcategory of image steganography in the sense that the images have special requirements. This paper presents a preliminary study on the degradation of medical images when embedded with different different steganographic algorithm, using a variety of popular systems. Image quality is measured with a number of widely used metrics, which is applied elsewhere in image processing. The general conclusion that arises from the results is that typical data  embedding can cause numerical and perceptual errors in an image.