Steganography is an art of writing for conveying message inside another media in a secret way that can only be detected by its intended recipient. There are security agents who would like to fight these data hiding systems by steganalysis, i.e. discovering covered secret messages and rendering them useless. Steganalysis is the art of detecting the message's existence, message length or place of message where it is to be hidden in covered media and blockading the covert communication. There is currently no more secured steganography system which can resist all steganalysis attacks such as visual attack, statistical attack (active and passive) or structural attack. The most notable steganalysis algorithm is the Reversible Statistical attack which detects the embedded message by the statistic analysis of pixel values. To maintain the security against the Reversible Statistical analysis, the proposed work presents a new steganography model based on Genetic Algorithm using Integer Wavelet Transform. We present a novel approach to resolve such problems of substitution technique of image steganography. Using the proposed Genetic Algorithm and Reversible Statistical analysis Algorithm, the system is more secured against attacks and increases robustness. The robustness would be increased against those attacks which try to reveal the hidden message and also some unintentional attacks like noise addition as well. In this proposed work, we studied the steganographic paradigm of data hiding in standard digital images. In recent literature, some algorithms have been proposed where marginal statistics are preserved for achieving more capacity and more security. This proposed system presents a novel technique to increase the data hiding capacity and the imperceptibility of the image after embedding the secret message. In proposed work Optimal Pixel Adjustment Process also applied to minimize the error difference between the cover and stego image. By this work best results have been obtained as compared to existing works. The proposed steganography model reduces the embedding error and provides higher embedding capacity. Detection of message existence will be very hard for those stego images that produced using the proposed method. This work shows the highest embedding capacity and security against Reversible Statistical attack.

In this paper two new blind adaptive digital watermarking methods of color images are presented. The adaptability is based on perceptual watermarking which exploits Human Visual System (HVS) models. The first method performs watermark embedding in transform domain of DCT and the second method is based on DWT. Watermark is embedded into transform domain of a chosen color image component in a selected color space. Both methods use a combination of HVS models to select perceptually significant transform coefficients and at the same time to determine the bounds of modification of selected coefficients. The final HVS model consists of three parts. The first part is the HVS model in DCT (DWT) domain. The second part is the HVS model based on Region of Interest and finally the third part is the HVS model based on Noise Visibility Function. Watermark has a form of a real number sequence with normal distribution.

In the paper two possible implementations of Human Visual System (HVS) models in digital watermarking of still images are presented. The first method performs watermark embedding in transform domain of Discrete Cosine Transform (DCT) and the second method is based on Discrete Wavelet Transform (DWT). Both methods use HVS models to select perceptually significant transform coefficients and at the same time to determine the bounds of modification of selected coefficients in watermark embedding process. The HVS models in DCT and DWT domains consist of three parts which exploit various properties of human eye. The first part is the HVS model in DCT (DWT) domain based on three basic properties of human vision: frequency sensitivity, luminance sensitivity and masking effects. The second part is the HVS model based on Region of Interest (ROI). It is composed of contrast thresholds as a function of spatial frequency and eye's eccentricity. The third part is the HVS model based on noise visibility in an image and is described by so called Noise Visibility Function (NVF). Watermark detection is performed without use of original image and watermarks have a form of real number sequences with normal distribution zero mean and unit variance. The robustness of presented perceptual watermarking methods against various types of attacks is also briefly discussed.

In this paper some Human Visual System (HVS) models used in digital image watermarking are presented. Four different HVS models, which exploit various properties of human eye, are described. Two of them operate in transform domain of Discrete Cosine Transform (DCT) and Discrete Wavelet Transform (DWT). HVS model in DCT domain consists of Just Noticeable Difference thresholds for corresponding DCT basis functions corrected by luminance sensitivity and selfor neighborhood contrast masking. HVS model in DWT domain is based on different HVS sensitivity in various DWT subbands. The third presented HVS model is composed of contrast thresholds as a function of spatial frequency and eye’s eccentricity. We present also a way of combining these three basic models to get better tradeoff between conflicting requirements of digital watermarks. The fourth HVS model is based on noise visibility in an image and is described by so called Noise Visibility Function (NVF). The possible ways of exploiting of the described HVS models in digital image watermarking are also briefly discussed.

This paper deals with some modifications of fractal image coding algorithms and implementation of digital watermarking in fractal image coding. The paper focuses on two approaches of fractal image coding: approximation based on linear polynomials with iterative decoding and approximation based on trigonometric polynomials with non-iterative decoding. The modifications in coding and decoding algorithm to get better quality of reconstructed image are presented too. More over three possible approaches of embedding digital watermarks into fractal code of images: embedding digital watermarks into coeflicients of block similarity, into parameters for position of similar blocks and into parameters for isometric transformation are proposed. The algorithms were analysed and verified on gray scale still images.