13-05-2014, 04:58 PM
Secure watermarking scheme using homomorphic encryption for compressed JPEG2000 images
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DIGITAL IMAGE PROCESSING:
Image: An image may be defined as a two-dimensional function,
f(x , y), where x and y are spatial (plane) coordinates, and the amplitude of f at any pair of coordinates (x,y) is called the intensity or gray level of the image at that point. When x,y and the amplitude values of f are all finite, discrete quantities, then the image is a digital image.
Digital Image: A digital image is composed of finite number of elements, each of which has a particular location and value. These elements are referred to as picture elements, image elements, pels and pixels. Pixel is the term used to denote the elements of a digital image.
The pixel (a word invented from "picture element") is the basic unit of programmable color on a computer display or in a computer image. A digital image is composed of pixels arranged in a rectangular array with a certain height and width. Each pixel may consist of one or more bits of information, representing the brightness of the image at that point and possibly including color information encoded as RGB triples.
Frequency Domain Techniques:
The frequency-domain techniques modify the values of some transformed coefficients. The frequency domain technique first transforms an image into a set of frequency domain coefficients. The watermark is then embedded in the transformed coefficients of the image such that the watermark is invisible and more robust for some image processing operations. Finally, the coefficients are inverse transformed to obtain the watermarked image. This technique is complex and watermark cannot be easily recovered at the receiver end as compared to the spatial domain technique.
Discrete Cosine Transform (DCT):
DCT based watermarking techniques are more robust compared to simple spatial domain watermarking techniques. However, they are difficult to implement and are computationally more expensive. At the same time they are weak against geometric attacks like rotation, scaling, cropping etc. Embedding the DCT coefficients in the perceptually significant portion of the image has its own advantages because most compression schemes remove the perceptually insignificant portion of the image. In spatial domain it represents the LSB however in the frequency domain it represents the high frequency components.
Advantages of DWT over DCT
1) Wavelet transform understands the HVS more closely than the DCT.
2) Wavelet coded image is a multi-resolution description of image.
3) Wavelet transform doesn’t decompose the image into blocks for processing as in DCT.
4) A wavelet transform provides both frequency and spatial description for an image.
5) The underlying concept for DCT and DWT is the same; however, the process to transform the image into its transform domain varies and hence the resulting coefficients are different. Wavelet transforms use wavelet filters to transform the image.
Wavelet filters decomposes the image into several frequencies. Single level decomposition gives four frequency representations of the images. These four representations are called the LL, LH, HL, HH subbands. We take the weak coefficients from the approximation subband, and the highest coefficients from the three details subbands. These all coefficients that are used as the host signal x, must be rearranged in order to keep the same number of them as it was done for the DCT domain.
TYPES OF ATTACKS:
Removal Attacks
Geometric attacks
Cryptographic Attacks
Protocol Attacks
Remodulation Attacks
Collision Attacks