Image Hashing And Content Authentication Based On Zernike Moments

With the development of network and multimedia technique, digital media suchas image, audio and video, are transmitted and applied widely. Digital media can beprocessed easily. Ensuring security and credibility of the media contents has becomean important issue in information security. Many researchers pay more attentions onimage hashing which is an initiative authentication method. Image hashing maps animage to a short binary sequence representing the image’s characteristics. In thisdissertation, we research on the basic theory of image hash and propose several imagehashing methods. The contributions of this dissertation are listed as follows:1．Image hashing using shape featureShape feature of an object is invariant to color and environment. We propose animage hashing method based on Zernike moments. The amplitude and phase ofZernike moments of preprocessed image are quantified, connected and permuted togenerate the image hash. The hash length is216bits. It is robust against thecontent-preserving procession such as rotation. It has good performance ofdifferentiating similar images and forged images.2．Image hashing based on conformal mapping and Zernike momentsThe input image is pseudo-randomly partitioned into blocks, which are resized tobecome squares of a standard size and mapped to a unit circle by conformal mapping.Zernike moments of the circular-shaped “images” are calculated. All amplitudes andphases of the modified Zernike moments are concatenated to form an intermediatehash sequence. The final hash is produced by pseudo-randomly permuting theintermediate hash. This method extracts local information to form image hash, so ithas long hash length, good robustness and uniqueness. Image blocking allows the tampered regions in the image to be correctly located. Compared to some othertechniques, the method has low probabilities of collision and errors.3．Image hashing for color forgery detectionThe color space is transmitted from RGB to YCbCr. Then each component ismapped from rectangle to circle. At last the amplitude and phase of Zernikemoments is connected and permuted to form the final image hash. The hash length is66numbers, which is longer than the first method. But it includes the intensity andcolor information of the image, so this method can be used to detect unusual contentand color changes.4．Image hashing based on saliency feature and Zernike momentsMany previous image hash schemes are either based on global or local featureswith various advantages and disadvantages. Both global and local features are used inproposing hash sequence. The global features are based on Zernike momentsrepresenting luminance and chrominance characteristics of the image as a whole. Thelocal features include position and texture information of salient regions in the image.Secret keys are introduced in feature extraction and hash construction for security.While being robust against content-preserving image processing, the hash is sensitiveto malicious tampering and therefore applicable to image authentication. Bydecomposing the hashes, the type of image forgery and location of forged areas can bedetermined.At last these methods are compared in conclusions.