Robust Hashing for Image Authentication Using Zernike Moments, Gabor Wavelets and Histogram Features

For detecting image forgery including removal, insertion, and replacement of objects, and abnormal colour modification and for locating the forged area a robust hashing method is developed. Global, local and histogram features are used in forming the hash sequence. The global features are based on Zernike moments representing luminance and chrominance characteristics of the image. The local features are extracted using Gabor wavelets which includes position and texture information of salient regions in the image. The histogram features includes the number of pixels in the same intensity. Secret keys are introduced in feature extraction and hash construction. Being robust against content-preserving image processing, the hash is sensitive to malicious tampering and applicable for image authentication. The hash of a test image is compared with that of a reference image. When the hash distance is greater than a threshold and less than, the received image is judged as a fake. By decomposing the hashes, the type of image forgery and location of forged areas can be determined.