Now a day’s digital images are manipulated using powerful image processing tools. It may lead to many problems like Copyright
infringement, hostile tampering to the image contents. A robust hashing method is developed for detecting image forgery including
removal, insertion, and replacement of objects, and abnormal color modification, and for locating the forged area. The global, local
and histogram features are used in forming the hash sequence. The global features are found out using Zernike moments. It represents
the luminance and chrominance characteristics of the image as a whole. The local features include position and texture information of
salient regions in the image. The histogram features includes the number of pixels with the same intensity. Secret keys are introduced
in feature extraction and hash construction. While being robust against content-preserving image processing, the hash is sensitive to
malicious tampering and, therefore, applicable to 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, 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.