Sub-images based image hashing with non-negative factorization

This paper proposes a robust and secure perceptual image hashing technique based on random sub-image selection, non-negative matrix factorization and DWT transform where the hashes are extracted in a binary form. The binary hash values are obtained with a perfect control over the probability distribution of hash bits. A key is used randomize the feature vector. Experimentally, the proposed technique has been shown to yield a good performance with respect to robustness against image processing operations including JPEG lossy compression, additive noise and geometric attacks such as rotation, translation and more secure of image hashing scheme.