Robust Image Hashing Using Radon Transform and Invariant Features

Abstract

A robust image hashing method based on radon transform and invariant features is proposed for image authentication, image retrieval, and image detection. Specifically, an input image is firstly converted into a counterpart with a normalized size. Then the invariant centroid algorithm is applied to obtain the invariant feature point and the surrounding circular area, and the radon transform is employed to acquire the mapping coefficient matrix of the area. Finally, the hashing sequence is generated by combining the feature vectors and the invariant moments calculated from the coefficient matrix. Experimental results show that this method not only can resist the normal image processing operations, but also some geometric distortions. Comparisons of receiver operating characteristic (ROC) curve indicate that the proposed method outperforms some existing methods in classification between perceptual robustness and discrimination.