Robust image hashing using SIFT feature points and DWT approximation coefficients

Abstract

This study proposes a robust hashing method using scale-invariant feature transform (SIFT) features points and discrete wavelet transform (DWT) approximation coefficients for image authentication. Initially, the invariant feature points are computed using SIFT from the L ∗ component of L ∗ a ∗ b ∗ color image. Next, n distinct SIFT feature points are utilized to extract image content from the L ∗ component. Then, DWT is applied to extracted content in order to compute approximation coefficients. Finally, the approximation coefficients are normalized to form a binary hash. Experimental results show that the proposed method is robust to various content-preserving operations such as compression, scaling, filtering, additive noise, brightness, and contrast adjustment. In addition, the performance of the proposed method is compared to existing methods using a receiver operating characteristics curve. The comparison results show that the proposed method performs better than the existing methods. Keywords: Image hashing, Image authentication, Scale-invariant feature transform, Discrete wavelet transform, Robustness and anti-collision