Perceptual image hashing using local entropies and DWT

Abstract

Image hashing is an emerging technology in multimedia security. It uses a short string called image hash to represent an input image and finds applications in image authentication, tamper detection, digital watermark, image indexing, content-based image retrieval and image copy detection. This paper presents a hashing algorithm based on the observation that block entropies are approximately linearly changed after content-preserving manipulations. This is done by converting the input image to a fixed size, dividing the normalised image into non-overlapping blocks, extracting entropies of image blocks and applying a single-level 2D discrete wavelet transform to perform feature compression. Correlation coefficient is exploited to evaluate similarity between hashes. Experimental results show that the proposed algorithm is robust against content-preserving operations, such as JPEG compression, watermark embedding, Gamma correction, Gaussian low-pass filtering, adjustments of brightness and contrast, scaling and small angle rotation. Similarity values between hashes of different images are small, indicating good performances in discriminative capability.