Compressed Binary Image Hashes Based on Semisupervised Spectral Embedding

Conventional image hashing maps invariant features of each digital image into a unique, compact, robust, and secure signature, which can be used as an index for fast content identification and copyright protection. This paper addresses an important issue of compressing the real-valued image hashes into short binary signatures, which can support fast image identification using Hamming distance metrics. The proposed binary image hashing approach presents a fundamental departure from existing methods: Prior information from virtual image distortions and attacks is explored the first time in image hash generation. More specifically, the proposed scheme takes advantages of the extended hash feature space from virtual distortions and attacks and generates the binary signature for each image based on spectral embedding. Since the objective function to learn the embedding is designed to both preserve local similarity between distorted copies of the same image and to distinguish visually distinct images, the generated binary image hash is more robust compared with the one using conventional quantization-based compression approaches. Further, the proposed method can be generalized to combine different types of image hashes to generate a fixed-length binary signature. Our experimental results demonstrate that the proposed binary image hash by combining different real-valued image hashes is more robust against various distortions and it is computationally efficient for image similarity comparison using Hamming metrics.