Perceptual Image Hashing With Texture and Invariant Vector Distance for Copy Detection

Content-based image copy detection has become one of the important technologies in copyright protection, where two major processes, content-based feature extraction and matching are included. However, it is certainly true that enough storage space is required to establish feature database for matching, which greatly increases time and storage consumption, as well as lacks flexibility. Fortunately, perceptual image hashing is a good strategy to address these problems, in which content-based features are extracted and further encoded to hash codes. On the one hand, content-based features provide and ensure higher copy detection accuracy, while on the other hand, hash codes instead of feature database reduce storage space and improve time efficiency. Meanwhile, a better balance between robustness and discrimination is one of the most objectives of image hashing, which is conducive to its application in multimedia management and security. Consequently, we present an effective image hashing method for copy detection. Specifically, to obtain perceptual robustness against to copy attacks, we extract the global statistical characteristics in gray-level co-occurrence matrix (GLCM) to reveal texture changes. Then, to make up the discrimination limitation, we leverage the local dominant DCT coefficients from the first row/column in each sub-image to calculate vector distance. Finally, two kinds of complementary information (global feature via texture and local feature via vector distance) are simultaneously preserved to generate hash codes. Various experiments performed on benchmark database indicate that our proposed perceptual image hashing provides higher detection accuracy and better balance between robustness and discrimination than the state-of-the-art algorithms.

Automated summary

Content-based image copy detection technology involves two key processes of feature extraction and matching. Perceptual image hashing can address the issues of large storage requirement and lack of flexibility, while improving detection accuracy. Maintaining a balance between robustness and discrimination is a major objective of image hashing technology.