Robust Hashing via Global and Local Invariant Features for Image Copy Detection

Author:

Liang Xiaoping1ORCID,Tang Zhenjun1ORCID,Li Zhixin1ORCID,Yu Mengzhu1ORCID,Zhang Hanyun1ORCID,Zhang Xianquan1ORCID

Affiliation:

1. Key Lab of Education Blockchain and Intelligent Technology, Ministry of Education, and Guangxi Key Lab of Multi-Source Information Mining & Security, Guangxi Normal University, China

Abstract

Robust hashing is a powerful technique for processing large-scale images. Currently, many reported image hashing schemes do not perform well in balancing the performances of discrimination and robustness, and thus they cannot efficiently detect image copies, especially the image copies with multiple distortions. To address this, we exploit global and local invariant features to develop a novel robust hashing for image copy detection. A critical contribution is the global feature calculation by gray level co-occurrence moment learned from the saliency map determined by the phase spectrum of quaternion Fourier transform, which can significantly enhance discrimination without reducing robustness. Another essential contribution is the local invariant feature computation via Kernel Principal Component Analysis (KPCA) and vector distances. As KPCA can maintain the geometric relationships within image, the local invariant features learned with KPCA and vector distances can guarantee discrimination and compactness. Moreover, the global and local invariant features are encrypted to ensure security. Finally, the hash is produced via the ordinal measures of the encrypted features for making a short length of hash. Numerous experiments are conducted to show efficiency of our scheme. Compared with some well-known hashing schemes, our scheme demonstrates a preferable classification performance of discrimination and robustness. The experiments of detecting image copies with multiple distortions are tested and the results illustrate the effectiveness of our scheme.

Funder

Guangxi Natural Science Foundation

National Natural Science Foundation of China

Guangxi “Bagui Scholar” Team for Innovation and Research, Guangxi Talent Highland Project of Big Data Intelligence and Application

Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing

Publisher

Association for Computing Machinery (ACM)

Subject

Computer Networks and Communications,Hardware and Architecture

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3. Online Cross-modal Hashing With Dynamic Prototype;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-06-13

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