Robust Image Hashing With Ring Partition and Invariant Vector Distance
Abstract
Robustness and discrimination are two of the most important objectives in image hashing. We incorporate ring partition and invariant vector distance to image hashing algorithm for enhancing rotation robustness and discriminative capability. As ring partition is unrelated to image rotation, the statistical features that are extracted from image rings in perceptually uniform color space, i.e., CIE L*a*b* color space, are rotation invariant and stable. In particular, the Euclidean distance between vectors of these perceptual features is invariant to commonly used digital operations to images (e.g., JPEG compression, gamma correction, and brightness/contrast adjustment), which helps in making image hash compact and discriminative. We conduct experiments to evaluate the efficiency with 250 color images, and demonstrate that the proposed hashing algorithm is robust at commonly used digital operations to images. In addition, with the receiver operating characteristics curve, we illustrate that our hashing is much better than the existing popular hashing algorithms at robustness and discrimination.
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Self-citation
- Authors
- Tang, Z., Zhang, X., Li, X., & Zhang, S.
- Affiliations
- Guangxi Normal University (CN)
- University of Technology Sydney (AU)
- Venue
- IEEE Transactions on Information Forensics and Security
- Published
- 2016-01
- Volume
- 11
- Issue
- 1
- Pages
- 200-214
- ISSN
- 1556-6013 (ISSN-L)
- 1556-6021
- OpenAlex
- W2183964636
- Cited by
- 247
- References
- 40
- Citations by year
- 2026: 6 · 2025: 13 · 2024: 24 · 2023: 27 · 2022: 29 · 2021: 32 · 2020: 30 · 2019: 27 · 2018: 25 · 2017: 24 · 2016: 10
- FWCI
- 17.06
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- IEEE Transactions on Information Forensics and Security (publishedVersion)
- Funders
- China 973 Program (2013CB329404, 2012CB326403)
- Guangxi Natural Science Foundation (2012GXNSFGA060004, 2015GXNSFDA139040)
- Guangxi “Bagui Scholar” Teams for Innovation and Research
- Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing
- Scientific and Technological Research Projects of Guangxi Education Administration (YB2014048)
- Project of the Guangxi Key Laboratory of Multi-Source Information Mining and Security (14-A-02-02)
- National Natural Science Foundation of China (61300109, 61363034, 61562007, 61450001, 61170131)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Steganography and Watermarking Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image Retrieval and Classification Techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Digital Media Forensic Detection
- Keywords
- image hashing, rotation invariance, CIELAB color space, JPEG compression, gamma correction, brightness and contrast adjustment
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