Robust Image Hashing Based on Random Gabor Filtering and Dithered Lattice Vector Quantization

In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on image processing 2012-04, Vol.21 (4), p.1963-1980
Main Authors: Li, Yuenan, Lu, Zheming, Zhu, Ce, Niu, Xiamu
Format: Article
Language:English
Subjects:
Algorithms
Authentication
Data Compression - methods
Data Interpretation, Statistical
Dithered lattice vector quantization (LVQ)
Feature extraction
image authentication
Image Enhancement - methods
Image Interpretation, Computer-Assisted - methods
Lattices
Reproducibility of Results
robust hashing
Robustness
Sensitivity and Specificity
Signal Processing, Computer-Assisted
Vector quantization
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In this paper, we propose a robust-hash function based on random Gabor filtering and dithered lattice vector quantization (LVQ). In order to enhance the robustness against rotation manipulations, the conventional Gabor filter is adapted to be rotation invariant, and the rotation-invariant filter is randomized to facilitate secure feature extraction. Particularly, a novel dithered-LVQ-based quantization scheme is proposed for robust hashing. The dithered-LVQ-based quantization scheme is well suited for robust hashing with several desirable features, including better tradeoff between robustness and discrimination, higher randomness, and secrecy, which are validated by analytical and experimental results. The performance of the proposed hashing algorithm is evaluated over a test image database under various content-preserving manipulations. The proposed hashing algorithm shows superior robustness and discrimination performance compared with other state-of-the-art algorithms, particularly in the robustness against rotations (of large degrees).
ISSN:1057-7149
1941-0042
DOI:10.1109/TIP.2011.2171698