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

Robust Image Hashing Based on Random Gabor Filtering and Dithered Lattice Vector Quantization
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基于随机Gabor滤波和抖动格矢量量化的鲁棒图像哈希

DOI:
10.1109/tip.2011.2171698
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发表时间:
2012-04
影响因子:
10.6
通讯作者:
Niu, Xiamu
Niu, Xiamu
中科院分区:
计算机科学1区
文献类型:
--
作者:
Li, Yuenan;Lu, Zheming;Zhu, Ce;Niu, Xiamu

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本文提出了一种基于随机Gabor滤波和抖动格型矢量量化的鲁棒散列函数。为了增强对旋转操作的鲁棒性,传统的Gabor滤波器被适配为旋转不变的,并且旋转不变滤波器被随机化以便于安全的特征提取。特别地,提出了一种新的基于抖动LVQ的量化方案,用于鲁棒散列。基于Dithered-LVQ的量化方案非常适合鲁棒散列,具有几个理想的功能,包括更好的鲁棒性和歧视之间的权衡,更高的随机性和保密性,这是通过分析和实验结果验证。所提出的散列算法的性能进行评估,在测试图像数据库中的各种内容保持操作。该算法与其他现有算法相比,具有上级鲁棒性和鉴别性能,特别是对大角度旋转的鲁棒性。
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).
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