School of Electronic and Information Engineering, Tianjin University, Tianjin, China.
IEEE Trans Image Process. 2012 Apr;21(4):1963-80. doi: 10.1109/TIP.2011.2171698. Epub 2011 Oct 13.
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).
在本文中，我们提出了一种基于随机 Gabor 滤波和抖动晶格矢量量化 (LVQ) 的鲁棒哈希函数。为了提高对旋转操作的鲁棒性，我们对传统的 Gabor 滤波器进行了适应性调整，使其具有旋转不变性，并对旋转不变滤波器进行了随机化处理，以方便安全的特征提取。特别地，我们提出了一种新颖的基于抖动 LVQ 的量化方案用于鲁棒哈希。基于抖动 LVQ 的量化方案非常适合鲁棒哈希，具有几个理想的特性，包括更好的鲁棒性和鉴别力之间的权衡、更高的随机性和保密性，这通过分析和实验结果得到了验证。我们在经过各种内容保留操作的测试图像数据库上评估了所提出的哈希算法的性能。与其他最先进的算法相比，所提出的哈希算法在鲁棒性和鉴别力方面表现出了优越的性能，特别是在对大角度旋转的鲁棒性方面。
