Robust image hashing via random gabor filtering and DWT
Robust image hashing via random gabor filtering and DWT
复制标题
通过随机 Gabor 过滤和 DWT 进行鲁棒图像哈希
DOI：
10.3970/cmc.2018.02222
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Shijie Xu
中科院分区：
文献类型：
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作者：
Zhenjun Tang;Man Ling;Heng Yao;Zhenxing Qian;Xianquan Zhang;Jilian Zhang;Shijie Xu
图像散列技术是一种多媒体技术，在图像认证、图像检索、图像拷贝检测和图像取证等方面有着广泛的应用。本文提出了一种基于随机Gabor滤波和离散小波变换的鲁棒图像散列算法。具体地说，鲁棒性和安全的图像特征提取从规范化的图像通过Gabor滤波和称为斜帐篷映射的混沌映射，然后通过一个单级的2-D DWT压缩。最后通过级联LL子带的DWT系数得到图像散列。在开放的图像数据集上进行了大量的实验，实验结果表明，该算法具有较好的鲁棒性、鉴别性和安全性。受试者工作特征（ROC）曲线的比较表明，我们的哈希是更好的一些流行的图像哈希算法的分类性能之间的鲁棒性和歧视。
Image hashing is a useful multimedia technology for many applications, such as image authentication, image retrieval, image copy detection and image forensics. In this paper, we propose a robust image hashing based on random Gabor filtering and discrete wavelet transform (DWT). Specifically, robust and secure image features are extracted from the normalized image by Gabor filtering and a chaotic map called Skew tent map, and then are compressed via a single-level 2-D DWT. Image hash is finally obtained by concatenating DWT coefficients in the LL sub-band. Many experiments with open image datasets are carries out and the results illustrate that our hashing is robust, discriminative and secure. Receiver operating characteristic (ROC) curve comparisons show that our hashing is better than some popular image hashing algorithms in classification performance between robustness and discrimination.