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Robust image hashing with visual attention model and invariant moments
IET Image Processing ( IF 2.3 ) Pub Date : 2020-04-09 , DOI: 10.1049/iet-ipr.2019.1157
Zhenjun Tang 1 , Hanyun Zhang 1 , Chi‐Man Pun 2 , Mengzhu Yu 1 , Chunqiang Yu 1 , Xianquan Zhang 1
Affiliation  

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub-band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination.

中文翻译:

具有视觉注意力模型和不变矩的鲁棒图像散列

图像哈希是许多应用程序进行多媒体处理的有效技术,例如图像复制检测,图像身份验证和社交事件检测。在这项研究中,作者提出了一种具有视觉注意力模型和不变矩的新型图像哈希算法。一个重要的贡献是通过将称为Itti显着性模型的视觉注意力模型并入LL子带中来实现加权DWT(离散小波变换)表示。由于Itti显着性模型可以有效地提取反映关注区域的显着性图,因此可以实现所提出的哈希的感知鲁棒性。此外,由于不变矩具有鲁棒性和区分性,因此从加权DWT表示中提取具有不变矩的哈希构造可确保鲁棒性和区分性之间的良好分类性能。使用开放图像数据集进行了广泛的实验,以验证所提出的散列的性能。结果表明,所提出的散列是鲁棒的和可区分的。还与一些散列算法进行了性能比较,并且接收器的工作特性结果表明,在鲁棒性和区分度之间的分类性能方面,所提出的散列优于已比较的散列算法。
更新日期:2020-04-22
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