Image hashing is a useful technology of many multimedia systems, such as image retrieval, image copy detection, multimedia forensics and image authentication. Most of the existing hashing algorithms do not reach a good classification between robustness and discrimination and some hashing algorithms based on dimensionality reduction have high computational cost. To solve these problems, we propose a robust and fast image hashing based on two-dimensional (2D) principal component analysis (PCA) and saliency map. The saliency map determined by a visual attention model called LC (luminance contrast) method can ensure good robustness of our hashing. Since 2D PCA is a fast and efficient technique of dimensionality reduction, the use of 2D PCA helps to learn a compact and discriminative code and provide a fast speed of our hashing. Extensive experiments are carried out to validate the performances of our hashing. Classification comparison shows that our hashing is better than some state-of-the-art algorithms. Computational time comparison illustrates that our hashing outperforms some compared algorithms based on dimensionality reduction.

中文翻译：

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使用二维 PCA 进行稳健且快速的图像散列

图像散列是许多多媒体系统的有用技术，例如图像检索、图像复制检测、多媒体取证和图像认证。现有的散列算法大多没有在鲁棒性和判别性之间达到很好的分类，一些基于降维的散列算法计算成本很高。为了解决这些问题，我们提出了一种基于二维 (2D) 主成分分析 (PCA) 和显着图的强大且快速的图像散列。由称为 LC（亮度对比）方法的视觉注意模型确定的显着图可以确保我们的哈希具有良好的鲁棒性。由于 2D PCA 是一种快速有效的降维技术，因此使用 2D PCA 有助于学习紧凑且有辨别力的代码并提供快速的散列速度。进行了大量实验以验证我们的散列性能。分类比较表明我们的散列比一些最先进的算法更好。计算时间比较表明，我们的散列优于一些基于降维的比较算法。