Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.

中文翻译：

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具有低秩表示和环形分区的鲁棒图像散列

在过去的几年中，图像散列吸引了多媒体安全社区的广泛关注。它已成功用于社交事件检测，图像认证，复制检测，图像质量评估等。本文提出了一种具有低秩表示（LRR）和环分区的新颖图像哈希。所提出的哈希通过频谱残差模型找到显着性图，并利用它来构建预处理图像的视觉表示。接下来，提出的哈希计算通过LRR进行的视觉表示的低秩恢复，并通过环划分从低秩恢复中提取旋转不变哈希。哈希相似性最终由规范。进行了广泛的实验以验证所提出的散列的有效性。结果表明，所提出的散列可以在鲁棒性和辨别力之间达到良好的平衡，并且在接收器工作特性曲线下的面积方面优于某些最新的散列算法。