Blind Image Clustering for Camera Source Identification via Row-Sparsity Optimization
Blind Image Clustering for Camera Source Identification via Row-Sparsity Optimization
复制标题
通过行稀疏优化进行相机源识别的盲图像聚类
DOI：
10.1109/tmm.2020.3013449
复制
发表时间：
2021
影响因子：
7.3
通讯作者：
Heng Huang
中科院分区：
文献类型：
--
作者：
Xiang Jiang;Shikui Wei;Ting Liu;Ruizhen Zhao;Yao Zhao;Heng Huang
鉴于一组图像具有提供未知图像的相机数量，如何盲目地识别图像的来源一直是数字取证中的关键问题。尽管最先进的方法取得了令人印象深刻的结果
Given a set of images with the number of cameras providing those images unknown, how to blindly identify the sources of the images has been a critical problem in digital forensics. Although state-of-the-art methods have achieved impressive results, they have failed at suppressing outliers. When they deal with a noisy dataset, the performance is significantly degraded. To address this issue, we propose an optimization approach with sparsity constraints to simultaneously handle the how-many subproblem (i.e., the number of cameras) and the which-from-which subproblem (i.e., the image–camera relationship). In our approach, we first formulate the blind camera source clustering as a row-sparsity optimization problem, in which the representation errors are minimized and the outliers caused by noisy features are suppressed. Then, a new two-stage refinement method based on inter- and the intra-class differences is proposed to achieve a more accurate estimation of the number of cameras. Because strong sparsity constraints have been adopted and the interactive relationship among data points can be fully explored to distinguish the images originated from different cameras, the proposed method can effectively handle outliers. Extensive experiments on the popular Dresden dataset show that the proposed method outperforms existing methods in both identification accuracy and efficiency.