Blind Image Clustering Based on PRNU With Reduced Computational Complexity
Paper ID: 3432
Sidra Naveed Mufti
The blind clustering of images without any prior information is always challenging for forensics experts. The problem gets severe in the case of a large dataset where the computational cost increases significantly. The proposed technique is one of the potential solutions for image clustering, especially for large-scale clustering. The technique clusters images based on camera fingerprints using two-stage clustering and additional processes. The algorithm is tested on the sub-datasets of the well-known Dresden dataset. The performance of the clustering framework is either better or comparable with the state-of-the-art clustering algorithms. The algorithm generates good-quality clusters with a significantly lower computational complexity.