Clustering Image Noise Patterns By Embedding And Visualization For Unknown Source Camera Detection

Document Type : Primary Research paper


Department of Computer Science Dravidan University Kuppam – 517426 A.P INDIA


We consider the problem of clustering a large set of images based on similarities of their noise patterns. Such clustering is necessary in forensic cases in which detection of common source of images is required, when the cameras are not physically available. We propose a novel method for clustering combining low dimensional embedding, visualization, and classical clustering of the dataset based on the similarity scores. We evaluate our method on the Dresden images database showing that the methodology is highly effective.