Correlation clustering for PRNU-based blind image source identification

Francesco Marra, Giovanni Poggi, Carlo Sansone, Luisa Verdoliva

Abstract: We propose a new algorithm for blind camera identification, based on the Photo-Response Non-Uniformity (PRNU) noise estimated by image residuals. Successful identification relies on the correct clustering of residuals coming from the same camera. We adopt a two-step strategy. First, residuals are efficiently grouped by correlation clustering, setting parameters so as to over-partition the data points and avoid any wrong associations. Then, basic clusters are progressively merged by an ad hoc refinement algorithm. Experiments on the Dresden database prove the effectiveness of the proposed method.