Abstract
Existing forensic fingerprints mostly rely on robust statistical estimates, which usually hinder accurate image tampering detection at fine-grained level. To date, people still put a big question mark behind "what has been tampered?" In this paper, we try to answer this question from a counterfeiter's perspective, devil in the details, that image tampering is usually sparsely and delicately manipulated. Thanks to recently well-established rank-sparsity incoherence, we formulate the fine-grained tampering detection as a constrained minimization problem in order to discriminate the authentic areas (sharing similar feature behaviours) from the tampered areas (inconsistently and sparsely distributed) in a forensic feature space. Our formulation could incorporate with any applicable forensic features and, unlike existing methods, needs neither statistical analysis nor model factor estimation. Our experimental results show that the proposed method successfully locates various kinds of image tampering, including copy-move forgery, resampling and recompression, at fine-grained level. © 2013 IEEE.