A PNU-based technique to detect forged regions in digital images

In this paper we propose a non-blind passive technique for image forgery detection. Our technique is a variant of a method presented in [8] and it is based on the analysis of the Sensor Pattern Noise (SPN). Its main features are the ability to detect small forged regions and to run in an automatic way. Our technique works by extracting the SPN from the image under scrutiny and, then, by correlating it with the reference SPN of a target camera. The two noises are partitioned into non-overlapping blocks before evaluating their correlation. Then, a set of operators is applied on the resulting Correlations Map to highlight forged regions and remove noise spikes. The result is processed using a multi-level segmentation algorithm to determine which blocks should be considered forged. We analyzed the performance of our technique by using a dataset of 4, 000 images