Sensor Pattern Noise Matching Based on Reliability Map for Source Camera Identification

Source camera identification using the residual noise pattern left by the sensor, or Sensor Pattern Noise, has received much attention by the digital image forensics community in recent years. One notable issue in this regard is that high-frequency components of an image (textures, edges) can be easily mistaken as being part of the SPN itself, due to the procedure used to extract SPN, which is based on adaptive low-pass filtering. In this paper, a method to cope with this problem is presented, which estimates a SPN reliability map associating a degree of reliability to each pixel, based on the amount of high-frequency content in its neighbourhood. The reliability map is then used to weight SPN pixels during matching. The technique is tested using a data set of images coming from 27 different cameras; results show a notable improvement with respect to standard, non-weighted matching.

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