Sensor pattern noise and image similarity for picture-to-identity linking

Picture sharing through social networks has become a prominent phenomenon, producing a large amount of data that law enforcers may be entitled to use, under the proper legal framework, as a source of information for investigating a crime. In this work, the authors exploit digital camera `fingerprinting´ based on noise residuals (sensor pattern noise or SPN) to achieve a novel forensic task, named picture-to-identity linking. It consists of finding social network accounts that possibly belong to the author of a certain photo (e.g. showing illegal content). The rationale is that the author of the offending photo has likely used the same camera for taking other (legal) pictures, and posted them in a social network account. The authors extend a previous work on the topic by coupling SPN with visual image similarity, a useful cue when pictures have been taken in the same environment (e.g. a room). The authors also improve the framework by allowing for multiple-image queries, and thoroughly evaluate the performance on two corpora of images from social network accounts, including the impact of image modifications. Reported results show a robust improvement with respect to the previous work, and prove the usefulness of picture-to-identity as an aid for digital forensic investigations.