Towards a More Reliable and Reproducible Protocol of Source Camera Recognition.

Source digital camera recognition is an important branch of digital image forensics, which aims at authenticating cameras from the captured images. By analysing the noise artifacts left on the images, it is possible to recognize the label: brand, model and device of the camera (e.g. Nikon - NikonD70 - NikonD70 of Alice). Camera recognition is increasingly difficult as the label become more precise. In the specific case of source camera recognition based on deep learning, literature has widely addressed recognition of the camera model, while the recognition of the instance of the camera (i.e. device) is currently under-studied. Moreover, we have identified a lack of protocols for performance assessment: state-of-the-art methods are usually assessed on databases that have specific compositions, such as the Dresden Image database (74 cameras of 27 models). However, using only one database for evaluation does not reflect reality, where it may be necessary to analyse different sets of devices that are more or less difficult to classify. Also, for some scenarios, verification (1-to-1) is better suited to camera recognition than identification (1-to-N). Based on these elements, we propose a more reliable and reproducible protocol for verification of the source camera made of three different levels (basic, intermediate and advanced) of increasing difficulty, based on camera labels (brand, model and device). State-of-the-art methods are tested with the proposed protocol on the Dresden Image Database and on SOCRatES. The obtained results prove our assumptions, with a relative drop in performance, up to 49.08% between the basic and advanced difficulty levels. Our protocol is able to assess the robustness of methods for source camera recognition, as it tests whether they are really able to correctly classify cameras in realistic contexts.