Abstract

We present SCMI30-IITRPR, a dataset for smartphone camera model identification (CMI) performance assessment comprising 9937 diverse scene images collected using 30 different camera models. Importantly, to allow assessment of CMI performance under different application settings where either similar or random content images may be available across the camera models, SCMI30-IITRPR provides images grouped in two sets: one set with similar image content and another with random image content. SCMI30-IITRPR therefore overcomes a key limitation of prior datasets that provided either images with random or similar content but not both. Additionally, SCMI30-IITRPR also allows researchers to test the robustness of CMI techniques under test conditions mismatched with the training and to explore alternative data selection approaches for more robust training. We present benchmarks of five CMI methods on the SCMI30-IITRPR dataset highlighting the facts that significant performance variations can be encountered under a mismatch between training and testing scenarios and that training datasets that merge images with similar and random content offer the most robustness.