Forensic camera device identification addresses the scenario, where an investigator has two pieces of evidence: a digital image from an unknown camera involved in a crime, such as child pornography, and a person of interest’s (POI’s) camera. The investigator wants to determine whether the image was taken by the POI’s camera. Small manufacturing imperfections in the photodiode cause slight variations among pixels in the camera sensor array. These spatial variations, called photo-response non-uniformity (PRNU), provide an identifying characteristic, or fingerprint, of the camera. Most work in camera device identification leverages the PRNU of the questioned image and the POI’s camera to make a yes-or-no decision. As in other areas of forensics, there is a need to introduce statistical and probabilistic methods that quantify the strength of evidence in favor of the decision. Score-based likelihood ratios (SLRs) have been proposed in the forensics community to do just that. Several types of SLRs have been studied individually for camera device identification. We introduce a framework for calculating and comparing the performance of three types of SLRs — source-anchored, trace-anchored, and general match. We employ PRNU estimates as camera fingerprints and use correlation distance as a similarity score. Three types of SLRs are calculated for 48 camera devices from four image databases: ALASKA; BOSSbase; Dresden; and StegoAppDB. Experiments show that the trace-anchored SLRs perform the best of these three SLR types on the dataset and the general match SLRs perform the worst.