Evaluating Deep Learning and Traditional Approaches Used in Source Camera Identification

Identifying the device that captured a digital image is a central task in image forensics, made more urgent by pervasive smartphone photography and the rise of AI-generated imagery. This paper compares three source camera identification (SCI) approaches - Photo Response Non-Uniformity (PRNU) analysis, JPEG compression artifact analysis, and a convolutional neural network (CNN) - on images from four smartphone devices (Xiaomi Redmi Note 10S, Samsung Galaxy S24, iPhone 13 Pro Max, and iPhone 17 Pro). For each method, discriminative features are extracted (DCT coefficient statistics for JPEG artifacts, denoised noise residuals for PRNU, and learned convolutional features for the CNN) and classified with an SVM (JPEG, PRNU) or trained end-to-end (CNN), using an identical fixed, stratified 70/30 train-test split for direct comparability. The DCT/JPEG-artifact-inspired classifier achieves the highest accuracy (0.90), followed by the PRNU-inspired method (0.71), while the CNN baseline reaches only 0.29. Under this experimental configuration, the DCT/statistical and PRNU-inspired approaches outperform the evaluated CNN baseline. The results motivate evaluation on larger, format-balanced datasets and investigation of hybrid or learned approaches under more robust validation protocols.