Source Camera Identification (SCI) is vital in digital forensics, yet its most prominent approach, Sensor Pattern Noise (SPN), faces new challenges in the era of modern devices and vast media datasets. This paper introduces the Source Camera Target Model (SCTM) to classify SCI approaches and formally defines three core problem classes: Verification, Identification, and Exploration. For each, we outline key evaluation metrics tailored to practical use cases. Applying this framework, we critically assess recognized SCI methods and their alignment with contemporary needs. Our findings expose significant gaps in scalability, efficiency, and relevance to modern imaging pipelines, challenging the notion of SPN as a gold standard. Finally, we provide a roadmap for advancing SCI research to address these limitations and adapt to evolving technological landscapes.    «

Source Camera Identification (SCI) is vital in digital forensics, yet its most prominent approach, Sensor Pattern Noise (SPN), faces new challenges in the era of modern devices and vast media datasets. This paper introduces the Source Camera Target Model (SCTM) to classify SCI approaches and formally defines three core problem classes: Verification, Identification, and Exploration. For each, we outline key evaluation metrics tailored to practical use cases. Applying this framework, we critically...    »