The IEEE Signal Processing Cup is an international competition that provides teams of undergraduate students an opportunity to use various signal-processing techniques to solve real-world problems. This year’s challenge was a forensic camera model identification challenge where teams were required to design a classifier system capable of determining the make and model of a camera used to capture a digital image. For my senior project, I acted as the tech lead for Union College’s first ever Signal Processing Cup Team, designing a functioning camera model identification system. The designed system uses a combination of image forensic and signal processing techniques to extract desired information from image data. This information is then used to train an ensemble-based machine learning classifier system that is used to construct the decision fusion that identifies the source camera of a respective image. Overall, we were able to construct a functioning camera identification system for the Signal Processing Cup competition that gave our team a final score of 65%, landing our team in the middle of the pack of all competitors. My oral presentation will discuss the design and implementation of our image classification system as well as a detailed analysis or our classification results and improvements for the overall system.