Camera Model Identification Using Convolutional Neural Networks
This addresses source camera identification for forensic applications, but it is incremental as it applies existing deep learning methods to a specific dataset.
The paper tackled the problem of identifying the camera model used to capture an image, achieving 98% accuracy on unseen data in a Kaggle challenge with 10 cameras.
Source camera identification is the process of determining which camera or model has been used to capture an image. In the recent years, there has been a rapid growth of research interest in the domain of forensics. In the current work, we describe our Deep Learning approach to the camera detection task of 10 cameras as a part of the Camera Model Identification Challenge hosted by Kaggle.com where our team finished 2nd out of 582 teams with the accuracy on the unseen data of 98%. We used aggressive data augmentations that allowed a model to stay robust against transformations. A number of experiments are carried out on datasets collected by organizers and scraped from the web.