Open-Set Source Camera Device Identification of Digital Images Using Deep Learning

Source camera identification plays an important role in forensics investigations on images. It is a forensic problem of linking an image in question to the camera used to capture it. Several source identification techniques have been developed in the literature since this may be a facilitating tool that help to trace back the images to the camera device held by the accused in various forensic applications. However, one of the key disadvantages is that the existing techniques fail if the image in question was taken by a new camera that is not used in the training process. Under a real-world forensic scenario, it is not possible to presume that each image being analyzed comes from one of the cameras used to train the source identification system. To address this issue, we propose a data-driven system based on convolutional neural network to identify the source camera device in an open-set scenario. The experimental results on various sets of cameras show that it is possible to leverage the data-driven model as the feature extractor paired with an open-set classifier to trace back the images to the open-set cameras. The results show that the proposed system outperforms the state-of-the-art techniques in identifying the exact device that are never seen before with considerably high accuracy and is resilient to unknown post-processing applied by the social network platforms. Moreover, the experimental results demonstrate the good generalization capability of the proposed system in extracting the source information, making it more suitable for open-set scenarios.

Automated summary

Source camera identification is a crucial task in image forensics, linking an image to the camera used to capture it. Existing techniques fail if the image was taken by a new camera not included in the training process. To address this issue, we propose a data-driven system based on convolutional neural networks that can identify the source camera in an open-set scenario. Experimental results demonstrate the system's high accuracy in identifying previously unseen devices and its resilience to unknown post-processing applied by social networks.