Anomaly Detection in Images and Videos Using Photo-Response Non-Uniformity

Abstract: When photos and videos are increasingly used as evidence material, it is of importance to know if these materials can be used as evidence material or if the risk of them being forged is impending. This thesis investigates methods for detecting anomalous regions in images and videos using photo-response non-uniformity -- a fixed-pattern sensor noise that can be estimated from photos or videos. For photos, experiments were performed on a method that assumes other photos from the same camera are available. For videos, experiments were performed on a method further developed from the still image method, with other videos from the same camera being available. The last experiments were performed on videos when only the video that was about to be investigated was available. The experiments on the still image method were performed on images with three different kinds of forged regions: a forged region from somewhere else in the same photo, a forged region from a photo taken by another camera, and a forged region from the same sensor position in a photo taken by the same camera. The method should not be able to detect the third kind of forged region. Experiments performed on videos had a forged region in several adjacent frames in the video. The forged region was from another video, and it moved and changed shape between the frames. The methods mainly consist of a classification process and some post-processing. In the classification process, features were extracted from images/videos and used in a random forest classifier. The results are presented in precision, recall, F1 score and false positive rate. The quality of the still images was generally better than the videos, which also resulted in better results. For the cameras used in the experiments, it seemed easier to estimate a good PRNU pattern from photos and videos from older cameras. Probably due to sensor differences and extra processing in newer camera models. How the images and videos are compressed also affects the possibility to estimate a good PRNU pattern, because important information may then be lost.