Identifying Unique Flatbed Scanner Characteristics for Matching a Scanned Image to its Source

Scanner identification is the ability to discern the devices by which an image was scanned. In this paper, a new and robustness individual source scanner identification scheme is proposed. This scheme formulates a unique fingerprint for each scanner using traces of dust, dirt, scratches, and source imperfection pattern over scanner platen on scanned images. A single Support Vector machine (SVM) classifier is implemented and trained using correlation features of scanned images to classify different scanners brands and different models for the same scanner brand, and a 99.79% detection accuracy is obtained. In addition, the robustness of the used individual source scanner identification scheme on resized different resolutions is experimentally tested. The aging effect is also experimentally tested by re-applying the proposed algorithm on the scanned images after a continuous usage of the scanners under test for certain long periods. The experimental results using the proposed classifier for different scanner brands and different models for the same scanner brand approved the validity, efficiency, and robustness of the proposed scheme to match the scanned image to its unique source.