Principal Investigator: Edward Delp
With digital images replacing their analog counterparts in more and more venues, and increasing functionality of image editing softwares, reliable forensic tools that help establish the origin, authenticity, and the chain of custody of digital images are becoming indispensable. These tools can prove to be vital whenever questions of digital image integrity are raised. There are various levels at which the image source identification problem can be addressed. One may want to find the particular device (digital camera or scanner), which generated the image, or one might be interested in knowing only the make and model of the device. We have developed reliable methods for image source classification and source scanner identification. Dividing images into camera generated or scanned images, is an essential step before applying the source identification algorithms specific to one class or the other. Source scanner identification may be of crucial importance in situations such as e-commerce between banks using scanned checks. Two key steps of our image forensics algorithms are extraction of appropriate features from the images and training of classifiers such as SVM. Our feature vector based methods allow close to 90% classification accuracy on a set of ten scanners and ten digital cameras. Future work in this direction will allow reliable image source identification from images undergone post-processing such as cropping, compression, sub-sampling, rotation and different kinds of filtering operations.
Other PIs: Jan P. Allebach (Purdue University) George Chiu (Purdue University)
N. Khanna, A. K. Mikkilineni, and E. J. Delp, " Scanner Identification Using Feature-Based Processing and Analysis, " IEEE Transactions on Information Forensics and Security, vol.4, no.1, pp.123-139, March 2009.
Keywords: image, provenance, source identification