Anti-Forensics of Median Filtered Images using Non- Linear Optimization Techniques
Seema Seema, Swati B. Gupta, Bijender Bansal, Dr. Deepak Goyal, Monika Goyal · Zenodo (CERN European Organization for Nuclear Research) · 2020
A number of forensic approaches have been developed to iden- tify the use of digital multimedia editing operations. In feedback, several anti-forensic operations have been designed to fool forensic algorithms. One action that has received huge attention is median filtering, since it can be used for image enhancement or anti-forensic purposes. As a conclusion, several median filtering detectors have been developed. In this paper, we propose an anti-forensic technique to disguise the use of median filtering. We do this by first state a model for an unaltered image's pixel difference distribution. We then modify a median filter image's pixel difference distri- bution using anti-forensic noise so that it no longer contains median filtering fingerprints. Through a series of analysis, we are able to show that our anti-forensic approach can fool existing median filtering detectors under realistic conditions.