2017/05/01 by Joel Brogan, Brogan, Joel, Paolo Bestagini +18
Biochemistry, Genetics and Molecular Biology · Computer Science · #Anomaly Detection Techniques and Applications #Cell Image Analysis Techniques #Computer Vision and Pattern Recognition (cs.CV) #Digital Media Forensic Detection #FOS: Computer and information sciences #Information Retrieval (cs.IR) #cs.CV #cs.IR
paper · pdf · doi:10.48550/arxiv.1705.00604
5 pages, 5 figures
arxiv created 2017/05/01 · openalex publication_date 2017/05/01 · arxiv updated 2019/04/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
As image tampering becomes ever more sophisticated and commonplace, the need for image forensics algorithms that can accurately and quickly detect forgeries grows. In this paper, we revisit the ideas of image querying and retrieval to provide clues to better localize forgeries. We propose a method to perform large-scale image forensics on the order of one million images using the help of an image search algorithm and database to gather contextual clues as to where tampering may have taken place. In this vein, we introduce five new strongly invariant image comparison methods and test their effectiveness under heavy noise, rotation, and color space changes. Lastly, we show the effectiveness of these methods compared to passive image forensics using Nimble [https://www.nist.gov/itl/iad/mig/nimble-challenge], a new, state-of-the-art dataset from the National Institute of Standards and Technology (NIST).