Journal article
Exposing Fake Images With Forensic Similarity Graphs
IEEE journal of selected topics in signal processing, v 14(5), pp 1049-1064
Aug 2020
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
In this paper, we propose new image forgery detection and localization algorithms by recasting these problems as graph-based community detection problems. To do this, we introduce a novel graph-based representation of an image, which we call the Forensic Similarity Graph, that captures key forensic relationships among regions in the image. In this representation, small image patches are represented by graph vertices with edges assigned according to the forensic similarity between patches. Localized tampering introduces unique structure into this graph, which aligns with a concept called "community structure" in graph-theory literature. In the Forensic Similarity Graph, communities correspond to the tampered and unaltered regions in the image. As a result, forgery detection is performed by identifying whether multiple communities exist, and forgery localization is performed by partitioning these communities. We present two community detection techniques, adapted from literature, to detect and localize image forgeries. We experimentally show that our proposed community detection methods outperform existing state-of-the-art forgery detection and localization methods, which do not capture such community structure.
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Details
- Title
- Exposing Fake Images With Forensic Similarity Graphs
- Creators
- Owen Mayer - Drexel UniversityMatthew C Stamm - Drexel University
- Publication Details
- IEEE journal of selected topics in signal processing, v 14(5), pp 1049-1064
- Publisher
- IEEE
- Grant note
- 1553610 / National Science Foundation (10.13039/100000001)
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000564205000012
- Scopus ID
- 2-s2.0-85090460550
- Other Identifier
- 991019167787804721
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- Web of Science research areas
- Engineering, Electrical & Electronic