Title
Aird: Adversarial Learning Framework For Image Repurposing Detection
Abstract
Image repurposing is a commonly used method for spreading misinformation on social media and online forums, which involves publishing untampered images with modified metadata to create rumors and further propaganda. While manual verification is possible, given vast amounts of verified knowledge available on the internet, the increasing prevalence and ease of this form of semantic manipulation call for the development of robust automatic ways of assessing the semantic integrity of multimedia data. In this paper, we present a novel method for image repurposing detection that is based on the real-world adversarial interplay between a bad actor who repurposes images with counterfeit metadata and a watchdog who verifies the semantic consistency between images and their accompanying metadata, where both players have access to a reference dataset of verified content, which they can use to achieve their goals. The proposed method exhibits state-of-the-art performance on location-identity, subject-identity and painting-artist verification, showing its efficacy across a diverse set of scenarios.
Year
DOI
Venue
2019
10.1109/CVPR.2019.01159
2019 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR 2019)
Field
DocType
Volume
Metadata,World Wide Web,Social media,Repurposing,Computer science,Misinformation,Artificial intelligence,Publishing,Counterfeit,Machine learning,The Internet,Adversarial system
Journal
abs/1903.00788
ISSN
Citations 
PageRank 
1063-6919
1
0.35
References 
Authors
14
5
Name
Order
Citations
PageRank
Ayush Jaiswal121.04
Yue Wu233131.69
Wael Abd-Almageed324824.52
Iacopo Masi434816.19
Premkumar Natarajan587479.46