Paper Info
Title
Deep Multimodal Image-Repurposing Detection.
Abstract
Nefarious actors on social media and other platforms often spread rumors and falsehoods through images whose metadata (e.g., captions) have been modified to provide visual substantiation of the rumor/falsehood. This type of modification is referred to as image repurposing, in which often an unmanipulated image is published along with incorrect or manipulated metadata to serve the actor's ulterior motives. We present the Multimodal Entity Image Repurposing (MEIR) dataset, a substantially challenging dataset over that which has been previously available to support research into image repurposing detection. The new dataset includes location, person, and organization manipulations on real-world data sourced from Flickr. We also present a novel, end-to-end, deep multimodal learning model for assessing the integrity of an image by combining information extracted from the image with related information from a knowledge base. The proposed method is compared against state-of-the-art techniques on existing datasets as well as MEIR, where it outperforms existing methods across the board, with AUC improvement up to 0.23.
Year
DOI
Venue
2018
10.1145/3240508.3240707
MM '18: ACM Multimedia Conference Seoul Republic of Korea October, 2018
Keywords
DocType
Volume
Rumor detection, fake news, computer vision, deep learning, multi-task learning
Conference
abs/1808.06686
ISBN
Citations 
PageRank 
978-1-4503-5665-7
2
0.37
References 
Authors
13
4
Name
Order
Citations
PageRank
Ekraam Sabir1152.42
Wael Abd-Almageed224824.52
Yue Wu333131.69
Premkumar Natarajan487479.46