Grand Challenge On Detecting Cheapfakes

Duc-Tien Dang-Nguyen, Sohail Ahmed Khan, Cise Midoglu, Michael Riegler, Pål Halvorsen +1 more
4/3/2023
cs.CVcs.CL

No official code found. Best implementation: detecting-cheapfakes/detecting-cheapfakes-code (4 stars, MIT, last commit 2022-09-09).

Abstract

Cheapfake is a recently coined term that encompasses non-AI ("cheap") manipulations of multimedia content. Cheapfakes are known to be more prevalent than deepfakes. Cheapfake media can be created using editing software for image/video manipulations, or even without using any software, by simply altering the context of an image/video by sharing the media alongside misleading claims. This alteration of context is referred to as out-of-context (OOC) misuse of media. OOC media is much harder to detect than fake media, since the images and videos are not tampered. In this challenge, we focus on detecting OOC images, and more specifically the misuse of real photographs with conflicting image captions in news items. The aim of this challenge is to develop and benchmark models that can be used to detect whether given samples (news image and associated captions) are OOC, based on the recently compiled COSMOS dataset.

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Code Implementations(1)

MIT
42Apr 22, 20224 years agoMIT

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Cite this paper

@article{dangnguyen2023grand,
  title  = {Grand Challenge On Detecting Cheapfakes},
  author = {Duc-Tien Dang-Nguyen and Sohail Ahmed Khan and Cise Midoglu and Michael Riegler and Pål Halvorsen and Minh-Son Dao},
  year   = {2023},
  eprint = {2304.01328},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2304.01328v1}
}

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