Grand Challenge On Detecting Cheapfakes
Not on the map. Why?
Abstract (arXiv)
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.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 6 of its 15 references that are in the map. First posted on arXiv 3 Apr 2023.
Builds on
6 of 15 references.
References · 6
- 2019FaceForensics++: Learning to Detect Manipulated Facial Imagescited by 1,095
- 2018Fighting Fake News: Image Splice Detection via Learned Self-Consistencycited by 136
- 2021NewsCLIPpings: Automatic Generation of Out-of-Context Multimodal Mediacited by 106
- 2019Detecting Photoshopped Faces by Scripting Photoshopcited by 55
- 2021COSMOS: Catching Out-of-Context Misinformation with Self-Supervised Learningcited by 26
- 2021MMSys'21 Grand Challenge on Detecting Cheapfakescited by 6
Built on by
Citing papers per year: 2023: 1.
1 paper.
- 2023Cheap-fake Detection with LLM using Prompt Engineeringcited by 15
- 2023Cheap-fake Detection with LLM using Prompt Engineering5 Jun 2023