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Grand Challenge On Detecting Cheapfakes

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arxiv 2304.01328 v1 pith:CD7U5ZKD submitted 2023-04-03 cs.CV cs.CL

Duc-Tien Dang-Nguyen , Sohail Ahmed Khan , Cise Midoglu , Michael Riegler , P{aa}l Halvorsen , Minh-Son Dao This is my paper · ORCID
classification cs.CVcs.CL
keywords mediaimagechallengecaptionscheapfakecheapfakescontextdetect
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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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  1. RAMA: Retrieval-Augmented Multi-Agent Framework for Misinformation Detection in Multimodal Fact-Checking

    cs.CL 2025-07 reject novelty 4.0 of 10

    RAMA, a retrieval-augmented multi-agent detector, reports 0.910 accuracy and F1 on the ICMR 2024 public test set, placing it behind the top published method on the same benchmark.

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