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ACM Multimedia Grand Challenge on Detecting Cheapfakes

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arxiv 2207.14534 v1 pith:H3HCMUOU submitted 2022-07-29 cs.MM

Shivangi Aneja , Cise Midoglu , Duc-Tien Dang-Nguyen , Sohail Ahmed Khan , Michael Riegler , P{aa}l Halvorsen , Chris Bregler , Balu Adsumilli This is my paper · ORCID
classification cs.MM
keywords mediaimagechallengecaptionscheapfakecheapfakescontextdetect
verification ladder T0 review T1 audit T2 compute T3 formal
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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. Multimedia Verification Through Multi-Agent Deep Research Multimodal Large Language Models

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    A six-stage multi-agent MLLM pipeline with reverse image search, metadata analysis, and fact-checking tools is demonstrated on a single Ukraine missile-strike video, with no quantitative evaluation.

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