论文标题

ACM多媒体大挑战赛检测廉价餐厅

ACM Multimedia Grand Challenge on Detecting Cheapfakes

论文作者

Aneja, Shivangi, Midoglu, Cise, Dang-Nguyen, Duc-Tien, Khan, Sohail Ahmed, Riegler, Michael, Halvorsen, Pål, Bregler, Chris, Adsumilli, Balu

论文摘要

Cheapfake是一个最近创造的术语,涵盖了多媒体内容的非AI(``便宜')操纵。众所周知,廉价餐厅比深果更普遍。可以使用用于图像/视频操作的编辑软件,甚至不使用任何软件来创建廉价媒体,通过简单地通过与误导性的索赔共享媒体来更改图像/视频的上下文。上下文的这种变化称为对媒体的滥用(OOC)。与假媒体相比,OOC媒体难以检测得多,因为图像和视频没有被篡改。在这一挑战中,我们专注于检测OOC图像,更具体地说是滥用新闻项目中图像标题的真实照片。这一挑战的目的是开发和基准测试模型,该模型可用于根据最近编译的COSMOS数据集来检测给定样品(新闻图像和相关字幕)是否为OOC。

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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