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COSMOS on steroids: a Cheap detector for cheapfakes

Title	COSMOS on steroids: a Cheap detector for cheapfakes
Author	Akgül, T., Civelek, Tuğçe Erkılıç, Uğur, Deniz, Beğen, Ali Cengiz
Publication Date:	2021
Publication Place	- The ACM Digital Library
Subject	Cheapfakes, RNN, BERT, SBERT, IoU, Differential sensing, Fake
Type	Document
Language	English
Digital	Yes
Manuscript	No
Library:	Özyeğin University
Library Asset ID	978-1-4503-8434-6
Record ID	3dbcb913-092c-4035-a8db-72394f751e4c
Library Location	Computer Science
Date	2021
Sample Text	The growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors developed a self-supervised training strategy that detected whether different captions for a given image were out-of-context, meaning that even though pointing to the same object(s) in the image, the captions implied different meanings. In this paper, we propose four methods to improve the detection accuracy of COSMOS. These methods range from differential sensing and fake-or-fact checking that detect contradicting or fake captions to object-caption matching and threshold adjustment that modify the baseline algorithm for improved accuracy.

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Özyeğin University

COSMOS on steroids: a Cheap detector for cheapfakes

Author Akgül, T., Civelek, Tuğçe Erkılıç, Uğur, Deniz, Beğen, Ali Cengiz

Publication Date 2021

Publication Place - The ACM Digital Library

Subject Cheapfakes, RNN, BERT, SBERT, IoU, Differential sensing, Fake

Type Document

Language English

Digital Yes

Manuscript No

Library Özyeğin University

Library Asset ID 978-1-4503-8434-6

Record ID 3dbcb913-092c-4035-a8db-72394f751e4c

Library Location Computer Science

Date 2021

Sample Text The growing prevalence of visual disinformation has become an important problem to solve nowadays. Cheapfake is a new term used for the altered media generated by non-AI techniques. In their recent COSMOS work, the authors developed a self-supervised training strategy that detected whether different captions for a given image were out-of-context, meaning that even though pointing to the same object(s) in the image, the captions implied different meanings. In this paper, we propose four methods to improve the detection accuracy of COSMOS. These methods range from differential sensing and fake-or-fact checking that detect contradicting or fake captions to object-caption matching and threshold adjustment that modify the baseline algorithm for improved accuracy.