COSMOS on steroids: a Cheap detector for cheapfakes

Name: COSMOS on steroids: a Cheap detector for cheapfakes
Author: Akgül, T., Civelek, Tuğçe Erkılıç, Uğur, Deniz, Beğen, Ali Cengiz

İsim	COSMOS on steroids: a Cheap detector for cheapfakes
Yazar	Akgül, T., Civelek, Tuğçe Erkılıç, Uğur, Deniz, Beğen, Ali Cengiz
Basım Tarihi:	2021
Basım Yeri	- The ACM Digital Library
Konu	Cheapfakes, RNN, BERT, SBERT, IoU, Differential sensing, Fake
Tür	Belge
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	978-1-4503-8434-6
Kayıt Numarası	3dbcb913-092c-4035-a8db-72394f751e4c
Lokasyon	Computer Science
Tarih	2021
Örnek Metin	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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COSMOS on steroids: a Cheap detector for cheapfakes

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

Basım Tarihi 2021

Basım Yeri - The ACM Digital Library

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

Tür Belge

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 978-1-4503-8434-6

Kayıt Numarası 3dbcb913-092c-4035-a8db-72394f751e4c

Lokasyon Computer Science

Tarih 2021

Örnek Metin 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.