An Investigation of the Effectiveness of Deepfake Models and Tools

IF 3.3 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Journal of Sensor and Actuator Networks Pub Date : 2023-08-04 DOI:10.3390/jsan12040061

Md. Saddam Hossain Mukta, Jubaer Ahmad, Mohaimenul Azam Khan Raiaan, Salekul Islam, Sami Azam, Mohammed Eunus Ali, Mirjam Jonkman

{"title":"An Investigation of the Effectiveness of Deepfake Models and Tools","authors":"Md. Saddam Hossain Mukta, Jubaer Ahmad, Mohaimenul Azam Khan Raiaan, Salekul Islam, Sami Azam, Mohammed Eunus Ali, Mirjam Jonkman","doi":"10.3390/jsan12040061","DOIUrl":null,"url":null,"abstract":"With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.","PeriodicalId":37584,"journal":{"name":"Journal of Sensor and Actuator Networks","volume":"25 1","pages":"0"},"PeriodicalIF":3.3000,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Sensor and Actuator Networks","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/jsan12040061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}

引用次数: 1

Abstract

With the development of computer vision and deep learning technologies, rapidly expanding approaches have been introduced that allow anyone to create videos and pictures that are both phony and incredibly lifelike. The term deepfake methodology is used to describe such technologies. Face alteration can be performed both in videos and pictures with extreme realism using deepfake innovation. Deepfake recordings, the majority of them targeting politicians or celebrity personalities, have been widely disseminated online. On the other hand, different strategies have been outlined in the research to combat the issues brought up by deepfake. In this paper, we carry out a review by analyzing and comparing (1) the notable research contributions in the field of deepfake models and (2) widely used deepfake tools. We have also built two separate taxonomies for deepfake models and tools. These models and tools are also compared in terms of underlying algorithms, datasets they have used and their accuracy. A number of challenges and open issues have also been identified.

查看原文本刊更多论文

深度造假模型和工具的有效性研究

随着计算机视觉和深度学习技术的发展，迅速扩展的方法已经被引入，允许任何人创建既虚假又令人难以置信的逼真的视频和图片。术语deepfake方法论用于描述此类技术。使用deepfake创新技术，可以在视频和图片中进行面部改变，具有极端的真实感。深度造假录音已在网上广泛传播，其中大多数是针对政客或名人的。另一方面，研究中概述了不同的策略来应对deepfake带来的问题。在本文中，我们通过分析和比较(1)在深度伪造模型领域的显著研究贡献和(2)广泛使用的深度伪造工具来进行综述。我们还为deepfake模型和工具构建了两个独立的分类法。这些模型和工具还会在基础算法、使用的数据集及其准确性方面进行比较。还确定了一些挑战和悬而未决的问题。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

Journal of Sensor and Actuator Networks Physics and Astronomy-Instrumentation

CiteScore

7.90

自引率

2.90%

发文量

审稿时长

11 weeks

期刊介绍： Journal of Sensor and Actuator Networks (ISSN 2224-2708) is an international open access journal on the science and technology of sensor and actuator networks. It publishes regular research papers, reviews (including comprehensive reviews on complete sensor and actuator networks), and short communications. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.