arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 2302.10280cs.CV

Deepfake Detection Analyzing Hybrid Dataset Utilizing CNN and SVM

University of Wisconsin-Eau Claire(威斯康星大学欧克莱尔分校);Minnesota State University, Mankato(明尼苏达州立大学曼卡托分校)

Jacob mallet, Laura Pryor, Rushit Dave, Mounika Vanamala

英文摘要:

Social media is currently being used by many individuals online as a major source of information. However, not all information shared online is true, even photos and videos can be doctored. Deepfakes have recently risen with the rise of technological advancement and have allowed nefarious online users to replace one face with a computer generated face of anyone they would like, including important political and cultural figures. Deepfakes are now a tool to be able to spread mass misinformation. There is now an immense need to create models that are able to detect deepfakes and keep them from being spread as seemingly real images or videos. In this paper, we propose a new deepfake detection schema using two popular machine learning algorithms.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

↑