新出现的深度假冒威胁:如何识别和预防

Murooj Aamer Taha, Wijdan Mahmood Khudhair, Ahmed Mahmood Khudhur, O. A. Mahmood, Y. Hammadi, Riyam Shihab Ahmed Al-husseinawi, Ahmed Aziz
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引用次数: 0

摘要

尽管自从有媒体以来,对视觉和听觉媒体的操纵就一直存在,但相对较近的深度伪造的引入标志着虚假信息创造的一个转折点。深度造假是一种自动化的方法,可以创造出越来越难以被人类观察者发现的虚假信息。人工智能和深度学习的最新技术进步使这些程序成为可能。深度学习是一种强大的方法,目前正在各种行业中实施,包括自然语言处理、计算机视觉、图像处理和机器视觉。深度伪造是利用深度学习算法合成和修改一个人的照片、视频或声音,使人类无法分辨真假的一种技术。在这篇文章中,我们提出了一种可行的深度伪造的描述,以及隐藏在它们下面的技术大纲。为了帮助人们思考深度造假的未来,我们概述了深度造假的好处以及潜在的威胁。本研究提出了一个完整的分析深层假冒方法,并讨论了最有效的策略,以防止假冒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
EMERGING THREAT OF DEEP FAKE: HOW TO IDENTIFY AND PREVENT IT
Although manipulations of visual and aural media have been around for as long as there have been media, the relatively recent introduction of deep fakes has marked a turning point in the creation of fake information. Deep fakes are automated methods that allow the creation of fake information that is becoming increasingly difficult for human observers to see. These procedures are made possible by the most recent technological advancements in artificial intelligence and deep learning. Deep Learning is a powerful method that is now being implemented in a variety of industries, including natural language processing, computer vision, image processing, and machine vision. Deep fakes are created by the use of deep learning algorithms in order to synthesize and modify photos, videos, or sounds of a person, to the point that human people are unable to discern the fake from the real one. In this article, we present a workable description of deep fakes as well as an outline of the technology that lies underneath them. In order to assist people in thinking about the future of deep fakes, we outline the benefits of the deepfake as well as the potential threats. This study presents a complete analysis of deep fake methods and discusses the most effective strategies for preventing counterfeiting.
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