人体生理学和面部生物力学对构建稳健的深度伪造检测器的作用:全面调查与分析

IF 13.3 1区 计算机科学 Q1 COMPUTER SCIENCE, INFORMATION SYSTEMS
Rajat Chakraborty, Ruchira Naskar
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引用次数: 0

摘要

基于人工智能的多媒体内容生成已经实现了超逼真,深深影响着人类的感知和信任。自 2017 年底左右出现以来,deepfake 技术因其多样化的应用而迅速走红,引起了人们对其恶意和不道德使用的极大关注。尽管近年来法医研究人员已经开发出许多深度伪造检测器,但仍迫切需要能够克服人口、社会和文化障碍的强大检测器来识别深度伪造。为了识别人的真伪,区分人与合成实体,文献迫切需要引入能够抵御各种形式的人口和社会偏见的深度伪造检测器。(近来已有多项研究证明合成媒体检测器存在社会和人口偏见)。在本文中,我们将人体生理信号作为鲁棒性深度防伪检测器的基础进行研究,并对依赖于人体生理信号和面部生物力学的深度防伪检测研究的最新进展进行了调查。我们对这些技术进行了深入分析,以了解人体生理在深度防伪检测中的贡献。因此,与其他现有检测器相比,我们通过利用生理信号固有的鲁棒性,了解了基于人体生理的深度防伪检测器的性能如何。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Role of human physiology and facial biomechanics towards building robust deepfake detectors: A comprehensive survey and analysis

AI based multimedia content generation, already having achieved hyper-realism, deeply influences human perception and trust. Since emerging around late 2017, deepfake technology has rapidly gained popularity due to its diverse applications, raising significant concerns regarding its malicious and unethical use. Although many deepfake detectors have been developed by forensic researchers in recent years, there is an urgent need for robust detectors that can overcome demographic, social, and cultural barriers in identifying deepfakes. To identify a human as a human, to distinguish a person from a synthetic entity, the literature faces compelling necessity to introduce deepfake detectors that can withstand all forms of demographic and social biases. (Multiple researches have been conducted in recent times to prove the existence of social and demographic biases in synthetic media detectors.) In this article, we examine human physiological signals as the foundation for robust deepfake detectors, and present a survey of recent developments in deepfake detection research that relies on human physiological signals and facial biomechanics. We perform in-depth analysis of the techniques to understand the contribution of human physiology in deepfake detection. Hence, we comprehend how human physiology based deepfake detectors fare by exploiting the inherent robustness of physiological signals, in contrast to other existing detectors.

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来源期刊
Computer Science Review
Computer Science Review Computer Science-General Computer Science
CiteScore
32.70
自引率
0.00%
发文量
26
审稿时长
51 days
期刊介绍: Computer Science Review, a publication dedicated to research surveys and expository overviews of open problems in computer science, targets a broad audience within the field seeking comprehensive insights into the latest developments. The journal welcomes articles from various fields as long as their content impacts the advancement of computer science. In particular, articles that review the application of well-known Computer Science methods to other areas are in scope only if these articles advance the fundamental understanding of those methods.
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