{"title":"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","authors":"Rajat Chakraborty, Ruchira Naskar","doi":"10.1016/j.cosrev.2024.100677","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":null,"pages":null},"PeriodicalIF":13.3000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Role of human physiology and facial biomechanics towards building robust deepfake detectors: A comprehensive survey and analysis\",\"authors\":\"Rajat Chakraborty, Ruchira Naskar\",\"doi\":\"10.1016/j.cosrev.2024.100677\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":48633,\"journal\":{\"name\":\"Computer Science Review\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":13.3000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Science Review\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1574013724000613\",\"RegionNum\":1,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Science Review","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1574013724000613","RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
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.
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.