Irene Amerini, A. Anagnostopoulos, Luca Maiano, L. R. Celsi
{"title":"多媒体取证的深度学习","authors":"Irene Amerini, A. Anagnostopoulos, Luca Maiano, L. R. Celsi","doi":"10.1561/0600000096","DOIUrl":null,"url":null,"abstract":"In the last two decades, we have witnessed an immense increase in the use of multimedia content on the internet, for multiple applications ranging from the most innocuous to very critical ones. Naturally, this emergence has given rise to many types of threats posed when this content can be manipulated/used for malicious purposes. For example, fake media can be used to drive personal opinions, ruining the image of a public figure, or for criminal activities such as terrorist propaganda and cyberbullying. The research community has of course moved to counter attack these threats by designing manipulation-detection systems based on a variety of techniques, such as signal processing, statistics, and machine learning. This research and practice activity has given rise to the field of multimedia forensics. The success of deep learning in the last decade has led to its use in multimedia forensics as well. In this survey, we look at the latest trends and deep-learning-based techniques introduced to solve three main questions investigated in the field of multimedia forensics. We begin by examining the manipulations of images and videos produced with editing tools, reporting the deep-learning approaches adopted to Irene Amerini, Aris Anagnostopoulos, Luca Maiano and Lorenzo Ricciardi Celsi (2021), “Deep Learning for Multimedia Forensics”, Foundations and Trends® in Computer Graphics and Vision: Vol. 12, No. 4, pp 309–457. DOI: 10.1561/0600000096. Full text available at: http://dx.doi.org/10.1561/0600000096","PeriodicalId":45662,"journal":{"name":"Foundations and Trends in Computer Graphics and Vision","volume":null,"pages":null},"PeriodicalIF":3.8000,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Deep Learning for Multimedia Forensics\",\"authors\":\"Irene Amerini, A. Anagnostopoulos, Luca Maiano, L. R. Celsi\",\"doi\":\"10.1561/0600000096\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the last two decades, we have witnessed an immense increase in the use of multimedia content on the internet, for multiple applications ranging from the most innocuous to very critical ones. Naturally, this emergence has given rise to many types of threats posed when this content can be manipulated/used for malicious purposes. For example, fake media can be used to drive personal opinions, ruining the image of a public figure, or for criminal activities such as terrorist propaganda and cyberbullying. The research community has of course moved to counter attack these threats by designing manipulation-detection systems based on a variety of techniques, such as signal processing, statistics, and machine learning. This research and practice activity has given rise to the field of multimedia forensics. The success of deep learning in the last decade has led to its use in multimedia forensics as well. In this survey, we look at the latest trends and deep-learning-based techniques introduced to solve three main questions investigated in the field of multimedia forensics. We begin by examining the manipulations of images and videos produced with editing tools, reporting the deep-learning approaches adopted to Irene Amerini, Aris Anagnostopoulos, Luca Maiano and Lorenzo Ricciardi Celsi (2021), “Deep Learning for Multimedia Forensics”, Foundations and Trends® in Computer Graphics and Vision: Vol. 12, No. 4, pp 309–457. DOI: 10.1561/0600000096. Full text available at: http://dx.doi.org/10.1561/0600000096\",\"PeriodicalId\":45662,\"journal\":{\"name\":\"Foundations and Trends in Computer Graphics and Vision\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.8000,\"publicationDate\":\"2021-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Foundations and Trends in Computer Graphics and Vision\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1561/0600000096\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Foundations and Trends in Computer Graphics and Vision","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1561/0600000096","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
In the last two decades, we have witnessed an immense increase in the use of multimedia content on the internet, for multiple applications ranging from the most innocuous to very critical ones. Naturally, this emergence has given rise to many types of threats posed when this content can be manipulated/used for malicious purposes. For example, fake media can be used to drive personal opinions, ruining the image of a public figure, or for criminal activities such as terrorist propaganda and cyberbullying. The research community has of course moved to counter attack these threats by designing manipulation-detection systems based on a variety of techniques, such as signal processing, statistics, and machine learning. This research and practice activity has given rise to the field of multimedia forensics. The success of deep learning in the last decade has led to its use in multimedia forensics as well. In this survey, we look at the latest trends and deep-learning-based techniques introduced to solve three main questions investigated in the field of multimedia forensics. We begin by examining the manipulations of images and videos produced with editing tools, reporting the deep-learning approaches adopted to Irene Amerini, Aris Anagnostopoulos, Luca Maiano and Lorenzo Ricciardi Celsi (2021), “Deep Learning for Multimedia Forensics”, Foundations and Trends® in Computer Graphics and Vision: Vol. 12, No. 4, pp 309–457. DOI: 10.1561/0600000096. Full text available at: http://dx.doi.org/10.1561/0600000096
期刊介绍:
The growth in all aspects of research in the last decade has led to a multitude of new publications and an exponential increase in published research. Finding a way through the excellent existing literature and keeping up to date has become a major time-consuming problem. Electronic publishing has given researchers instant access to more articles than ever before. But which articles are the essential ones that should be read to understand and keep abreast with developments of any topic? To address this problem Foundations and Trends® in Computer Graphics and Vision publishes high-quality survey and tutorial monographs of the field.
Each issue of Foundations and Trends® in Computer Graphics and Vision comprises a 50-100 page monograph written by research leaders in the field. Monographs that give tutorial coverage of subjects, research retrospectives as well as survey papers that offer state-of-the-art reviews fall within the scope of the journal.