Ahmet Semih Uçan, Fatih Mustafa Buçak, Mehmet Ali Han Tutuk, Halis İbrahim Aydin, Ertuğrul Semiz, Şerif Bahtiyar
{"title":"视频会议的深度造假与安全","authors":"Ahmet Semih Uçan, Fatih Mustafa Buçak, Mehmet Ali Han Tutuk, Halis İbrahim Aydin, Ertuğrul Semiz, Şerif Bahtiyar","doi":"10.1109/UBMK52708.2021.9558963","DOIUrl":null,"url":null,"abstract":"Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep learning algorithms are known as deepfake. Recently, malicious users tend to use deepfake to manipulate genuine contents to carry out variety of attacks. Video conferencing applications has been a significant target of the malicious users since the beginning of Covid-19 pandemic who use deepfake models to create fake virtual identities in online video conferences. We propose a lightweight deepfake detection model that may be integrated with video conference applications to detect fake faces. Experimental analyses show that the proposed model provides acceptable accuracy to detect fake images on video conferences.","PeriodicalId":106516,"journal":{"name":"2021 6th International Conference on Computer Science and Engineering (UBMK)","volume":"33 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Deepfake and Security of Video Conferences\",\"authors\":\"Ahmet Semih Uçan, Fatih Mustafa Buçak, Mehmet Ali Han Tutuk, Halis İbrahim Aydin, Ertuğrul Semiz, Şerif Bahtiyar\",\"doi\":\"10.1109/UBMK52708.2021.9558963\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep learning algorithms are known as deepfake. Recently, malicious users tend to use deepfake to manipulate genuine contents to carry out variety of attacks. Video conferencing applications has been a significant target of the malicious users since the beginning of Covid-19 pandemic who use deepfake models to create fake virtual identities in online video conferences. We propose a lightweight deepfake detection model that may be integrated with video conference applications to detect fake faces. Experimental analyses show that the proposed model provides acceptable accuracy to detect fake images on video conferences.\",\"PeriodicalId\":106516,\"journal\":{\"name\":\"2021 6th International Conference on Computer Science and Engineering (UBMK)\",\"volume\":\"33 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-09-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 6th International Conference on Computer Science and Engineering (UBMK)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/UBMK52708.2021.9558963\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 6th International Conference on Computer Science and Engineering (UBMK)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/UBMK52708.2021.9558963","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Deep learning is widely used to create artificial contents on the Internet. Similarly, it is also used to detect fake contents. Fake frames created and integrated with deep learning algorithms are known as deepfake. Recently, malicious users tend to use deepfake to manipulate genuine contents to carry out variety of attacks. Video conferencing applications has been a significant target of the malicious users since the beginning of Covid-19 pandemic who use deepfake models to create fake virtual identities in online video conferences. We propose a lightweight deepfake detection model that may be integrated with video conference applications to detect fake faces. Experimental analyses show that the proposed model provides acceptable accuracy to detect fake images on video conferences.