Vurimi Veera Venkata Naga Sai Vamsi , Sukanya S. Shet , Sodum Sai Mohan Reddy , Sharon S. Rose , Sona R. Shetty , S. Sathvika , Supriya M. S. , Sahana P. Shankar
{"title":"数字媒体取证中的深度造假检测","authors":"Vurimi Veera Venkata Naga Sai Vamsi , Sukanya S. Shet , Sodum Sai Mohan Reddy , Sharon S. Rose , Sona R. Shetty , S. Sathvika , Supriya M. S. , Sahana P. Shankar","doi":"10.1016/j.gltp.2022.04.017","DOIUrl":null,"url":null,"abstract":"<div><p>With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. In this paper, an approach for Deepfake detection has been provided. ResNext, a Convolutional Neural Network (CNN) algorithm and Long Short-Term Memory (LSTM) is used as an approach to detect the Deepfake videos. The approach and its steps are discussed in this paper. The accuracy obtained for the developed Deep-Learning (DL) model over the Celeb-Df dataset is 91%.</p></div>","PeriodicalId":100588,"journal":{"name":"Global Transitions Proceedings","volume":"3 1","pages":"Pages 74-79"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666285X2200053X/pdfft?md5=2df3d71db7169b57a9eaa3250dfa26e8&pid=1-s2.0-S2666285X2200053X-main.pdf","citationCount":"10","resultStr":"{\"title\":\"Deepfake detection in digital media forensics\",\"authors\":\"Vurimi Veera Venkata Naga Sai Vamsi , Sukanya S. Shet , Sodum Sai Mohan Reddy , Sharon S. Rose , Sona R. Shetty , S. Sathvika , Supriya M. S. , Sahana P. Shankar\",\"doi\":\"10.1016/j.gltp.2022.04.017\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. In this paper, an approach for Deepfake detection has been provided. ResNext, a Convolutional Neural Network (CNN) algorithm and Long Short-Term Memory (LSTM) is used as an approach to detect the Deepfake videos. The approach and its steps are discussed in this paper. The accuracy obtained for the developed Deep-Learning (DL) model over the Celeb-Df dataset is 91%.</p></div>\",\"PeriodicalId\":100588,\"journal\":{\"name\":\"Global Transitions Proceedings\",\"volume\":\"3 1\",\"pages\":\"Pages 74-79\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666285X2200053X/pdfft?md5=2df3d71db7169b57a9eaa3250dfa26e8&pid=1-s2.0-S2666285X2200053X-main.pdf\",\"citationCount\":\"10\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Global Transitions Proceedings\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666285X2200053X\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Global Transitions Proceedings","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666285X2200053X","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
With the development of technology and ease of creation of fake content, the manipulation of media is carried out on a large scale in recent times. The rise of AI altered videos or Deepfake media has posed a great threat to media integrity and is being produced and spread widely across social media platforms, the detection of which is seen to be a major challenge. In this paper, an approach for Deepfake detection has been provided. ResNext, a Convolutional Neural Network (CNN) algorithm and Long Short-Term Memory (LSTM) is used as an approach to detect the Deepfake videos. The approach and its steps are discussed in this paper. The accuracy obtained for the developed Deep-Learning (DL) model over the Celeb-Df dataset is 91%.