DeepFake Video Analysis using SIFT Features

被引：5
|
作者：
Dordevic, Miljan [1 ]
Milivojevic, Milan [2 ]
Gavrovska, Ana [2 ]
机构：
[1] Univ Belgrade, Sch Elect Engn, Dept Comp Sci & Informat Technol, Bulevar Kralja Aleksandra 73, Belgrade 11020, Serbia
[2] Univ Belgrade, Sch Elect Engn, Dept Telecommun, Bulevar Kralja Aleksandra 73, Belgrade 11020, Serbia
来源：
2019 27TH TELECOMMUNICATIONS FORUM (TELFOR 2019) | 2019年
关键词：
SIFT features; video analysis; forgery; DeepFake; deep learning; computer vision;
D O I：
10.1109/telfor48224.2019.8971206
中图分类号：
TN [电子技术、通信技术];
学科分类号：
0809 ;
摘要：
Recent advantages in changing faces using DeepFake algorithms, which replace a face of one person with a face of another, truly represent what artificial intelligence and deep learning are capable of. Deepfakes in still images or video clips represent forgeries and tampered visual information. They are becoming increasingly successful and even difficult to notice in some cases. In this paper we analyze deepfakes using SIFT (Scale-Invariant Feature Transform) features. The experimental results show that in deepfake analysis using SIFT keypoints can be considered valuable.
引用
收藏
页码：507 / 510
页数：4
相关论文
共 50 条
  • [1] On the effectiveness of handcrafted features for deepfake video detection
    Kaddar, Bachir
    Fezza, Sid Ahmed
    Hamidouche, Wassim
    Akhtar, Zahid
    Hadid, Abdenour
    [J]. JOURNAL OF ELECTRONIC IMAGING, 2023, 32 (05)
  • [2] SIFT features tracking for video stabilization
    Battiato, Sebastiano
    Gallo, Giovanni
    Puglisi, Giovanni
    Scellato, Salvatore
    [J]. 14TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING, PROCEEDINGS, 2007, : 825 - +
  • [3] Video Stabilization Using SIFT-ME Features and Fuzzy Clustering
    Veon, Kevin L.
    Mahoor, Mohammad H.
    Voyles, Richard M.
    [J]. 2011 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS, 2011,
  • [4] Object Classification in an Ultrasound Video Using LP-SIFT Features
    Maraci, Mohammad Ali
    Napolitano, Raffaele
    Papageorghiou, Aris
    Noble, J. Allison
    [J]. MEDICAL COMPUTER VISION: ALGORITHMS FOR BIG DATA, 2014, 8848 : 71 - 81
  • [5] Video Shot Detection based on SIFT Features and Video Summarization using Expectation-Maximization
    Majumdar, Jharna
    Awale, Manish
    Kumar, Santhosh K. L.
    [J]. 2018 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2018, : 1033 - 1037
  • [6] HCiT: Deepfake Video Detection Using a Hybrid Model of CNN features and Vision Transformer
    Kaddar, Bachir
    Fezza, Sid Ahmed
    Hamidouche, Wassim
    Akhtar, Zahid
    Hadid, Abdenour
    [J]. 2021 INTERNATIONAL CONFERENCE ON VISUAL COMMUNICATIONS AND IMAGE PROCESSING (VCIP), 2021,
  • [7] Layout Analysis for Historical Manuscripts Using SIFT Features
    Garz, Angelika
    Sablatnig, Robert
    Diem, Markus
    [J]. 11TH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION (ICDAR 2011), 2011, : 508 - 512
  • [8] Capturing the Persistence of Facial Expression Features for Deepfake Video Detection
    Zhao, Yiru
    Ge, Wanfeng
    Li, Wenxin
    Wang, Run
    Zhao, Lei
    Ming, Jiang
    [J]. INFORMATION AND COMMUNICATIONS SECURITY (ICICS 2019), 2020, 11999 : 630 - 645
  • [9] SIFT features in semi-fragile video watermarks
    Thiemert, Stefan
    Steinebach, Martin
    [J]. MEDIA FORENSICS AND SECURITY II, 2010, 7541
  • [10] PERSONALIZED VIDEO SUMMARIZATION USING SIFT
    Darabi, Kaveh
    Ghinea, Gheorghita
    [J]. 30TH ANNUAL ACM SYMPOSIUM ON APPLIED COMPUTING, VOLS I AND II, 2015, : 1252 - 1256
12345