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A Detection Method for DeepFake Hard Compressed Videos based on Super-resolution Reconstruction Using CNN

Published: 25 August 2020 Publication History

Abstract

The DeepFake video detection method based on convolutional neural networks has a poor performance in the dataset of hard compressed DeepFake video. And a large number of false tests will occur to the real data. To solve this problem, a networks model detection method for super-resolution reconstruction of DeepFake video is proposed. First of all, the face area of real data is processed by Gaussian blur, which is converted into negative data, and the real data and processing data are input into neural network for training. Then the residual network is used for super-resolution reconstruction of test data. Finally, the trained model is used to test the video after super-resolution reconstruction. Experiments show that the proposed method can reduce the false detection rate and improve the accuracy in detection of single frames.

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  • (2025)Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and DetectionNeurocomputing10.1016/j.neucom.2024.129116619(129116)Online publication date: Mar-2025
  • (2024)DeepFake Video Detection Using Machine Learning and Deep Learning Techniques2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO61523.2024.10522247(1-6)Online publication date: 14-Mar-2024
  • (2024)Enhancing Global Security: A Robust CNN Model for Deepfake Video Detection2024 7th International Conference on Information and Computer Technologies (ICICT)10.1109/ICICT62343.2024.00044(238-243)Online publication date: 15-Mar-2024
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  1. A Detection Method for DeepFake Hard Compressed Videos based on Super-resolution Reconstruction Using CNN

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    cover image ACM Other conferences
    HPCCT & BDAI '20: Proceedings of the 2020 4th High Performance Computing and Cluster Technologies Conference & 2020 3rd International Conference on Big Data and Artificial Intelligence
    July 2020
    276 pages
    ISBN:9781450375603
    DOI:10.1145/3409501
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    • Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University

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    Publication History

    Published: 25 August 2020

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    Author Tags

    1. Deep Learning
    2. DeepFake detection
    3. Hard compressed video
    4. Super-resolution reconstruction

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    Cited By

    View all
    • (2025)Exploring the Landscape of Compressed DeepFakes: Generation, Dataset and DetectionNeurocomputing10.1016/j.neucom.2024.129116619(129116)Online publication date: Mar-2025
    • (2024)DeepFake Video Detection Using Machine Learning and Deep Learning Techniques2024 11th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO)10.1109/ICRITO61523.2024.10522247(1-6)Online publication date: 14-Mar-2024
    • (2024)Enhancing Global Security: A Robust CNN Model for Deepfake Video Detection2024 7th International Conference on Information and Computer Technologies (ICICT)10.1109/ICICT62343.2024.00044(238-243)Online publication date: 15-Mar-2024
    • (2024)Advanced Deepfake Detection using Machine Learning Algorithms: A Statistical Analysis and Performance Comparison2024 7th International Conference on Information and Computer Technologies (ICICT)10.1109/ICICT62343.2024.00019(75-81)Online publication date: 15-Mar-2024
    • (2024)Deepfakes – Reality Under Threat?2024 IEEE 14th Annual Computing and Communication Workshop and Conference (CCWC)10.1109/CCWC60891.2024.10427659(0721-0727)Online publication date: 8-Jan-2024
    • (2023)Deepfakes: evolution and trendsSoft Computing10.1007/s00500-023-08605-y27:16(11295-11318)Online publication date: 15-Jun-2023
    • (2023)Deepfake detection using deep learning methods: A systematic and comprehensive reviewWIREs Data Mining and Knowledge Discovery10.1002/widm.152014:2Online publication date: 20-Nov-2023
    • (2022)Deepfake Detection: A Systematic Literature ReviewIEEE Access10.1109/ACCESS.2022.315440410(25494-25513)Online publication date: 2022

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