A novel assessment framework for learning-based deepfake detectors in realistic conditions-Reference-Cited by-同舟云学术

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A novel assessment framework for learning-based deepfake detectors in realistic conditions

Published:2022-10-03 Issue: Volume: Page:
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Container-title:Applications of Digital Image Processing XLV
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Author:

Lu Yuhang,Ebrahimi Touradj

Publisher

SPIE

Reference40 articles.

1. The deepfake detection challenge dataset;Dolhansky,2020

2. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

3. Ai singapore trusted media challenge dataset;Chen;arXiv preprint arXiv:2201.04788,2022

4. 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

5. Use of a capsule network to detect fake images and videos;Nguyen;ArXiv abs/1910.12467,2019

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1. Assessment framework for deepfake detection in real-world situations;EURASIP Journal on Image and Video Processing;2024-02-13

2. Facial deepfake performance evaluation based on three detection tools: MTCNN, Dlib, and MediaPipe;AIP Conference Proceedings;2024

3. Improving deepfake detectors against real-world perturbations with amplitude-phase switch augmentation;Applications of Digital Image Processing XLVI;2023-10-04

4. Impact of Video Processing Operations in Deepfake Detection;2023 24th International Conference on Digital Signal Processing (DSP);2023-06-11

5. A Novel Method of Deepfake Detection;ITM Web of Conferences;2022

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