On the Vulnerability of Deepfake Detectors to Attacks Generated by Denoising Diffusion Models-Reference-Cited by-同舟云学术

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On the Vulnerability of Deepfake Detectors to Attacks Generated by Denoising Diffusion Models

Published:2024-01-01 Issue: Volume:34 Page:1051-1060
ISSN:
Container-title:2024 IEEE/CVF Winter Conference on Applications of Computer Vision Workshops (WACVW)
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Author:

Ivanovska Marija¹,Štruc Vitomir¹

Affiliation:

1. University of Ljubljana,Faculty of electrical engineering,Ljubljana,Slovenia

Funder

ARRS

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10495586/10495587/10495703.pdf?arnumber=10495703

Reference36 articles.

1. MesoNet: a Compact Facial Video Forgery Detection Network

2. End-to-End Reconstruction-Classification Learning for Face Forgery Detection

3. Evading Deepfake-Image Detectors with White- and Black-Box Attacks

4. Xception: Deep Learning with Depthwise Separable Convolutions

Cited by 2 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Crafting imperceptible and transferable adversarial examples: leveraging conditional residual generator and wavelet transforms to deceive deepfake detection;The Visual Computer;2024-09-06

2. Deepfake video detection: challenges and opportunities;Artificial Intelligence Review;2024-05-29

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