Exploring Frequency Adversarial Attacks for Face Forgery Detection-Reference-Cited by-同舟云学术

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Exploring Frequency Adversarial Attacks for Face Forgery Detection

Published:2022-06 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
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Author:

Jia Shuai¹,Ma Chao¹,Yao Taiping²,Yin Bangjie²,Ding Shouhong²,Yang Xiaokang¹

Affiliation:

1. AI Institute, Shanghai Jiao Tong University,MoE Key Lab of Artificial Intelligence

2. Tencent,Youtu Lab

Funder

NSFC

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9878378/9878366/09878492.pdf?arnumber=9878492

Reference53 articles.

1. On the Effectiveness of Low Frequency Perturbations

2. Faceforen-sics++: Learning to detect manipulated facial images;rossler;ICCV,2019

3. The creation and detection of deepfakes: A survey;mirsky;ar Xiv preprint,2020

4. Two-branch recurrent network for isolating deepfakes in videos;masi;ECCV,2020

5. Towards deep learning models resistant to adversarial attacks;madry;ICLRE,2018

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2. Meta Invariance Defense Towards Generalizable Robustness to Unknown Adversarial Attacks;IEEE Transactions on Pattern Analysis and Machine Intelligence;2024-10

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4. Harmonizing Transferability and Imperceptibility: A Novel Ensemble Adversarial Attack;IEEE Internet of Things Journal;2024-08-01

5. Multi-attribute Semantic Adversarial Attack Based on Cross-layer Interpolation for Face Recognition;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

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