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Home > Proceedings / Proceedings of the AAAI Conference on Artificial Intelligence, 36 > No. 1: AAAI-22 Technical Tracks 1

CMUA-Watermark: A Cross-Model Universal Adversarial Watermark for Combating Deepfakes

February 1, 2023

Authors

Hao Huang

Peking University State Key Laboratory of Media Convergence Production Technology and Systems


Yongtao Wang

Peking University State Key Laboratory of Media Convergence Production Technology and Systems


Zhaoyu Chen

Fudan University


Yuze Zhang

Peking University


Yuheng Li

Peking University


Zhi Tang

Peking University State Key Laboratory of Media Convergence Production Technology and Systems


Wei Chu

Ant Group


Jingdong Chen

Ant Group


Weisi Lin

Nanyang Technological University, Singapore


Kai-Kuang Ma

Nanyang Technological University, Singapore


Proceedings:

No. 1: AAAI-22 Technical Tracks 1

Volume

Issue:

Proceedings of the AAAI Conference on Artificial Intelligence, 36

Track:

AAAI Technical Track on Computer Vision I

Downloads:

Download PDF

Abstract:

Malicious applications of deepfakes (i.e., technologies generating target facial attributes or entire faces from facial images) have posed a huge threat to individuals' reputation and security. To mitigate these threats, recent studies have proposed adversarial watermarks to combat deepfake models, leading them to generate distorted outputs. Despite achieving impressive results, these adversarial watermarks have low image-level and model-level transferability, meaning that they can protect only one facial image from one specific deepfake model. To address these issues, we propose a novel solution that can generate a Cross-Model Universal Adversarial Watermark (CMUA-Watermark), protecting a large number of facial images from multiple deepfake models. Specifically, we begin by proposing a cross-model universal attack pipeline that attacks multiple deepfake models iteratively. Then, we design a two-level perturbation fusion strategy to alleviate the conflict between the adversarial watermarks generated by different facial images and models. Moreover, we address the key problem in cross-model optimization with a heuristic approach to automatically find the suitable attack step sizes for different models, further weakening the model-level conflict. Finally, we introduce a more reasonable and comprehensive evaluation method to fully test the proposed method and compare it with existing ones. Extensive experimental results demonstrate that the proposed CMUA-Watermark can effectively distort the fake facial images generated by multiple deepfake models while achieving a better performance than existing methods. Our code is available at https://github.com/VDIGPKU/CMUA-Watermark.

DOI:

10.1609/aaai.v36i1.19982


AAAI

Proceedings of the AAAI Conference on Artificial Intelligence, 36



Topics: AAAI

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