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Our proposed method offers a powerful and efficient solution for combating Deepfake, which can help preserve personal privacy and prevent reputational damage.
Oct 28, 2020 · In this paper, we propose a novel deep learning technique for generating more transferable UAPs. We utilize a perturbation generator and some given pretrained ...
Missing: Combating | Show results with:Combating
Sep 9, 2024 · In this paper, we propose a novel deep learning technique for generating more transferable UAPs. We utilize a perturbation generator and some ...
Missing: Combating Deepfakes.
Sep 17, 2024 · Incorporating adversarial perturbations into images to fool Deepfake models is a pivotal strategy in defending against manipulated content.
Nov 19, 2020 · Universal adversarial perturbations pose a more practical threat to DeepFake detection since they can be easily shared amongst attackers and ...
Oct 28, 2020 · This paper proposes a novel deep learning technique for generating more transferable universal adversarial perturbations (UAPs) and proposes ...
Missing: Combating Deepfakes.
Aug 30, 2024 · This paper discusses multiple approaches to mitigate the deepfake predicament at its source, emphasizing the need to prevent their generation.
Our experiments demonstrate that our approach outperforms the state-of-the-art universal and transferable attack strategies. 1 Introduction. In recent years, ...
Missing: Combating Deepfakes.
We propose a simple yet effective perturbation fusion strategy to alleviate the conflict and enhance the image- level and model-level transferability of the ...
Jun 18, 2022 · Our method effectively generates universal adversarial perturbations achieving state-of-the-art fooling rates across different models, tasks, and datasets.
Missing: Deepfakes. | Show results with:Deepfakes.