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Found 4 papers, 3 papers with code
Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective
Dataset distillation offers a potential means to enhance data efficiency in deep learning.
Elijah: Eliminating Backdoors Injected in Diffusion Models via Distribution Shift
Diffusion models (DM) have become state-of-the-art generative models because of their capability to generate high-quality images from noises without adversarial training.
VillanDiffusion: A Unified Backdoor Attack Framework for Diffusion Models
This paper presents a unified backdoor attack framework (VillanDiffusion) to expand the current scope of backdoor analysis for DMs.
How to Backdoor Diffusion Models?
To gain a better understanding of the limitations and potential risks, this paper presents the first study on the robustness of diffusion models against backdoor attacks.
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