3 citations · 5 across the 11 of their papers we have counts for
7 papers · 1 filter
Certified but Fooled! Breaking Certified Defences with Ghost Certificates
Quoc Viet Vo, Tashreque M. Haq, Paul Montague +3
Certified defenses promise provable robustness guarantees. We study the malicious exploitation of probabilistic certification frameworks to better understand the limits of guarante…
Mitigating Semantic Collapse in Generative Personalization with Test-Time Embedding Adjustment
Anh Bui, Trang Vu, Trung Le +5
In this paper, we investigate the semantic collapsing problem in generative personalization, an under-explored topic where the learned visual concept () gradually shifts from it…
Fantastic Targets for Concept Erasure in Diffusion Models and Where To Find Them
Anh Bui, Trang Vu, Long Vuong +5
Concept erasure has emerged as a promising technique for mitigating the risk of harmful content generation in diffusion models by selectively unlearning undesirable concepts. The c…
Erasing Undesirable Concepts in Diffusion Models with Adversarial Preservation
Anh Bui, Long Vuong, Khanh Doan +4
Diffusion models excel at generating visually striking content from text but can inadvertently produce undesirable or harmful content when trained on unfiltered internet data. A pr…
Hiding and Recovering Knowledge in Text-to-Image Diffusion Models via Learnable Prompts
Anh Bui, Khanh Doan, Trung Le +3
Diffusion models have demonstrated remarkable capability in generating high-quality visual content from textual descriptions. However, since these models are trained on large-scale…
Improving Adversarial Robustness by Enforcing Local and Global Compactness
Anh Bui, Trung Le, He Zhao +4
The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defens…