3 citations · 3 across the 5 of their papers we have counts for
5 papers
Evaluating Constrained Iterative Refinement for Scalable Vector Graphics Generation with Off-the-Shelf VLMs
Matthew Perlman, James Beetham, Niels Da Vitoria Lobo +2
Scalable Vector Graphics (SVGs) power much of the modern visual ecosystem, yet state-of-the-art generative models focus almost entirely on rasterized images. We explore whether inf…
The Illusion of High Utility in Safety Alignment of Text-to-Image Diffusion Models
Adeel Yousaf, Soumik Ghosh, James Beetham +2
Safety alignment of text-to-image (T2I) diffusion models aims to suppress harmful generations while preserving utility on benign prompts. Recent methods often appear to deliver hig…
SafeR-CLIP: Mitigating NSFW Content in Vision-Language Models While Preserving Pre-Trained Knowledge
Adeel Yousaf, Joseph Fioresi, James Beetham +2
Improving the safety of vision-language models like CLIP via fine-tuning often comes at a steep price, causing significant drops in their generalization performance. We find this t…
LIAR: Leveraging Inference Time Alignment (Best-of-N) to Jailbreak LLMs in Seconds
James Beetham, Souradip Chakraborty, Mengdi Wang +3
Jailbreak attacks expose vulnerabilities in safety-aligned LLMs by eliciting harmful outputs through carefully crafted prompts. Existing methods rely on discrete optimization or tr…
Dual Student Networks for Data-Free Model Stealing
James Beetham, Navid Kardan, Ajmal Mian +1
Existing data-free model stealing methods use a generator to produce samples in order to train a student model to match the target model outputs. To this end, the two main challeng…