181 citations · 206 across the 15 of their papers we have counts for
15 papers
PHANTOM: A Large-Scale Dataset of Multimodal Adversarial Attacks for Vision-Language Models
Simone Gallivanone, Hossein Khodadadi, Mauro Dore +2
We introduce a large-scale, open-source dataset of pre-generated adversarial attacks for vision-language models (VLMs). The dataset is designed to be diverse, representative, and p…
A Red-Team Study of Anthropic Fable 5 & Opus 4.8 Models
Nicola Franco
We evaluate the adversarial robustness of three frontier large language models (LLMs) developed by Anthropic, Opus 4.8, Fable 5 and its successor Fable 5.1, against four families o…
LipShiFT: A Certifiably Robust Shift-based Vision Transformer
Rohan Menon, Nicola Franco, Stephan Günnemann
Deriving tight Lipschitz bounds for transformer-based architectures presents a significant challenge. The large input sizes and high-dimensional attention modules typically prove t…
Certifiably Robust Encoding Schemes
Aman Saxena, Tom Wollschläger, Nicola Franco +2
Quantum machine learning uses principles from quantum mechanics to process data, offering potential advances in speed and performance. However, previous work has shown that these m…
Discrete Randomized Smoothing Meets Quantum Computing
Tom Wollschläger, Aman Saxena, Nicola Franco +2
Breakthroughs in machine learning (ML) and advances in quantum computing (QC) drive the interdisciplinary field of quantum machine learning to new levels. However, due to the susce…
Quadratic Advantage with Quantum Randomized Smoothing Applied to Time-Series Analysis
Nicola Franco, Marie Kempkes, Jakob Spiegelberg +1
As quantum machine learning continues to develop at a rapid pace, the importance of ensuring the robustness and efficiency of quantum algorithms cannot be overstated. Our research…