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…
Constructing Optimal Noise Channels for Enhanced Robustness in Quantum Machine Learning
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
With the rapid advancement of Quantum Machine Learning (QML), the critical need to enhance security measures against adversarial attacks and protect QML models becomes increasingly…
Predominant Aspects on Security for Quantum Machine Learning: Literature Review
Nicola Franco, Alona Sakhnenko, Leon Stolpmann +4
Quantum Machine Learning (QML) has emerged as a promising intersection of quantum computing and classical machine learning, anticipated to drive breakthroughs in computational task…
Quantum Neural Networks under Depolarization Noise: Exploring White-Box Attacks and Defenses
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
Leveraging the unique properties of quantum mechanics, Quantum Machine Learning (QML) promises computational breakthroughs and enriched perspectives where traditional systems reach…
Challenges and Opportunities in Quantum Optimization
Amira Abbas, Andris Ambainis, Brandon Augustino +43
Recent advances in quantum computers are demonstrating the ability to solve problems at a scale beyond brute force classical simulation. As such, a widespread interest in quantum a…
Efficient MILP Decomposition in Quantum Computing for ReLU Network Robustness
Nicola Franco, Tom Wollschläger, Benedikt Poggel +2
Emerging quantum computing technologies, such as Noisy Intermediate-Scale Quantum (NISQ) devices, offer potential advancements in solving mathematical optimization problems. Howeve…
Diffusion Denoised Smoothing for Certified and Adversarial Robust Out-Of-Distribution Detection
Nicola Franco, Daniel Korth, Jeanette Miriam Lorenz +2
As the use of machine learning continues to expand, the importance of ensuring its safety cannot be overstated. A key concern in this regard is the ability to identify whether a gi…
A Comparative Study On Solving Optimization Problems With Exponentially Fewer Qubits
David Winderl, Nicola Franco, Jeanette Miriam Lorenz
Variational Quantum optimization algorithms, such as the Variational Quantum Eigensolver (VQE) or the Quantum Approximate Optimization Algorithm (QAOA), are among the most studied…
Quantum Robustness Verification: A Hybrid Quantum-Classical Neural Network Certification Algorithm
Nicola Franco, Tom Wollschlaeger, Nicholas Gao +2
In recent years, quantum computers and algorithms have made significant progress indicating the prospective importance of quantum computing (QC). Especially combinatorial optimizat…
Hyperbolic Generative Adversarial Network
Diego Lazcano, Nicolás Fredes, Werner Creixell
Recently, Hyperbolic Spaces in the context of Non-Euclidean Deep Learning have gained popularity because of their ability to represent hierarchical data. We propose that it is poss…