27 citations · 62 across the 9 of their papers we have counts for
11 papers
Stealix: Model Stealing via Prompt Evolution
Zhixiong Zhuang, Hui-Po Wang, Maria-Irina Nicolae +1
Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing int…
ProxyPrompt: Securing System Prompts against Prompt Extraction Attacks
Zhixiong Zhuang, Maria-Irina Nicolae, Hui-Po Wang +1
The integration of large language models (LLMs) into a wide range of applications has highlighted the critical role of well-crafted system prompts, which require extensive testing…
Medical Multimodal Model Stealing Attacks via Adversarial Domain Alignment
Yaling Shen, Zhixiong Zhuang, Kun Yuan +4
Medical multimodal large language models (MLLMs) are becoming an instrumental part of healthcare systems, assisting medical personnel with decision making and results analysis. Mod…
Stealthy Imitation: Reward-guided Environment-free Policy Stealing
Zhixiong Zhuang, Maria-Irina Nicolae, Mario Fritz
Deep reinforcement learning policies, which are integral to modern control systems, represent valuable intellectual property. The development of these policies demands considerable…
Revisiting Neural Program Smoothing for Fuzzing
Maria-Irina Nicolae, Max Eisele, Andreas Zeller
Testing with randomly generated inputs (fuzzing) has gained significant traction due to its capacity to expose program vulnerabilities automatically. Fuzz testing campaigns generat…
Adversarial Robustness Toolbox v1.0.0
Maria-Irina Nicolae, Mathieu Sinn, Minh Ngoc Tran +9
Adversarial Robustness Toolbox (ART) is a Python library supporting developers and researchers in defending Machine Learning models (Deep Neural Networks, Gradient Boosted Decision…
Adversarial Phenomenon in the Eyes of Bayesian Deep Learning
Ambrish Rawat, Martin Wistuba, Maria-Irina Nicolae
Deep Learning models are vulnerable to adversarial examples, i.e.\ images obtained via deliberate imperceptible perturbations, such that the model misclassifies them with high conf…
Efficient Defenses Against Adversarial Attacks
Valentina Zantedeschi, Maria-Irina Nicolae, Ambrish Rawat
Following the recent adoption of deep neural networks (DNN) accross a wide range of applications, adversarial attacks against these models have proven to be an indisputable threat.…
Open-World Visual Recognition Using Knowledge Graphs
Vincent P. A. Lonij, Ambrish Rawat, Maria-Irina Nicolae
In a real-world setting, visual recognition systems can be brought to make predictions for images belonging to previously unknown class labels. In order to make semantically meanin…
Similarity Learning for Time Series Classification
Maria-Irina Nicolae, Éric Gaussier, Amaury Habrard +1
Multivariate time series naturally exist in many fields, like energy, bioinformatics, signal processing, and finance. Most of these applications need to be able to compare these st…
Algorithmic Robustness for Learning via -Good Similarity Functions
Maria-Irina Nicolae, Marc Sebban, Amaury Habrard +2
The notion of metric plays a key role in machine learning problems such as classification, clustering or ranking. However, it is worth noting that there is a severe lack of theoret…