148 citations · 439 across the 31 of their papers we have counts for
39 papers
Adversarial Frontiers: Minimum-Norm Attack Ensembles for Robustness Evaluation
Luca Scionis, Luca Melis, Maura Pintor +5
Adversarial robustness is commonly evaluated with predefined attack ensembles, such as AutoAttack, at a single perturbation budget and on a selective choice of pertur…
Out-of-Distribution Detection for Continual Learning: Design Principles and Benchmarking
Srishti Gupta, Riccardo Balia, Daniele Angioni +7
Recent years have witnessed significant progress in the development of machine learning models across a wide range of fields, fueled by increased computational resources, large-sca…
Prototype-Guided Robust Learning against Backdoor Attacks
Wei Guo, Maura Pintor, Ambra Demontis +1
Backdoor attacks poison the training data, causing the model to behave normally on clean inputs but predict attacker-chosen labels when trigger patterns are embedded into the input…
Silent Until Sparse: Backdoor Attacks on Semi-Structured Sparsity
Wei Guo, Fabio Brau, Maura Pintor +2
Semi-structured (2:4) sparsity is a widely adopted pruning method in modern hardware and software ecosystems (e.g., NVIDIA Sparse Tensor Cores and PyTorch), achieving up to 2X fast…
Evaluating the Evaluators: Trust in Adversarial Robustness Tests
Antonio Emanuele Cinà, Maura Pintor, Luca Demetrio +3
Despite significant progress in designing powerful adversarial evasion attacks for robustness verification, the evaluation of these methods often remains inconsistent and unreliabl…
Buffer-free Class-Incremental Learning with Out-of-Distribution Detection
Srishti Gupta, Daniele Angioni, Maura Pintor +4
Class-incremental learning (CIL) poses significant challenges in open-world scenarios, where models must not only learn new classes over time without forgetting previous ones but a…
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
Giorgio Piras, Maura Pintor, Ambra Demontis +3
Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i.e., well-crafted inputs induc…
A Hybrid Training-time and Run-time Defense Against Adversarial Attacks in Modulation Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Motivated by the superior performance of deep learning in many applications including computer vision and natural language processing, several recent studies have focused on applyi…
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks
Raffaele Mura, Giuseppe Floris, Luca Scionis +6
Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed l…
Over-parameterization and Adversarial Robustness in Neural Networks: An Overview and Empirical Analysis
Srishti Gupta, Zhang Chen, Luca Demetrio +9
Thanks to their extensive capacity, over-parameterized neural networks exhibit superior predictive capabilities and generalization. However, having a large parameter space is consi…
AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples
Antonio Emanuele Cinà, Jérôme Rony, Maura Pintor +5
Adversarial examples are typically optimized with gradient-based attacks. While novel attacks are continuously proposed, each is shown to outperform its predecessors using differen…
Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization
Giuseppe Floris, Raffaele Mura, Luca Scionis +4
Evaluating the adversarial robustness of machine learning models using gradient-based attacks is challenging. In this work, we show that hyperparameter optimization can improve fas…
Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks
Giorgio Piras, Maura Pintor, Ambra Demontis +1
Neural network pruning has shown to be an effective technique for reducing the network size, trading desirable properties like generalization and robustness to adversarial attacks…
Hardening RGB-D Object Recognition Systems against Adversarial Patch Attacks
Yang Zheng, Luca Demetrio, Antonio Emanuele Cinà +6
RGB-D object recognition systems improve their predictive performances by fusing color and depth information, outperforming neural network architectures that rely solely on colors.…
Minimizing Energy Consumption of Deep Learning Models by Energy-Aware Training
Dario Lazzaro, Antonio Emanuele Cinà, Maura Pintor +4
Deep learning models undergo a significant increase in the number of parameters they possess, leading to the execution of a larger number of operations during inference. This expan…
Security of Deep Reinforcement Learning for Autonomous Driving: A Survey
Ambra Demontis, Srishti Gupta, Maura Pintor +6
Reinforcement learning (RL) enables agents to learn optimal behaviors through interaction with their environment and has been increasingly deployed in safety-critical applications,…
Stateful Detection of Adversarial Reprogramming
Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia +5
Adversarial reprogramming allows stealing computational resources by repurposing machine learning models to perform a different task chosen by the attacker. For example, a model tr…
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis +7
The success of machine learning is fueled by the increasing availability of computing power and large training datasets. The training data is used to learn new models or update exi…
Machine Learning Security against Data Poisoning: Are We There Yet?
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis +3
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications. However, the…
Energy-Latency Attacks via Sponge Poisoning
Antonio Emanuele Cinà, Ambra Demontis, Battista Biggio +2
Sponge examples are test-time inputs optimized to increase energy consumption and prediction latency of deep networks deployed on hardware accelerators. By increasing the fraction…
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
Maura Pintor, Daniele Angioni, Angelo Sotgiu +4
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally de…
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference
Yang Zheng, Xiaoyi Feng, Zhaoqiang Xia +5
Adversarial reprogramming allows repurposing a machine-learning model to perform a different task. For example, a model trained to recognize animals can be reprogrammed to recogniz…
The Threat of Offensive AI to Organizations
Yisroel Mirsky, Ambra Demontis, Jaidip Kotak +7
AI has provided us with the ability to automate tasks, extract information from vast amounts of data, and synthesize media that is nearly indistinguishable from the real thing. How…
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Maura Pintor, Luca Demetrio, Angelo Sotgiu +4
Evaluating robustness of machine-learning models to adversarial examples is a challenging problem. Many defenses have been shown to provide a false sense of robustness by causing g…