1.4k citations · 4.6k across the 52 of their papers we have counts for
61 papers
A Neural Rejection System Against Universal Adversarial Perturbations in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +1
Advantages of deep learning over traditional methods have been demonstrated for radio signal classification in the recent years. However, various researchers have discovered that e…
Attention-based Adversarial Robust Distillation in Radio Signal Classifications for Low-Power IoT Devices
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +3
Due to great success of transformers in many applications such as natural language processing and computer vision, transformers have been successfully applied in automatic modulati…
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…
Countermeasures Against Adversarial Examples in Radio Signal Classification
Lu Zhang, Sangarapillai Lambotharan, Gan Zheng +2
Deep learning algorithms have been shown to be powerful in many communication network design problems, including that in automatic modulation classification. However, they are vuln…
Serial fusion of multi-modal biometric systems
Gian Luca Marcialis, Paolo Mastinu, Fabio Roli
Serial, or sequential, fusion of multiple biometric matchers has been not thoroughly investigated so far. However, this approach exhibits some advantages with respect to the widely…
3D Face Reconstruction: the Road to Forensics
Simone Maurizio La Cava, Giulia Orrù, Martin Drahansky +2
3D face reconstruction algorithms from images and videos are applied to many fields, from plastic surgery to the entertainment sector, thanks to their advantageous features. Howeve…
Adversarial Attacks Against Uncertainty Quantification
Emanuele Ledda, Daniele Angioni, Giorgio Piras +3
Machine-learning models can be fooled by adversarial examples, i.e., carefully-crafted input perturbations that force models to output wrong predictions. While uncertainty quantifi…
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…
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks
Emanuele Ledda, Giorgio Fumera, Fabio Roli
Among Bayesian methods, Monte-Carlo dropout provides principled tools for evaluating the epistemic uncertainty of neural networks. Its popularity recently led to seminal works that…
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…
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Fabio Roli
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this…
Support Vector Machines under Adversarial Label Contamination
Huang Xiao, Battista Biggio, Blaine Nelson +3
Machine learning algorithms are increasingly being applied in security-related tasks such as spam and malware detection, although their security properties against deliberate attac…
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…
Can machines learn to see without visual databases?
Alessandro Betti, Marco Gori, Stefano Melacci +2
This paper sustains the position that the time has come for thinking of learning machines that conquer visual skills in a truly human-like context, where a few human-like object su…
Fingerprint recognition with embedded presentation attacks detection: are we ready?
Marco Micheletto, Gian Luca Marcialis, Giulia Orrù +1
The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms…
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…
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…
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
Antonio Emanuele Cinà, Kathrin Grosse, Sebastiano Vascon +4
Backdoor attacks inject poisoning samples during training, with the goal of forcing a machine learning model to output an attacker-chosen class when presented a specific trigger at…
BAARD: Blocking Adversarial Examples by Testing for Applicability, Reliability and Decidability
Xinglong Chang, Katharina Dost, Kaiqi Zhao +4
Adversarial defenses protect machine learning models from adversarial attacks, but are often tailored to one type of model or attack. The lack of information on unknown potential a…