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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…
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,…
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…
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…
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?
Antonio Emanuele Cinà, Sebastiano Vascon, Ambra Demontis +3
One of the most concerning threats for modern AI systems is data poisoning, where the attacker injects maliciously crafted training data to corrupt the system's behavior at test ti…
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Maura Pintor, Fabio Roli, Wieland Brendel +1
Evaluating adversarial robustness amounts to finding the minimum perturbation needed to have an input sample misclassified. The inherent complexity of the underlying optimization r…
Domain Knowledge Alleviates Adversarial Attacks in Multi-Label Classifiers
Stefano Melacci, Gabriele Ciravegna, Angelo Sotgiu +4
Adversarial attacks on machine learning-based classifiers, along with defense mechanisms, have been widely studied in the context of single-label classification problems. In this p…
Adversarial Feature Selection against Evasion Attacks
Fei Zhang, Patrick P. K. Chan, Battista Biggio +2
Pattern recognition and machine learning techniques have been increasingly adopted in adversarial settings such as spam, intrusion and malware detection, although their security ag…
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Marco Melis, Michele Scalas, Ambra Demontis +4
While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of…
Poisoning Behavioral Malware Clustering
Battista Biggio, Konrad Rieck, Davide Ariu +4
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for anti…
Is Data Clustering in Adversarial Settings Secure?
Battista Biggio, Ignazio Pillai, Samuel Rota Bulò +3
Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with d…
Why Do Adversarial Attacks Transfer? Explaining Transferability of Evasion and Poisoning Attacks
Ambra Demontis, Marco Melis, Maura Pintor +5
Transferability captures the ability of an attack against a machine-learning model to be effective against a different, potentially unknown, model. Empirical evidence for transfera…
Towards Adversarial Configurations for Software Product Lines
Paul Temple, Mathieu Acher, Battista Biggio +2
Ensuring that all supposedly valid configurations of a software product line (SPL) lead to well-formed and acceptable products is challenging since it is most of the time impractic…
Is feature selection secure against training data poisoning?
Huang Xiao, Battista Biggio, Gavin Brown +3
Learning in adversarial settings is becoming an important task for application domains where attackers may inject malicious data into the training set to subvert normal operation o…
Explaining Black-box Android Malware Detection
Marco Melis, Davide Maiorca, Battista Biggio +2
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on fea…
Security Evaluation of Pattern Classifiers under Attack
Battista Biggio, Giorgio Fumera, Fabio Roli
Pattern classification systems are commonly used in adversarial applications, like biometric authentication, network intrusion detection, and spam filtering, in which data can be p…
On Security and Sparsity of Linear Classifiers for Adversarial Settings
Ambra Demontis, Paolo Russu, Battista Biggio +2
Machine-learning techniques are widely used in security-related applications, like spam and malware detection. However, in such settings, they have been shown to be vulnerable to a…