6 papers
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,…
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…
Energy-Latency Attacks via Sponge Poisoning
Antonio Emanuele CinÃ, 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…
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
Antonio Emanuele CinÃ, 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…