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12/12/2022
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experienc...
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11/05/2022
Stateful Detection of Adversarial Reprogramming
Adversarial reprogramming allows stealing computational resources by rep...
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03/14/2022
Energy-Latency Attacks via Sponge Poisoning
Sponge examples are test-time inputs carefully-optimized to increase ene...
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03/07/2022
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
Adversarial patches are optimized contiguous pixel blocks in an input im...
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08/26/2021
Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference
Adversarial reprogramming allows repurposing a machine-learning model to...
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06/30/2021
The Threat of Offensive AI to Organizations
AI has provided us with the ability to automate tasks, extract informati...
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06/18/2021
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Evaluating robustness of machine-learning models to adversarial examples...
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06/14/2021
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
Backdoor attacks inject poisoning samples during training, with the goal...
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05/02/2021
Intriguing Usage of Applicability Domain: Lessons from Cheminformatics Applied to Adversarial Learning
Defending machine learning models from adversarial attacks is still a ch...
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03/23/2021
The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?
One of the most concerning threats for modern AI systems is data poisoni...
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06/06/2020
Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?
Adversarial attacks on machine learning-based classifiers, along with de...
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05/04/2020
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Machine-learning algorithms trained on features extracted from static co...
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12/20/2019
secml: A Python Library for Secure and Explainable Machine Learning
We present secml, an open-source Python library for secure and explainab...
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10/01/2019
Deep Neural Rejection against Adversarial Examples
Despite the impressive performances reported by deep neural networks in ...
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09/08/2018
On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks
Transferability captures the ability of an attack against a machine-lear...
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03/12/2018
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Machine-learning methods have already been exploited as useful tools for...
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12/17/2017
Super-sparse Learning in Similarity Spaces
In several applications, input samples are more naturally represented in...
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08/23/2017