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09/20/2023
3D Face Reconstruction: the Road to Forensics
3D face reconstruction algorithms from images and videos are applied to ...
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09/19/2023
Adversarial Attacks Against Uncertainty Quantification
Machine-learning models can be fooled by adversarial examples, i.e., car...
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02/06/2023
Dropout Injection at Test Time for Post Hoc Uncertainty Quantification in Neural Networks
Among Bayesian methods, Monte-Carlo dropout provides principled tools fo...
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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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07/12/2022
Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
While machine learning is vulnerable to adversarial examples, it still l...
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06/01/2022
Support Vector Machines under Adversarial Label Contamination
Machine learning algorithms are increasingly being applied in security-r...
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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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10/20/2021
Fingerprint recognition with embedded presentation attacks detection: are we ready?
The diffusion of fingerprint verification systems for security applicati...
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10/12/2021
Can machines learn to see without visual databases?
This paper sustains the position that the time has come for thinking of ...
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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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06/18/2021
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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02/25/2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Evaluating adversarial robustness amounts to finding the minimum perturb...
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10/13/2020
Detecting Anomalies from Video-Sequences: a Novel Descriptor
We present a novel descriptor for crowd behavior analysis and anomaly de...
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08/17/2020
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection
Recent work has shown that adversarial Windows malware samples - also re...
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07/07/2020
Are spoofs from latent fingerprints a real threat for the best state-of-art liveness detectors?
We investigated the threat level of realistic attacks using latent finge...
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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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05/25/2020
Adversarial Feature Selection against Evasion Attacks
Pattern recognition and machine learning techniques have been increasing...
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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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03/30/2020
Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations
Windows malware detectors based on machine learning are vulnerable to ad...
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11/28/2019
A novel classification-selection approach for the self updating of template-based face recognition systems
The boosting on the need of security notably increased the amount of pos...
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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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09/16/2019
Towards Quality Assurance of Software Product Lines with Adversarial Configurations
Software product line (SPL) engineers put a lot of effort to ensure that...
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03/27/2019
3D Face Mask Presentation Attack Detection Based on Intrinsic Image Analysis
Face presentation attacks have become a major threat to face recognition...
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01/11/2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Recent work has shown that deep-learning algorithms for malware detectio...
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11/25/2018
Poisoning Behavioral Malware Clustering
Clustering algorithms have become a popular tool in computer security to...
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11/25/2018
Is Data Clustering in Adversarial Settings Secure?
Clustering algorithms have been increasingly adopted in security applica...
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10/31/2018
Face Presentation Attack Detection in Learned Color-liked Space
Face presentation attack detection (PAD) has become a thorny problem for...
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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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05/30/2018
Towards Adversarial Configurations for Software Product Lines
Ensuring that all supposedly valid configurations of a software product ...
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04/21/2018
Is feature selection secure against training data poisoning?
Learning in adversarial settings is becoming an important task for appli...
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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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03/09/2018
Explaining Black-box Android Malware Detection
Machine-learning models have been recently used for detecting malicious ...
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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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12/08/2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have demons...
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08/23/2017
Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid
Deep neural networks have been widely adopted in recent years, exhibitin...
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09/06/2016
Review of the Fingerprint Liveness Detection (LivDet) competition series: 2009 to 2015
A spoof attack, a subset of presentation attacks, is the use of an artif...
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09/06/2016
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
Prior work has shown that multibiometric systems are vulnerable to prese...
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05/12/2011
