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Found 20 papers, 2 papers with code
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact on testing procedures, focusing on some of the essential requirements for trustworthy AI.
Manipulating Trajectory Prediction with Backdoors
In this paper, we focus on backdoors - a security threat acknowledged in other fields but so far overlooked for trajectory prediction.
Towards more Practical Threat Models in Artificial Intelligence Security
Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and security risks of AI.
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experience.
Machine Learning Security in Industry: A Quantitative Survey
Despite the large body of academic work on machine learning security, little is known about the occurrence of attacks on machine learning systems in the wild.
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning
In this survey, we provide a comprehensive systematization of poisoning attacks and defenses in machine learning, reviewing more than 100 papers published in the field in the last 15 years.
Machine Learning Security against Data Poisoning: Are We There Yet?
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.
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
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 test time.
Mental Models of Adversarial Machine Learning
Our study reveals two \facets of practitioners' mental models of machine learning security.
Adversarial Examples and Metrics
In this work we study the limitations of robust classification if the target metric is uncertain.
How many winning tickets are there in one DNN?
The recent lottery ticket hypothesis proposes that there is one sub-network that matches the accuracy of the original network when trained in isolation.
Backdoor Smoothing: Demystifying Backdoor Attacks on Deep Neural Networks
Backdoor attacks mislead machine-learning models to output an attacker-specified class when presented a specific trigger at test time.
Adversarial Vulnerability Bounds for Gaussian Process Classification
To protect against this we devise an adversarial bound (AB) for a Gaussian process classifier, that holds for the entire input domain, bounding the potential for any future adversarial method to cause such misclassification.
On the security relevance of weights in deep learning
Recently, a weight-based attack on stochastic gradient descent inducing overfitting has been proposed.
The Limitations of Model Uncertainty in Adversarial Settings
Machine learning models are vulnerable to adversarial examples: minor perturbations to input samples intended to deliberately cause misclassification.
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
In this paper, we propose MLCapsule, a guarded offline deployment of machine learning as a service.
Killing four birds with one Gaussian process: the relation between different test-time attacks
For example, we are able to secure GPC against empirical membership inference by proper configuration.
How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models
In this paper, we leverage Gaussian Processes to investigate adversarial examples in the framework of Bayesian inference.
On the (Statistical) Detection of Adversarial Examples
Specifically, we augment our ML model with an additional output, in which the model is trained to classify all adversarial inputs.
Adversarial Perturbations Against Deep Neural Networks for Malware Classification
Deep neural networks, like many other machine learning models, have recently been shown to lack robustness against adversarially crafted inputs.
