Sources of risk of AI systems
A Steimers, M Schneider - … Journal of Environmental Research and Public …, 2022 - mdpi.com
Artificial intelligence can be used to realise new types of protective devices and assistance
systems, so their importance for occupational safety and health is continuously increasing …
systems, so their importance for occupational safety and health is continuously increasing …
A state-of-the-art review on adversarial machine learning in image classification
Computer vision applications like traffic monitoring, security checks, self-driving cars,
medical imaging, etc., rely heavily on machine learning models. It raises an essential …
medical imaging, etc., rely heavily on machine learning models. It raises an essential …
Dual-branch sparse self-learning with instance binding augmentation for adversarial detection in remote sensing images
Remote sensing image analysis technology based on neural networks has significantly
facilitated human life. However, adversarial attacks can drastically impair the performance of …
facilitated human life. However, adversarial attacks can drastically impair the performance of …
Robustness with query-efficient adversarial attack using reinforcement learning
A measure of robustness against naturally occurring distortions is key to safety, success, and
trustworthiness of machine learning models on deployment. We propose an adversarial …
trustworthiness of machine learning models on deployment. We propose an adversarial …
Reinforcement learning based black-box adversarial attack for robustness improvement
We propose a Reinforcement Learning (RL) based adversarial Black-box attack (RLAB) that
aims at adding minimum distortion to the input iteratively to deceive image classification …
aims at adding minimum distortion to the input iteratively to deceive image classification …
Toward robust 3d perception for autonomous vehicles: A review of adversarial attacks and countermeasures
At present the perception system of autonomous vehicles is grounded on 3D vision
technologies along with deep learning to process depth information. Although deep learning …
technologies along with deep learning to process depth information. Although deep learning …
[PDF][PDF] Robustness with Black-Box Adversarial Attack using Reinforcement Learning.
A measure of robustness against naturally occurring distortions is key to the safety, success,
and trustworthiness of machine learning models on deployment. We investigate an …
and trustworthiness of machine learning models on deployment. We investigate an …
The co-12 recipe for evaluating interpretable part-prototype image classifiers
Interpretable part-prototype models are computer vision models that are explainable by
design. The models learn prototypical parts and recognise these components in an image …
design. The models learn prototypical parts and recognise these components in an image …
RLUC: Strengthening robustness by attaching constraint considerations to policy network
J Tang, Q Liu, F Li, F Zhu - Expert Systems with Applications, 2024 - Elsevier
Deep reinforcement learning is widely used in many fields. However, recent research has
found vulnerabilities in agents trained by reinforcement learning algorithms and raised …
found vulnerabilities in agents trained by reinforcement learning algorithms and raised …
A Survey on Large Language Models for Critical Societal Domains: Finance, Healthcare, and Law
In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …