Robustness of 3D deep learning in an adversarial setting

Robustness of 3D deep learning in an adversarial setting

Understanding the spatial arrangement and nature of real-world objects is of paramount importance to many complex engineering tasks, including autonomous navigation. Deep learning has revolutionized state-of-the-art performance for tasks in 3D environments; however, relatively little is known about...

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Bibliografiske detaljer
Main Authors:	Wicker, M, Kwiatkowska, M
Format:	Conference item
Sprog:	English
Udgivet:	IEEE 2020

Lignende værker

Adversarial robustness certification for Bayesian neural networks
af: Wicker, M, et al.
Udgivet: (2024)

Bayesian inference with certifiable adversarial robustness
af: Wicker, M, et al.
Udgivet: (2021)

Adversarial robustness of Bayesian neural networks
af: Wicker, M
Udgivet: (2021)

Adversarial robustness of deep reinforcement learning
af: Qu, Xinghua
Udgivet: (2022)

Adversarial robustness guarantees for Gaussian processes
af: Patane, A, et al.
Udgivet: (2022)

Investigating robustness of deep learning against adversarial examples
af: Chua, Shan Jing
Udgivet: (2019)

Adversarial robustness guarantees for classification with Gaussian Processes
af: Blaas, A, et al.
Udgivet: (2020)

Safety and robustness for deep learning with provable guarantees (keynote)
af: Kwiatkowska, M
Udgivet: (2019)

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
af: Everett, Michael F, et al.
Udgivet: (2021)

Certifiable Robustness to Adversarial State Uncertainty in Deep Reinforcement Learning
af: Everett, Michael, et al.
Udgivet: (2021)

Adversarial Robustness for Deep Learning-Based Wildfire Prediction Models
af: Ryo Ide, et al.
Udgivet: (2025-01-01)

Enhancing adversarial robustness of deep neural networks
af: Zhang, Jeffrey,M. Eng.Massachusetts Institute of Technology.
Udgivet: (2019)

ARGAN: Adversarially Robust Generative Adversarial Networks for Deep Neural Networks Against Adversarial Examples
af: Seok-Hwan Choi, et al.
Udgivet: (2022-01-01)

Multiexpert Adversarial Regularization for Robust and Data-Efficient Deep Supervised Learning
af: Behnam Gholami, et al.
Udgivet: (2022-01-01)

Adversarial Robustness of Deep Reinforcement Learning Based Dynamic Recommender Systems
af: Siyu Wang, et al.
Udgivet: (2022-05-01)

On the role of deep learning model complexity in adversarial robustness for medical images
af: David Rodriguez, et al.
Udgivet: (2022-06-01)

Safety and robustness for deep learning with provable guarantees (invited paper - keynote)
af: Kwiatkowska, M
Udgivet: (2020)

The Adversarial Robust and Generalizable Stereo Matching for Infrared Binocular Based on Deep Learning
af: Bowen Liu, et al.
Udgivet: (2024-10-01)

Multi-Task Learning With Self-Defined Tasks for Adversarial Robustness of Deep Networks
af: Changhun Hyun, et al.
Udgivet: (2024-01-01)

Adversarial Robust and Explainable Network Intrusion Detection Systems Based on Deep Learning
af: Kudzai Sauka, et al.
Udgivet: (2022-06-01)

Robustness of Deep Learning-Based Specific Emitter Identification under Adversarial Attacks
af: Liting Sun, et al.
Udgivet: (2022-10-01)

Adversarial robustness of deep learning models : an error-correcting codes based approach
af: Gupta, Samarth (computation scientist)
Udgivet: (2021)

Towards deep neural networks robust to adversarial examples
af: Matyasko, Alexander
Udgivet: (2020)

Adversarial Robustness Guarantees for Random Deep Neural Networks
af: De Palma, Giacomo, et al.
Udgivet: (2022)

Adversarial Robustness Enhancement for Deep Learning-Based Soft Sensors: An Adversarial Training Strategy Using Historical Gradients and Domain Adaptation
af: Runyuan Guo, et al.
Udgivet: (2024-06-01)

A study of natural robustness of deep reinforcement learning algorithms towards adversarial perturbations
af: Qisai Liu, et al.
Udgivet: (2024-01-01)

Adversarial Robustness of Deep Learning-Based Malware Detectors via (De)Randomized Smoothing
af: Daniel Gibert, et al.
Udgivet: (2024-01-01)

Deep Learning Based Robust Text Classification Method via Virtual Adversarial Training
af: Wei Zhang, et al.
Udgivet: (2020-01-01)

On the adversarial robustness of Bayesian machine learning models
af: Blaas, AC
Udgivet: (2021)

Improved methodology for evaluating adversarial robustness in deep neural networks
af: Lee, Kyungmi,(Computer scientist)Massachusetts Institute of Technology.
Udgivet: (2020)

Adversarial attacks and adversarial robustness in computational pathology
af: Narmin Ghaffari Laleh, et al.
Udgivet: (2022-09-01)

Exploring the vulnerabilities and enhancing the adversarial robustness of deep neural networks
af: Bai, Tao
Udgivet: (2022)

Benchmarking adversarially robust quantum machine learning at scale
af: Maxwell T. West, et al.
Udgivet: (2023-06-01)

Adversarial attacks on deep learning
af: Yee, An Qi
Udgivet: (2023)

Robustness guarantees for deep neural networks on videos
af: Kwiatkowska, M, et al.
Udgivet: (2020)

Evaluating the Robustness of Deep Learning Models against Adversarial Attacks: An Analysis with FGSM, PGD and CW
af: William Villegas-Ch, et al.
Udgivet: (2024-01-01)

Feature-guided black-box safety testing of deep neural networks
af: Wicker, M, et al.
Udgivet: (2018)

Adversarial Representation Learning for Robust Privacy Preservation in Audio
af: Shayan Gharib, et al.
Udgivet: (2024-01-01)

Robust and Scalable Multiagent Reinforcement Learning in Adversarial Scenarios
af: Shen, Macheng
Udgivet: (2022)

Not So Robust after All: Evaluating the Robustness of Deep Neural Networks to Unseen Adversarial Attacks
af: Roman Garaev, et al.
Udgivet: (2024-04-01)