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...

Full beskrivning

Bibliografiska uppgifter
Huvudupphovsmän:	Wicker, M, Kwiatkowska, M
Materialtyp:	Conference item
Språk:	English
Publicerad:	IEEE 2020

Liknande verk

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

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

Adversarial robustness of Bayesian neural networks
av: Wicker, M
Publicerad: (2021)

Adversarial robustness of deep reinforcement learning
av: Qu, Xinghua
Publicerad: (2022)

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Towards deep neural networks robust to adversarial examples
av: Matyasko, Alexander
Publicerad: (2020)

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

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

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

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

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

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

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

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

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

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

Adversarial attacks on deep learning
av: Yee, An Qi
Publicerad: (2023)

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

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

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

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

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

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