Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging-Reference-Cited by-同舟云学术

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Achieving Adversarial Robustness in Deep Learning-Based Overhead Imaging

Published:2022-10-11 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)
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Author:

Braun Dagen¹,Reisman Matthew²,Dewell Larry³,Banburski-Fahey Andrzej⁴,Deza Arturo⁴,Poggio Tomaso⁴

Affiliation:

1. Lockheed Martin Space,Optical Payload Center of Excellence,Sunnyvale,California

2. Lockheed Martin Space,Optical Payload Center of Excellence,Waterton,Colorado

3. Lockheed Martin Space,Advanced Technology Center,Palo Alto,California

4. Massachusetts Institute of Technology,Center for Brains, Minds, and Machines,Cambridge,Massachusetts

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10091955/10092198/10092213.pdf?arnumber=10092213

Reference19 articles.

1. Satellite image classification with deep learning;pritt,2020

2. Planet dump;contributors,2017

3. Foolbox Native: Fast adversarial attacks to benchmark the robustness of machine learning models in PyTorch, TensorFlow, and JAX

4. An adversarial approach for explaining the predictions of deep neural networks;rahnama,2020

5. Simple black-box adversarial perturbations for deep networks;narodytska,2016

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1. Edge Conductivity in PtSe₂ Nanostructures;Small Structures;2023-11-12

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