Watch out! Motion is blurring the vision of your deep neural networks
The state-of-the-art deep neural networks (DNNs) are vulnerable to adversarial examples with additive random noise-like perturbations. While such examples are hardly found in the physical world, the image blurring effect caused by object motion, on the other hand, commonly occurs in practice, making...
Saved in:
Main Authors: | GUO, Qing, JUEFEI-XU, Felix, XIE, Xiaofei, MA, Lei, WANG, Jian, YU, Bing, FENG, Wei, LIU, Yang |
---|---|
Format: | text |
Language: | English |
Published: |
Institutional Knowledge at Singapore Management University
2020
|
Subjects: | |
Online Access: | https://ink.library.smu.edu.sg/sis_research/7108 https://ink.library.smu.edu.sg/context/sis_research/article/8111/viewcontent/NeurIPS_2020_watch_out_motion_is_blurring_the_vision_of_your_deep_neural_networks_Paper.pdf |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Singapore Management University |
Language: | English |
Similar Items
-
DeepHunter: A coverage-guided fuzz testing framework for deep neural networks
by: XIE, Xiaofei, et al.
Published: (2019) -
NPC: Neuron path coverage via characterizing decision logic of deep neural networks
by: XIE, Xiaofei, et al.
Published: (2022) -
DeepRepair: Style-guided repairing for deep neural networks in the real-world operational environment
by: YU, Bing, et al.
Published: (2021) -
DeepMutation++: A mutation testing framework for deep learning systems
by: HU, Qiang, et al.
Published: (2019) -
DeepSonar: Towards effective and robust detection of AI-synthesized fake voices
by: WANG, Run, et al.
Published: (2020)