التفاصيل البيبلوغرافية
| العنوان: |
Video_1_Adversarial attacks on spiking convolutional neural networks for event-based vision.MP4 |
| المؤلفون: |
Julian Büchel (14292209), Gregor Lenz (9173969), Yalun Hu (2873021), Sadique Sheik (5696735), Martino Sorbaro (8336109) |
| سنة النشر: |
2022 |
| مصطلحات موضوعية: |
Neuroscience, Biological Engineering, Developmental Biology, Stem Cells, Artificial Intelligence and Image Processing, Endocrinology, Radiology and Organ Imaging, Autonomic Nervous System, Cellular Nervous System, Central Nervous System, Sensory Systems, Clinical Nursing: Tertiary (Rehabilitative), Decision Making, Rehabilitation Engineering, Biomedical Engineering not elsewhere classified, Signal Processing, Neurogenetics, Image Processing, spiking convolutional neural networks, adversarial examples, neuromorphic engineering, robust AI, dynamic vision sensors |
| الوصف: |
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications. Convolutional spiking neural networks model such event-based data and develop their full energy-saving potential when deployed on asynchronous neuromorphic hardware. Event-based vision being a nascent field, the sensitivity of spiking neural networks to potentially malicious adversarial attacks has received little attention so far. We show how white-box adversarial attack algorithms can be adapted to the discrete and sparse nature of event-based visual data, and demonstrate smaller perturbation magnitudes at higher success rates than the current state-of-the-art algorithms. For the first time, we also verify the effectiveness of these perturbations directly on neuromorphic hardware. Finally, we discuss the properties of the resulting perturbations, the effect of adversarial training as a defense strategy, and future directions. |
| نوع الوثيقة: |
dataset |
| اللغة: |
unknown |
| Relation: |
https://figshare.com/articles/media/Video_1_Adversarial_attacks_on_spiking_convolutional_neural_networks_for_event-based_vision_MP4/21780953 |
| DOI: |
10.3389/fnins.2022.1068193.s002 |
| الاتاحة: |
https://doi.org/10.3389/fnins.2022.1068193.s002 |
| Rights: |
CC BY 4.0 |
| رقم الانضمام: |
edsbas.C1A9CCB8 |
| قاعدة البيانات: |
BASE |