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Titel:

Traffic Sign Classifiers Under Physical World Realistic Sticker Occlusions: A Cross Analysis Study

Dokumenttyp:
Konferenzbeitrag
Autor(en):
Bayzidi, Yasin; Smajic, Alen; Hüger, Fabian; Moritz, Ruby; Varghese, Serin; Schlicht, Peter; Knoll, Alois
Abstract:
Recent adversarial attacks with real world applications are capable of deceiving deep neural networks (DNN), which often appear as printed stickers applied to objects in physical world. Though achieving high success rate in lab tests and limited field tests, such attacks have not been tested on multiple DNN architectures with a standard setup to unveil the common robustness and weakness points of both the DNNs and the attacks. Furthermore, realistic looking stickers applied by normal people as a...     »
Stichworte:
Machine Learning, AI, Safety, Traffic Sign
Kongress- / Buchtitel:
33rd IEEE Intelligent Vehicles Symposium (IV)
Verlagsort:
Aachen
Jahr:
2022
Monat:
Jun
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