Increasing Neural-Based Pedestrian Detectors' Robustness to Adversarial Patch Attacks Using Anomaly...
Increasing Neural-Based Pedestrian Detectors' Robustness to Adversarial Patch Attacks Using Anomaly Localization
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Publisher
Switzerland: MDPI AG
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Language
English
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Switzerland: MDPI AG
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Object detection in images is a fundamental component of many safety-critical systems, such as autonomous driving, video surveillance systems, and robotics. Adversarial patch attacks, being easily implemented in the real world, provide effective counteraction to object detection by state-of-the-art neural-based detectors. It poses a serious danger...
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Increasing Neural-Based Pedestrian Detectors' Robustness to Adversarial Patch Attacks Using Anomaly Localization
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TN_cdi_doaj_primary_oai_doaj_org_article_61005d9ff6ef484c898a153beb0efb5d
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https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_61005d9ff6ef484c898a153beb0efb5d
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ISSN
2313-433X
E-ISSN
2313-433X
DOI
10.3390/jimaging11010026
