Familiarity-Based Open-Set Recognition Under Adversarial Attacks
Publikation: Working paper › Preprint › Forskning
Dokumenter
- Familiarity-Based
Forlagets udgivne version, 2,06 MB, PDF-dokument
Open-set recognition (OSR), the identification of novel categories, can be a critical component when deploying classification models in real-world applications. Recent work has shown that familiarity-based scoring rules such as the Maximum Softmax Probability (MSP) or the Maximum Logit Score (MLS) are strong baselines when the closed-set accuracy is high. However, one of the potential weaknesses of familiarity-based OSR are adversarial attacks. Here, we present gradient-based adversarial attacks on familiarity scores for both types of attacks, False Familiarity and False Novelty attacks, and evaluate their effectiveness in informed and uninformed settings on TinyImageNet.
Originalsprog | Engelsk |
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Udgiver | arXiv.org |
Antal sider | 5 |
Status | Udgivet - 2023 |
Links
- https://arxiv.org/abs/2311.05006
Forlagets udgivne version
ID: 384869429