Familiarity-Based Open-Set Recognition Under Adversarial Attacks
Research output: Working paper › Preprint › Research
Documents
- Familiarity-Based
Final published version, 2.06 MB, PDF document
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.
Original language | English |
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Publisher | arXiv.org |
Number of pages | 5 |
Publication status | Published - 2023 |
Links
- https://arxiv.org/abs/2311.05006
Final published version
ID: 384869429