Familiarity-Based Open-Set Recognition Under Adversarial Attacks
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 study 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. Furthermore, we explore how novel and familiar samples react to adversarial attacks and formulate the adversarial reaction score as an alternative OSR scoring rule, which shows a high correlation with the MLS familiarity score.
Code (0)
등록된 구현이 없습니다.
Tasks
Open Set Learningscoring ruleMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods
In many object recognition applications, the set of possible categories is an open set, and the deployed recognition system will encounter novel objects belonging to categories unseen during training. Detecting such "nov…
Anomaly DetectionObjectObject RecognitionOutlier Detection+1Who is Real Bob? Adversarial Attacks on Speaker Recognition Systems
Speaker recognition (SR) is widely used in our daily life as a biometric authentication or identification mechanism. The popularity of SR brings in serious security concerns, as demonstrated by recent adversarial attacks…
Adversarial AttackSpeaker Recognitionspeech-recognitionSpeech RecognitionFACESEC: A Fine-grained Robustness Evaluation Framework for Face Recognition Systems
We present FACESEC, a framework for fine-grained robustness evaluation of face recognition systems. FACESEC evaluation is performed along four dimensions of adversarial modeling: the nature of perturbation (e.g., pixel-l…
Face RecognitionHow Deep Learning Sees the World: A Survey on Adversarial Attacks & Defenses
Deep Learning is currently used to perform multiple tasks, such as object recognition, face recognition, and natural language processing. However, Deep Neural Networks (DNNs) are vulnerable to perturbations that alter th…
Face RecognitionMalware DetectionObject RecognitionAdversarial Light Projection Attacks on Face Recognition Systems: A Feasibility Study
Deep learning-based systems have been shown to be vulnerable to adversarial attacks in both digital and physical domains. While feasible, digital attacks have limited applicability in attacking deployed systems, includin…
Face Recognition