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Adversarial Robustness
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Adversarial Robustness evaluates the vulnerabilities of machine learning models under various types of adversarial attacks.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach
Tail-aware Adversarial Attacks: A Distributional Approach to Efficient LLM Jailbreaking
Rectifying Adversarial Sample with Low Entropy Prior for Test-Time Defense
Evaluating the Evaluators: Trust in Adversarial Robustness Tests
Is Reasoning All You Need? Probing Bias in the Age of Reasoning Language Models
NIC-RobustBench: A Comprehensive Open-Source Toolkit for Neural Image Compression and Robustness Analysis
PRISON: Unmasking the Criminal Potential of Large Language Models
NAP-Tuning: Neural Augmented Prompt Tuning for Adversarially Robust Vision-Language Models
Intriguing Frequency Interpretation of Adversarial Robustness for CNNs and ViTs
Canonical Latent Representations in Conditional Diffusion Models
The interplay of robustness and generalization in quantum machine learning
Towards Class-wise Fair Adversarial Training via Anti-Bias Soft Label Distillation
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo Anomalies
ProARD: progressive adversarial robustness distillation: provide wide range of robust students
Enhancing Adversarial Robustness with Conformal Prediction: A Framework for Guaranteed Model Reliability
Sylva: Tailoring Personalized Adversarial Defense in Pre-trained Models via Collaborative Fine-tuning
Dynamic Epsilon Scheduling: A Multi-Factor Adaptive Perturbation Budget for Adversarial Training
SafeGenes: Evaluating the Adversarial Robustness of Genomic Foundation Models
Speech Unlearning
The Butterfly Effect in Pathology: Exploring Security in Pathology Foundation Models
On the Scaling of Robustness and Effectiveness in Dense Retrieval
Model Unlearning via Sparse Autoencoder Subspace Guided Projections
A Flat Minima Perspective on Understanding Augmentations and Model Robustness
How Do Diffusion Models Improve Adversarial Robustness?
Are classical deep neural networks weakly adversarially robust?
Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains
Erasing Concepts, Steering Generations: A Comprehensive Survey of Concept Suppression
Your Classifier Can Do More: Towards Bridging the Gaps in Classification, Robustness, and Generation
Multi-Agent Reinforcement Learning in Cybersecurity: From Fundamentals to Applications
Curvature Dynamic Black-box Attack: revisiting adversarial robustness via dynamic curvature estimation
LORE: Lagrangian-Optimized Robust Embeddings for Visual Encoders
Enhancing Adversarial Robustness of Vision Language Models via Adversarial Mixture Prompt Tuning
Experimental robustness benchmark of quantum neural network on a superconducting quantum processor
Few-Shot Adversarial Low-Rank Fine-Tuning of Vision-Language Models
Beyond Classification: Evaluating Diffusion Denoised Smoothing for Security-Utility Trade off
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