Adversarial Robustness in Machine Learning

Adversarial Robustness in Machine Learning is a research topic within Artificial Intelligence. Science Explorer counts 19k research works in it since 1964. 9.8% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on the robustness of deep learning models against adversarial attacks, exploring topics such as adversarial examples, security, uncertainty estimation, defenses, and verification. It delves into the challenges and potential solutions for ensuring the resilience of neural networks in the face of malicious inputs.

• Adversarial Examples
• Deep Learning
• Robustness
• Neural Networks
• Machine Learning
• Security
• Uncertainty Estimation
• Defenses
• Attack
• Verification