Definition
Adversarial attacks exploit the vulnerabilities of neural networks by introducing subtle noise to inputs, such as images or text, which causes significant errors in model output. These attacks highlight the fragility of deep learning systems and raise critical safety concerns. They are categorized into white-box attacks, where the attacker has full knowledge of the model, and black-box attacks, where only input-output pairs are observable. Defending against these attacks is essential for deploying robust AI in security-sensitive applications like autonomous driving and facial recognition.
Summary
An adversarial attack is a technique where small, often imperceptible perturbations are added to input data to deceive machine learning models into making incorrect predictions.
Key Concepts
- Perturbation
- Model Robustness
- White-box vs Black-box
- Evasion Attacks
Use Cases
- Testing model security
- Generating adversarial examples for training
- Analyzing vulnerability in computer vision systems