Understanding Adversarial Attacks And Defenses In Ai
Adversarial attacks involve manipulating input data to fool AI models, while defenses are techniques to make AI models more robust against such attacks. This article explores the nature of adversarial attacks, their impact on AI systems, and the various strategies developed to defend against them.
Areas of application
- Cybersecurity
- Autonomous vehicles
- Healthcare and Medical Imaging
- Banking and Finance
- Speech and Text Recognition
- AI-Based Surveillance Systems
- Machine Learning Models
- Neural Networks
Example
For instance, an attacker could manipulate an image of a stop sign to make it appear as a speed limit sign to a self-driving car, causing the car to misinterpret the sign and potentially cause an accident.