Adversarial Attacks on AI Systems
Understanding and defending against techniques that manipulate AI behavior
Module 1: Adversarial Attacks
Learn how attackers can manipulate AI systems through carefully crafted inputs and how to defend against these threats
Adversarial attacks exploit the fundamental limitations in how AI systems process and interpret data. These attacks involve creating inputs that appear normal to humans but cause AI systems to behave in unexpected or incorrect ways.
The core insight behind adversarial attacks is that most AI models, particularly deep neural networks, are highly sensitive to specific patterns in their input space. By carefully modifying inputs to emphasize or suppress these patterns, attackers can manipulate the model's behavior without making changes that would be obvious to human observers.
The Adversarial Gap
"The gap between human and machine perception creates a vulnerability surface that adversaries can exploit. What appears as random noise to us may contain precisely crafted signals that dramatically alter an AI's behavior."
Key Characteristics of Adversarial Attacks
- Transferability: Attacks created for one model often work against other models trained on similar data
- Imperceptibility: Many attacks involve changes that are subtle or invisible to humans
- Targeted vs. Untargeted: Attacks can aim for specific incorrect outputs or simply any incorrect output
- White-box vs. Black-box: Attacks can be developed with full knowledge of the model or with limited information
- Physical World Attacks: Some attacks work even when implemented in the physical world (e.g., adversarial patches on traffic signs)