Adversarial Attacks

Adversarial attacks exploit vulnerabilities in AI models by introducing carefully designed perturbations to input data that can cause the model to make mistakes while appearing normal to human observers. These attacks can be white-box (with knowledge of model architecture) or black-box (without such knowledge) and can target either training or inference phases.

Testing autonomous vehicle perception systems Evaluating facial recognition security Assessing medical diagnosis model reliability Protecting against fraud in financial systems