This research reveals how the flatness of a model's loss surface directly affects its vulnerability to adversarial attacks, providing a novel perspective on AI security.

• During adversarial attacks, model behavior enters an "uncanny valley" where flatness metrics temporarily decrease
• Models with flatter loss surfaces demonstrate better resistance against adversarial examples
• First-order white-box attacks can be detected by monitoring relative flatness metrics
• Findings suggest promising new directions for developing more robust AI systems

For security professionals, this research offers innovative approaches to detect and defend against adversarial examples by analyzing geometric properties of model loss landscapes.

The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective