Adversarial Attacks on AI

An Evasion Attack is the most common type of adversarial manipulation. It occurs at inference time, meaning the model is already trained and running in production. By making tiny, pixel-level changes to an input (changes that are often invisible to the human eye), an attacker can shift the input across the model's 'Decision Boundary'. A clean image of a stop sign might be correctly classified, but adding a specific pattern of 'noise' can trick the AI into seeing a speed limit sign, creating a critical safety failure in autonomous systems.

The best defense against adversarial attacks is Adversarial Training. This involves generating adversarial examples using techniques like the Fast Gradient Sign Method (FGSM) and including them in the training dataset. By forcing the model to correctly classify these 'tricky' examples during training, you smooth out its decision boundaries and make it much harder for attackers to find gaps. This process, combined with Input Sanitization (removing high-frequency noise), creates a layered defense for enterprise AI deployments.