What are the differences between adversarial attacks and regular attacks in AI?

What are the differences between adversarial attacks and regular attacks in AI?

Adversarial attacks are specifically designed to deceive AI models by introducing subtle perturbations, while regular attacks may not target AI systems directly.

Can you give me an example of an adversarial attack?

Sure! An example is adding noise to an image so that an AI misclassifies it, like changing a cat image to be recognized as a dog.

How do regular attacks differ in their approach?

Regular attacks might involve exploiting vulnerabilities in software or systems without targeting AI, like phishing or DDoS attacks.

Are adversarial attacks more dangerous than regular attacks?

It depends on the context. Adversarial attacks can be particularly dangerous in critical applications like self-driving cars or security systems.

What measures can be taken to defend against adversarial attacks?

Defenses include adversarial training, input preprocessing, and using robust models that are less sensitive to perturbations.