The video explains poisoning attacks in AI, where attackers inject malicious data into training sets. It uses the Adversarial Robustness Toolbox (ART) by IBM to demonstrate a poisoning attack on a digit recognition system. The demo shows how poisoned data can alter AI predictions, such as recognizing a zero as a one. To counteract this, the video introduces defense methods like clustering and PCA to detect poisoned images. The video concludes with a brief mention of upcoming content on privacy attacks.

Describe the process of how poisoned data can affect a classifier's predictions.

What methods can be applied to detect poisoned images?