Adversarial Attacks on Neural Networks

Adversarial attacks on neural networks are unplanned and skillfully produced inputs that are intended to influence the network’s output or predictions in a negative way. Adversarial attacks represent a serious threat to the security and dependability of neural networks since they can be exploited to deceive or manipulate the system. A purposeful input that aims to influence neural networks’ predictions is called an adversarial attack. The security and dependability of neural networks are seriously threatened by these assaults because they can be exploited to trick or control the system. Adversarial attacks come in a variety of forms, including physical, black-box, gradient-based, and transferability attacks. Several defense mechanisms, such as adversarial training and input sanitization, have been developed to lessen the effects of these attacks.