What is an adversarial attack in the context of machine learning?

In the context of machine learning, an adversarial attack refers to a malicious attempt to manipulate model predictions by introducing deceptive inputs designed to confuse or mislead the model. This type of attack often involves small, intentional perturbations to the input data that can cause machine learning models, especially neural networks, to produce incorrect or unintended outputs.

For example, in image classification tasks, an attacker might slightly alter pixels in an image such that a model misclassifies it while remaining indistinguishable to humans. Understanding adversarial attacks is critical because it highlights vulnerabilities in AI systems and emphasizes the need for robustness and security in machine learning applications. The knowledge of adversarial attacks is crucial for developing defenses and creating more reliable AI technologies.

Other options describe processes related to enhancing model performance or data quality but do not pertain to the malicious actions that define adversarial attacks. Thus, they do not accurately capture the essence of this concept within machine learning.