What is an adversarial attack in the context of AI systems?

An adversarial attack refers to a manipulation of input data specifically designed to deceive or compromise the integrity of an AI model. These attacks are often subtle and can involve slightly altering input data in ways that are imperceptible to humans, yet lead the AI system to make incorrect predictions or classifications. This type of attack is of significant concern in the field of AI and machine learning because it highlights vulnerabilities in AI systems, indicating that they can be tricked by unexpectedly crafted input.

Understanding adversarial attacks is crucial for developing robust AI systems that can withstand such vulnerabilities. In practice, recognizing the potential for these manipulations enables AI practitioners to design safer algorithms and incorporate mechanisms for detecting and mitigating these kinds of threats.

The other options refer to enhancing AI performance or improving data security, which do not align with the core definition of adversarial attacks. While feedback, speed enhancement, and data security strategies are important aspects of AI governance, they do not address the specific challenge posed by adversarial manipulations.