An evasion attack is a type of cyber attack targeting artificial intelligence (AI) systems, particularly those involved in classification tasks, such as image recognition or spam detection. The primary goal of an evasion attack is to deceive the AI into making incorrect predictions or classifications by subtly manipulating the input data.
In an evasion attack, the attacker alters the input in a way that is often imperceptible to human observers but significantly impacts the performance of the AI model. For example, in the context of image recognition, an attacker might add noise or modify certain pixels in an image to cause the AI to misclassify it. These alterations are typically designed to exploit vulnerabilities in the model’s training or decision-making processes.
Evasion attacks can have serious implications, especially in fields such as cybersecurity, autonomous vehicles, and finance, where AI systems are heavily relied upon for making critical decisions. By successfully executing an evasion attack, an adversary can bypass security measures, leading to data breaches, financial losses, or even physical harm.
To defend against evasion attacks, researchers and practitioners employ various strategies, including adversarial training, which involves training AI models on both clean and adversarially modified data to improve their robustness. Ongoing research continues to explore ways to strengthen AI systems against these types of attacks, ensuring they remain reliable and secure in real-world applications.