Adversarial machine learning is a technique used within the field of machine learning that aims to deceive or confuse models through malicious input. This can be done by adding specific ‘noise’ or alterations to the original input data, making subtle changes that are often imperceptible to humans but can cause a machine learning model to misclassify an object.
The process often involves understanding the model, crafting adversarial examples based on that understanding, and then using those examples to test the model’s vulnerability.
Adversarial machine learning can be used both offensively, to attack models, and defensively, to make models more robust against these kinds of attacks. In a defensive context, techniques can be developed to detect and mitigate the impact of adversarial examples.
Adversarial machine learning has applications in many areas, including cybersecurity, where it can both expose vulnerabilities and be used to enhance the protection of systems. Understanding and defending against adversarial attacks is an active area of research in the machine learning community.