Adversarial Machine Learning involves the manipulation of AI models by introducing carefully crafted input data to deceive or confuse them. These adversarial inputs are designed to exploit vulnerabilities, leading the model to make erroneous predictions or classifications.
Types of adversarial attacks
- Poisoning: manipulates the data before training.
- Evasion: manipulates the model to make false predictions.
- Model Stealing: manipulates the model to learn about the model or data.
Even though adversarial attacks have been uncommon thus far, Adversarial Machine Learning still poses great risks. As machine-learning is being implemented in a wide range of sectors, an attack can create bigger vulnerabilities in the future. For example, an attack would be especially dangerous if it occurred in technology like self-driving cars.
Adversarial Machine Learning unveils a world where algorithms learn not only from data, but from their own vulnerabilities, evolving to become more resilient.