Adatmérgezés

Adatmérgezés, often translated as data poisoning, is a type of attack on machine learning models where malicious actors inject corrupted or misleading data into the training dataset. The goal is to compromise the integrity and performance of the resulting model. This can manifest in several ways, such as causing the model to misclassify specific inputs, reducing its overall accuracy, or even making it completely unusable.

The effectiveness of adatmérgezés depends on the attacker's knowledge of the model's architecture, training process, and the specific data it is trained on. In some cases, a small percentage of poisoned data can significantly degrade model performance. For example, an attacker might aim to make a spam filter misclassify legitimate emails as spam, or to cause a facial recognition system to fail to identify certain individuals.

Preventing adatmérgezés involves robust data validation and sanitization techniques. This can include anomaly detection to identify suspicious data points, rigorous data source verification, and implementing secure data pipelines. Techniques like differential privacy can also help to make models more resilient to poisoned data by adding noise to the training process. As machine learning becomes more prevalent, understanding and mitigating adatmérgezés is crucial for ensuring the reliability and trustworthiness of AI systems.