adatmegrzést

Adatmérgezés, often translated as data poisoning, is a type of adversarial attack in machine learning where an attacker injects malicious or corrupted data into the training dataset of a machine learning model. The goal of this attack is to manipulate the model's behavior during the training process, leading to its misclassification of future inputs or a degradation of its overall performance. This can manifest in various ways, such as causing the model to repeatedly misidentify specific objects or to become generally unreliable.

The effectiveness of adatmérgezés relies on the fact that machine learning models learn patterns and relationships from the data they are trained on. By subtly altering or introducing false information, attackers can subtly shift these learned patterns, making the model learn incorrect associations. For instance, in an image recognition system, an attacker might add images of cats labeled as dogs to the training set. If sufficient poisoned data is introduced, the model might start classifying cats as dogs.

Defending against adatmérgezés is a significant challenge in machine learning security. Mitigation strategies often involve rigorous data validation, anomaly detection techniques to identify and remove suspicious data points before training, and robust training methods that are less susceptible to outliers. Researchers are continuously developing more sophisticated defenses to protect machine learning models from such manipulation.