Adversarial machine learning (AML) is a branch of machine learning that focuses on training models so that they are resistant to adversarial attacks. In the context of AML, an adversarial attack is a deliberate perturbation to the input data that causes the machine learning model to produce an incorrect or unwanted output.
AML focuses on developing machine learning models that can detect and resist these adversarial attacks, which can be critical in security applications such as fraud detection in financial transactions, facial recognition and intrusion detection in computer networks.
Adversarial attacks can be classified into different types, such as perturbation attacks, where small modifications are added to the input data to fool the model, or injection attacks, where malicious data is inserted into the input.
To combat these attacks, AML models are trained with input data containing adversarial perturbations. This helps the model learn to recognise and resist such attacks in the future. Techniques such as data masking, anomaly detection and model aggregation are also used to improve model resilience.
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