Abstract: Techniques are disclosed relating to training a machine learning model to handle adversarial attacks. In some embodiments, a computer system perturbs, using a set of adversarial attack methods, a set of training examples used to train a machine learning model. In some embodiments, the computer system identifies, from among the perturbed set of training examples, a set of sparse perturbed training examples that are usable to train machine learning models to identify adversarial attacks, where the set of sparse perturbed training examples includes examples whose perturbations are below a perturbation threshold and whose classifications satisfy a classification difference threshold. In some embodiments, the computer system retrains, using the set of sparse perturbed training examples, the machine learning model. The disclosed techniques may advantageously enable a machine learning model to correctly classify data associated with adversarial attacks.

Abstract: Techniques are disclosed for automatically generating and updating a control group. In disclosed techniques, a server computer system trains, using a plurality of transactions, a machine learning model. During training the machine learning model learns a feature distribution of both a current set of control group (CG) transactions and a current set of non-control group (non-CG) transactions included in the plurality of transactions. The system inputs the current set of CG transactions into the trained machine learning model. Based on the output of the trained machine learning model for the current set of CG transactions, the system modifies the current set of CG transactions to generate an updated set of CG transactions. Based on the updated set of CG transactions, the server performs one or more preventative measures for a transaction processing system. The disclosed techniques may advantageously improve the accuracy e.g., of a transaction processing system.

Abstract: Techniques are disclosed relating to training a machine learning model to handle adversarial attacks. In some embodiments, a computer system perturbs, using a set of adversarial attack methods, a set of training examples used to train a machine learning model. In some embodiments, the computer system identifies, from among the perturbed set of training examples, a set of sparse perturbed training examples that are usable to train machine learning models to identify adversarial attacks, where the set of sparse perturbed training examples includes examples whose perturbations are below a perturbation threshold and whose classifications satisfy a classification difference threshold. In some embodiments, the computer system retrains, using the set of sparse perturbed training examples, the machine learning model. The disclosed techniques may advantageously enable a machine learning model to correctly classify data associated with adversarial attacks.