stp2y

Adversarial attacks manipulate ML model predictions, steal models, or extract data. Different attack types exist, including evasion, data poisoning, Byzantine, and model extraction attacks. Defense strategies like adversarial learning, monitoring, defensive distillation, and differential privacy improve robustness against adversarial attacks. Multiple aspects have to be considered when evaluating the effectiveness of different defense strategies, including the method’s robustness, impact on model performance, and adaptability to the constant flow of brand-new attack mechanisms. The growing prevalence of ML models in business-critical applications results in an increased incentive for malicious actors to attack the models for their benefit. Developing robust defense strategies…