Unmasking Adversarial Attacks: Improving Model Robustness скачать в хорошем качестве

Unmasking Adversarial Attacks: Improving Model Robustness

An adversarial attack is a technique that uses malicious input to trick or mislead a machine-learning model. Adversarial robustness is the way that classifiers can be developed to be robust against perturbations of their inputs by an adversary intent on fooling the classifier. For more details, refer to the following reference E. Abdukhamidov, M. Abuhamad, S. S. Woo, E. Chan-Tin and T. Abuhmed, "Hardening Interpretable Deep Learning Systems: Investigating Adversarial Threats and Defenses," in IEEE Transactions on Dependable and Secure Computing, doi: 10.1109/TDSC.2023.3341090