Projects by Tag: Adversarial Analysis for Robust ML Security and Threat Modeling
Explore projects tagged "adversarial-analysis" to discover open-source adversarial robustness tools, attack/defense implementations, and threat-modeling research that evaluate and harden ML systems against adversarial examples. This curated list of projects shows how teams implement adversarial training pipelines, gradient-based attacks (FGSM, PGD), certified robustness methods, robustness evaluation and benchmarking for neural networks, and runtime model hardening techniques. Use the filtering UI to narrow by technique, framework (PyTorch, TensorFlow), dataset, license, and maturity; compare benchmark results, reproduce experiments, review code and metrics, and adopt best practices for secure model deployment. Filter, sort, and explore these projects to accelerate adoption, contribute code, or integrate adversarial-analysis tools into your ML security workflow.
