# Falsification-Driven Robust Training

> Using adversarial falsification to expand certified robustness radii by 48%.

- Year: 2025
- Period: August–November 2025
- Type: course
- Stack: CARLA, VerifAI, AutoLiRPA, PyTorch
- Guide: Prof. Supratik Chakraborty, IIT Bombay

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- Built a pipeline coupling the CARLA simulator with VerifAI to synthesise
  adversarial examples, then used them to train models that are provably more
  robust.
- Computed certified L2 robustness radii with Alpha-CROWN via AutoLiRPA, which
  sidestepped the VRAM and latency bottlenecks that made standard
  Branch-and-Bound infeasible at this scale.
- Falsification-driven active learning **expanded certified L2 robustness radii
  by 48%** over five training cycles, showing markedly better sample efficiency
  than random sampling.