Corruption Emulation

Corruption emulation in machine learning focuses on simulating various data corruptions—from bit flips in hardware to noise and artifacts in images and videos—to assess and improve model robustness. Current research employs diverse techniques, including explainable AI for root-cause analysis of prediction anomalies, diffusion models for synthetic data generation and restoration, and data augmentation strategies informed by synthetic corruptions. This work is crucial for building reliable AI systems, particularly in safety-critical applications like medical imaging and autonomous vehicles, by identifying vulnerabilities and developing more resilient models.