Poisoning Efficiency

Poisoning efficiency in machine learning focuses on minimizing the amount of malicious data needed to compromise a model's accuracy or introduce backdoors. Current research explores optimizing poison generation using techniques like guided diffusion and adaptive poisoning strategies, investigating the impact of data selection methods on attack success, and developing defenses that leverage robust training or self-supervised learning. Understanding and mitigating poisoning efficiency is crucial for ensuring the security and reliability of machine learning models across various applications, from image recognition to sensitive data analysis.