Generating Potent Poisons and Backdoors from Scratch with Guided Diffusion

Understand

Modern neural networks are often trained on massive datasets that are web scraped with minimal human inspection.

• As a result of this insecure curation pipeline, an adversary can poison or backdoor the resulting model by uploading malicious data to the internet and waiting for a victim to scrape and train on it.
• Existing approaches for creating poisons and backdoors start with randomly sampled clean data, called base samples, and then modify those samples to craft poisons.
• However, some base samples may be significantly more amenable to poisoning than others.