Adversarial-Machine-Learning

We demonstrate that universal adversarial triggers can control both the topic and stance of GPT-2’s generated text, revealing security vulnerabilities in deployed language models and proposing constructive applications for bias auditing.

An in-depth guide to GANs: how two neural networks compete to generate realistic data, the math behind it, and the evolution of objective functions that stabilize training.