Purification Model

Purification models aim to remove unwanted noise or adversarial perturbations from data, enhancing the robustness and reliability of machine learning models. Current research focuses on developing efficient and effective purification methods using diverse architectures, including diffusion models, energy-based models, and ensemble approaches, often tailored to specific applications like image classification, speaker verification, and chatbot safety. These advancements are crucial for improving the security and trustworthiness of AI systems, mitigating the impact of data poisoning attacks and adversarial examples, and enabling more reliable performance in real-world scenarios.