Dual Domain Anti Personalization

Dual-domain anti-personalization focuses on mitigating the risks of personalized AI models, particularly in image generation and federated learning, by disrupting the generation of individually targeted content or models. Current research emphasizes techniques like adversarial perturbations in both spatial and frequency domains for image generation, and the development of personalized federated learning algorithms using stacked generalization, disentangled latent representations, and transformer-based architectures with personalized self-attention. These advancements aim to enhance privacy protection and improve the robustness of AI systems against malicious use while maintaining model utility and efficiency.