Identity Preserving

Identity-preserving techniques aim to manipulate images or videos while maintaining the identity of depicted individuals, crucial for applications like face recognition security, personalized image generation, and video editing. Current research focuses on developing novel algorithms and model architectures, often based on diffusion models and generative adversarial networks (GANs), to achieve high-fidelity identity preservation during various image manipulations, including morphing, inpainting, and style transfer. This field is significant for advancing privacy-preserving technologies, improving the realism of image and video editing tools, and enhancing the robustness of biometric systems against adversarial attacks.