Safe T2I Generation

It studies how to keep text-to-image systems from producing harmful images without blocking legitimate requests. Approaches include prompt moderation, safety alignment during training, concept erasure, and inference-time steering or localized repair during denoising. Researchers test these methods on safety benchmarks and red-team attacks, including jailbreaks, unsafe combinations of benign concepts, and image-editing settings, while measuring image quality and prompt fidelity. A central challenge is making defenses robust to changing attacks and context-dependent harms without suppressing benign content, with performance varying across model architectures and generation methods.