Researchers from Macao Polytechnic University, Guangdong University of Technology, Jinan University, and the Institute of Automation, Chinese Academy of Sciences reported their work in *Machine Intelligence Research* on June 17, 2026, introducing Latent Seal, an encoder-decoder framework for closed-source latent diffusion services. It embeds a customized image watermark during content generation and checks suspicious images by extracting and comparing the recovered mark with the provider's original reference, enabling generative-content detection and copyright verification. The team built Latent Seal around Stable Diffusion 2.1, assembling 74,247 generated images and their latent representations from prompts drawn from DiffusionDB and JourneyDB, using 69,247 images for training and 5,000 for testing. The system freezes the original denoising network, clones and fine-tunes the variational autoencoder decoder, and inserts a latent-space watermark encoder into an intermediate decoding block. A separate decoder learns to recover the target watermark from protected images and return a blank output for unprotected images. During training, an attack layer simulated ten common distortions, including brightness, contrast, saturation changes, blur, noise, compression, flips, cropping, and rotation. In benchmark tests, watermarked images reached a peak signal-to-noise ratio of 44.29 decibels and a structural similarity index of 0.9933, while recovered watermarks achieved 39.19 decibels, 0.9971 structural similarity, and 0.9992 normalized cross