BiSLW: Bi-Spectral Latent Watermarking for Generative Diffusion Models

Diffusion-based generative models have transformed visualcontent synthesis, yet they remain vulnerable to unauthorized usage andlack reliable attribution methods. Existing watermarking techniques of-ten treat latent tensors as static spatial feature maps or depend on pixel-domain modification, and most do not explicitly leverage the internalfrequency structure of the latent space for dual-band redundant embed-ding, leaving them susceptible to the stochastic nature of diffusion andregeneration attacks. We introduce BiSLW, a trainable bi-spectral la-tent watermarking framework that jointly embeds aligned identity signalsacross complementary spectral bands of the decoded diffusion latent us-ing learned encoders and decoders, going beyond fixed-pattern frequencyapproaches. We leverage the inherent frequency structure of diffusionlatents to design a dual-band watermarking framework. Low-frequencycomponents encode global semantics, while high-frequency componentscapture fine texture. We exploit this structure to embed watermarksacross complementary spectral bands. The watermark is independentlyinjected into both bands via learned encoders and recombined before de-coding, ensuring it becomes intrinsic to the generative trajectory. Dualspectral decoders recover the watermark from each band, while a cross-band consistency constraint enforces alignment between semantic andtextural embeddings. Experiments show that BiSLW achieves a strongbalance between perceptual fidelity and robustness, improving PSNR byover 3 dB compared to prior latent diffusion watermarking methods whilepreserving near-perfect bit accuracy under aggressive regeneration andcommon distortions, all with negligible computational overhead.