Latent Watermarking: Secure Cryptographic Hashing in Flow-Matching for AI Content Authentication

The rise of deepfakes and growing concerns about content authenticity require advanced watermarking techniques for diffusion models. While some methods embed watermarks into the initial latent variable without pretraining, they face challenges such as distortion from inversion techniques and restrictive distribution requirements, making it challenging to embed and decode lengthy, meaningful authentication messages. We theoretically demonstrate that the initial latent variable in Flow Matching could serve as a unique and secure hash seed under the manifold hypothesis, and this insight underpins latent watermarking, a novel framework leveraging the initial latent variable as a secure hash seed in Flow Matching . Our method embeds a QR code derived from the initial latent variable into the final generated image, linking the generated content to a certified generation record stored in a trusted database with inquiry services. This approach addresses the limitations of prior techniques, overcoming inversion-induced distortion while enabling efficient embedding and decoding of meaningful authentication messages without pretraining or fine-tuning. Extensive evaluations across multiple Stable Diffusion versions and sampling algorithms demonstrate the effectiveness of our approach. It preserves image quality, ensures efficient and verifiable watermarking, and supports a broader range of practical authentication cases.