@InProceedings{10.1007/978-3-032-31663-9_34,
author="Das, Bibek
and Deo, Anurag
and Adak, Chandranath
and Chattopadhyay, Soumi
and Akhtar, Zahid
and Dutta, Soumya
and Hadid, Abdenour",
editor="De Marsico, Maria
and Ho, Tin Kam
and Jurie, Frederic
and Liu, Cheng-Lin
and Lopresti, Daniel
and Nystr{\"o}m, Ingela
and Ogier, Jean-Marc
and Ross, Arun
and Wang, Liang",
title="Diffusion-Latent Invisible Watermarking for Proactive Deepfake Provenance Verification",
booktitle="Pattern Recognition",
year="2027",
publisher="Springer Nature Switzerland",
address="Cham",
pages="512--527",
abstract="The rapid advancement of generative face manipulation techniques has exposed fundamental limitations of conventional passive deepfake detection systems, particularly under distribution shift, compression, and pose variation. In this paper, we propose diffusion-latent invisible watermarking as a proactive deepfake provenance verification mechanism, enabling reliable authentication of synthetic content through diffusion-latent provenance verification. Our approach embeds structured, imperceptible watermarks directly into the latent space of diffusion models, ensuring minimal visual distortion while preserving robust recoverability under post-processing operations. Unlike existing watermarking methods that are primarily evaluated on near-frontal facial imagery, our framework explicitly addresses non-frontal and side-face deepfake scenarios, where facial geometry and texture statistics differ substantially, and robustness is significantly challenged. We introduce a perceptual-robust latent reconstruction objective that enhances watermark stability under compression, geometric transformations, and learned distortions without sacrificing visual fidelity. To rigorously evaluate the proposed approach, we conduct comprehensive experiments on public datasets, featuring diverse facial identities, multiple state-of-the-art manipulation methods, and controlled pose variations. Results show that our method consistently surpasses prior diffusion-based watermarking baselines in both watermark detection and provenance verification reliability, especially under extreme pose variations and challenging post-processing conditions. These results highlight the effectiveness of proactive, diffusion-latent invisible watermarking as a practical and robust solution for trustworthy deployment of generative face technologies.",
isbn="978-3-032-31663-9"
}