[2603.23178] SAiW: Source-Attributable Invisible Watermarking for Proactive Deepfake Defense

Computer Science > Artificial Intelligence arXiv:2603.23178 (cs) [Submitted on 24 Mar 2026] Title:SAiW: Source-Attributable Invisible Watermarking for Proactive Deepfake Defense Authors:Bibek Das, Chandranath Adak, Soumi Chattopadhyay, Zahid Akhtar, Soumya Dutta View a PDF of the paper titled SAiW: Source-Attributable Invisible Watermarking for Proactive Deepfake Defense, by Bibek Das and 4 other authors View PDF HTML (experimental) Abstract:Deepfakes generated by modern generative models pose a serious threat to information integrity, digital identity, and public trust. Existing detection methods are largely reactive, attempting to identify manipulations after they occur and often failing to generalize across evolving generation techniques. This motivates the need for proactive mechanisms that secure media authenticity at the time of creation. In this work, we introduce SAiW, a Source-Attributed Invisible watermarking Framework for proactive deepfake defense and media provenance verification. Unlike conventional watermarking methods that treat watermark payloads as generic signals, SAiW formulates watermark embedding as a source-conditioned representation learning problem, where watermark identity encodes the originating source and modulates the embedding process to produce discriminative and traceable signatures. The framework integrates feature-wise linear modulation to inject source identity into the embedding network, enabling scalable multi-source watermark generatio...