Trust Adaptive Provenance Watermarking for Synthetic News Broadcasting
DOI:
https://doi.org/10.68012/beam.v2i1.265Keywords:
Adaptive Provenance, Artificial Intelligence, Digital Watermarking, News Broadcasting, WatermarkingAbstract
The rapid adoption of generative artificial intelligence in digital news broadcasting has increased the production of synthetic news content, raising critical concerns regarding authenticity, source verification, content manipulation, and audience trust. This study proposes a trust-adaptive provenance watermarking framework designed to dynamically protect and verify AI-generated news content by integrating provenance information with trust-aware watermarking mechanisms throughout the broadcasting workflow. The proposed framework combines adaptive digital watermarking, provenance metadata, and a trust-scoring mechanism that adjusts watermark strength according to content sensitivity, source credibility, and manipulation risk. Experimental evaluation is conducted using synthetic news samples under common content transformations, including compression, resizing, cropping, re-encoding, and partial manipulation, while performance is assessed through watermark detection accuracy, robustness, content quality, and provenance verification reliability. The experimental findings demonstrate that the proposed approach achieves high watermark detectability while maintaining acceptable perceptual quality across different broadcasting transformations. The adaptive trust mechanism further improves provenance verification for high-risk synthetic content and enables more reliable identification of manipulated or unauthorized news assets compared with conventional fixed-strength watermarking approaches. The proposed trust-adaptive provenance watermarking framework provides a practical approach for strengthening authenticity, traceability, and accountability in synthetic news broadcasting, offering a foundation for trustworthy AI-enabled media ecosystems while supporting broadcasters, audiences, and digital platforms in mitigating misinformation and unauthorized content manipulation.
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