PCD-GAN: A perceptual and copyright dual generative adversarial network for artwork style transfer and copyright protection

The lack of effective copyright protection for AI-generated artworks in style transfer remains a significant challenge. Existing approaches typically embed watermarks as post-processing steps after image generation, separating copyright protection from the artistic creation process. This leads to sh...

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Bibliografische Detailangaben

Hauptverfasser:	Zhiliang Lyu, Jihong Qin, Xuan Wang, Xiao Wang, Yuwei Tan
Format:	Artigo
Sprache:	Inglês
Veröffentlicht:	Elsevier 2026-04-01
Schriftenreihe:	Alexandria Engineering Journal
Schlagworte:	Artificial intelligence Artwork style transfer Copyright protection Digital watermarking Generative adversarial networks
Online-Zugang:	http://www.sciencedirect.com/science/article/pii/S1110016826001894
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