CREATIVE BLOCKCHAIN TRACEABILITY: A DEEP LEARNING AI FRAMEWORK FOR SOURCE AUTHENTICATION IN DIGITAL DESIGN AND MEDIA - Kosare, Hemlata; Zade, Amol (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The rapid expansion of digital design and media has intensified challenges related to authorship, authenticity, and source attribution within creative ecosystems. In response to these concerns, this study proposes a creative blockchain traceability framework supported by deep learning–based artificial intelligence for real-time source authentication in digital design and media practices. The framework repositions blockchain technology as a cultural infrastructure that safeguards creative ownership, while deep learning models enable intelligent analysis of visual patterns and content provenance. By integrating AI-driven visual recognition with decentralized traceability mechanisms, the proposed approach enhances transparency and trust across digital creative platforms. The study conceptually demonstrates how this framework can support diverse creative domains, including graphic design, digital art, media production, and interactive visual communication. Emphasis is placed on preserving creative integrity without constraining artistic expression or design innovation. This interdisciplinary research contributes to emerging discussions on digital authorship and creative rights by bridging visual arts, design management, and intelligent technologies. The findings offer strategic insights for designers, artists, cultural institutions, and digital platforms seeking resilient solutions for authenticating creative content in an increasingly decentralized and algorithm-driven media landscape.