ZeroDay
Ensuring Authenticity in Generative Artworks
Created on 7th November 2025
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ZeroDay
Ensuring Authenticity in Generative Artworks
The problem ZeroDay solves
AI-generated content has revolutionized creativity, allowing anyone to create stunning images, text, or music with just a prompt. However, this accessibility has also brought a serious issue — there is no easy way to verify who actually created a piece of AI-generated work. Without clear authorship, artists and creators face the constant risk of their work being stolen, reused, or falsely claimed by others.
Our system solves this problem by introducing a verifiable chain of authorship for every AI-generated creation. It ensures that each piece of digital art, writing, or audio is securely linked to the user who generated it, along with the original prompt and timestamp. This creates a trustworthy record that cannot be tampered with, offering creators a reliable way to prove ownership and originality.
By protecting intellectual property and preventing plagiarism, our solution makes digital creativity safer and more transparent. It also helps users of existing AI platforms — like Midjourney, DALL·E, or ChatGPT — verify their work with just a single upload, without needing any technical expertise. Ultimately, the system restores fairness and accountability in the world of AI-generated content, empowering creators to share their work confidently and protecting their rights in the digital space.
Challenges we ran into
One of our biggest challenges was finding a unique, practical solution that went beyond just a basic blockchain NFT implementation. We wanted our Proof-of-Art system to not only verify authorship but also establish authentic creator identity -which led us to explore ideas like biometric verification and seed-based session linking.
Integrating these advanced security layers while keeping the system lightweight and user-friendly was tricky, especially under time constraints. We iterated quickly, experimented with multiple approaches, and finally designed a hybrid model using hash-based proofs + user seed context for identity validation.
Tracks Applied (5)
1st Prize
2nd Prize
3rd Prize
Category Winner
Ethereum Track
ETHIndia
