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[2509.10766] MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks
Summary
The paper presents MetaSeal, a novel framework for content-dependent cryptographic watermarks designed to combat image attribution forgery, enhancing security and verifiability in digital images.
Why It Matters
As digital and AI-generated images proliferate, the risk of misattribution increases, potentially harming creators and developers. MetaSeal addresses these vulnerabilities by providing a more secure method of watermarking that is resistant to forgery, thus safeguarding the integrity of image attribution.
Key Takeaways
- MetaSeal offers a content-dependent watermarking solution that enhances image attribution security.
- The framework provides forgery resistance and evidence of tampering, making unauthorized alterations detectable.
- MetaSeal maintains robustness against benign transformations, ensuring reliability across various image types.
Computer Science > Cryptography and Security arXiv:2509.10766 (cs) [Submitted on 13 Sep 2025 (v1), last revised 13 Feb 2026 (this version, v2)] Title:MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks Authors:Tong Zhou, Ruyi Ding, Gaowen Liu, Charles Fleming, Ramana Rao Kompella, Yunsi Fei, Xiaolin Xu, Shaolei Ren View a PDF of the paper titled MetaSeal: Defending Against Image Attribution Forgery Through Content-Dependent Cryptographic Watermarks, by Tong Zhou and 7 other authors View PDF HTML (experimental) Abstract:The rapid growth of digital and AI-generated images has amplified the need for secure and verifiable methods of image attribution. While digital watermarking offers more robust protection than metadata-based approaches--which can be easily stripped--current watermarking techniques remain vulnerable to forgery, creating risks of misattribution that can damage the reputations of AI model developers and the rights of digital artists. The vulnerabilities of digital watermarking arise from two key issues: (1) content-agnostic watermarks, which, once learned or leaked, can be transferred across images to fake attribution, and (2) reliance on detector-based verification, which is unreliable since detectors can be tricked. We present MetaSeal, a novel framework for content-dependent watermarking with cryptographic security guarantees to safeguard image attribution. Our design provides (1) \textbf{forgery resistanc...
