Soft Redaction of Image Provenance via Zero-Knowledge Proofs
arXiv SecurityArchived Aug 10, 2026✓ Full text saved
arXiv:2608.07063v1 Announce Type: new Abstract: Content provenance standards, such as C2PA, are increasingly used to attach signed records of origin, editing history, and rights to digital images. However, provenance transparency can conflict with privacy -- assertions that strengthen trust in an image may also reveal sensitive information about the creator or capture context. We propose soft redaction for image provenance: a mechanism that replaces sensitive provenance assertions with zero-know
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✦ AI Summary· Claude Sonnet
Computer Science > Cryptography and Security
[Submitted on 7 Aug 2026]
Soft Redaction of Image Provenance via Zero-Knowledge Proofs
Muhammad Awan, John Collomosse
Content provenance standards, such as C2PA, are increasingly used to attach signed records of origin, editing history, and rights to digital images. However, provenance transparency can conflict with privacy -- assertions that strengthen trust in an image may also reveal sensitive information about the creator or capture context. We propose soft redaction for image provenance: a mechanism that replaces sensitive provenance assertions with zero-knowledge proofs (ZKPs) of selected properties over hidden data. Our work focuses on distance proofs. We first show how location assertions can support proofs of proximity to a public reference point, using Chebyshev polynomial approximations within the ZKP proof circuit. We then extend the approach to L2 distance proofs over biometric embeddings, enabling privacy-preserving claims related to likeness to help enforce personality rights with images. Finally, we apply the same distance-proof construction to perceptual hashes (visual fingerprints), supporting an anti-spoofing use case in watermark-based recovery of stripped provenance metadata. Our results demonstrate that ZKPs over image provenance can provide practical soft-redaction capabilities, compatible with C2PA, that may be constructed in seconds and verified in milliseconds.
Comments: To appear at ECCV 2026 workshop on Privacy Fairness Accountability and Transparency in Computer Vision (PFATCV)
Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI)
Cite as: arXiv:2608.07063 [cs.CR]
(or arXiv:2608.07063v1 [cs.CR] for this version)
https://doi.org/10.48550/arXiv.2608.07063
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Submission history
From: John Collomosse [view email]
[v1] Fri, 7 Aug 2026 10:13:13 UTC (684 KB)
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