I’ve been thinking a lot about how digital assets (images, videos, documents, even raw data streams) lose trustworthiness almost immediately after creation. Not just from AI edits or deepfakes, but from routine handling: compression, metadata stripping, format conversions, platform re-uploads, etc. Most current approaches to provenance (watermarks, C2PA-style manifests, blockchain hashes) feel like snapshots at the point of origin or publication. They verify “this was real/clean at time T,” but then… what? The asset moves through systems, gets cropped/resized/AI-enhanced/forwarded, and that initial proof becomes outdated or unverifiable without continuous tracking. I’m exploring a different framing: treat the origin capture itself as the foundational layer of a living trust chain. Instead of a static certificate, build an integrity envelope right at the point of creation/capture (e.g., device-level signed metadata, tamper-evident hashing during acquisition, cryptographically bound to hardware/sensor fingerprints). This “reality shield” layer would record immutable signals about how/where/when the asset was first digitized—before any mutation events kick in. Those origin signals could then feed into downstream systems that recalculate confidence as changes accumulate (e.g., “High Confidence origin, but Moderate after AI upscaling detected”). Questions for anyone working in this space: What origin-capture techniques have you seen that actually survive real-world pipelines (e.g., social media, editing tools, AI processing)? Where do existing provenance standards (C2PA, etc.) fall short on the “capture integrity” part specifically? Does thinking in terms of a hardened origin layer make sense as a prerequisite for dynamic trust systems, or am I overcomplicating it? Edge cases: How to handle phone cameras, screen captures, legacy files, or content from untrusted devices? Curious if this resonates with others building verification tools or dealing with misinformation/authenticity in AI workflows. Happy to hear why this is naive or what better metaphors/approaches exist. Looking forward to thoughts/critiques! submitted by /u/okfixitdrunk
Originally posted by u/okfixitdrunk on r/ArtificialInteligence