# Deepfake detection is losing. Timestamped proof-of-existence might be the other half of the answer

> Source: <https://dev.to/kimeric_t2a/deepfake-detection-is-losing-timestamped-proof-of-existence-might-be-the-other-half-of-the-answer-l19>
> Published: 2026-09-13 16:43:13+00:00

I've been reading into why deepfake detectors keep losing ground (some benchmarks put failure rates around 45-50% on recent generative models), and it pushed me toward a question that's more about infrastructure than machine learning: instead of trying to prove after the fact that a piece of media is fake, what if you could cheaply prove, at the moment of capture, that it existed in a specific form at a specific time?

That's the core idea behind "proof of existence" / timestamping systems that a lot of the DFIR and blockchain-timestamping communities already work with (OpenTimestamps being the best-known example). The mechanism is simple and doesn't require trusting a central authority:

None of this proves a photo is "true" in a philosophical sense. It proves a specific bit-for-bit artifact existed at time T, which is exactly the kind of chain-of-custody primitive that forensic investigators, journalists and human-rights researchers already work with, just applied earlier in the pipeline (at capture, not at review).

I found this framed well in a free book called time2act, which digs into this from a "detection is not enough" angle and connects it to the Berkeley Protocol on digital open-source investigations. It's free, available in English/Spanish/Catalan, web/PDF/EPUB: [https://www.time2act.info/read/en/](https://www.time2act.info/read/en/)

Curious whether others here have worked with proof-of-existence or timestamping schemes in forensic or content-provenance pipelines, and what broke when you tried to make them verifiable offline.

— Kimeric

(Disclosure: drafted with AI assistance, per DEV's AI content guidelines.)