talk · Thursday 24 September · 11:39–⁠11:51 · SAL B

Unifying Biodiversity Images: Content-Based Identification with the ISCC standard (ISO 24138:2024)

Wouter Addink · AI-Readiness Metrics and Metadata for Biodiversity Data

Recording time 3:37:06–3:51:28Open on Vimeo ↗

Summaries and key moments are machine-produced. Check the recording for the speaker’s exact words.

The short versionISCC gives images a content-derived, similarity-comparable identifier that anyone can recompute, addressing duplication, broken links and AI-generated fakes.

Overview

What this was about

Wouter Addink introduced the International Standard Content Code (ISCC, ISO 24138:2024) as a content-aware identifier for biodiversity media; nobody in the room had heard of it. He explained why DOIs (location-focused) and cryptographic hashes (break on any pixel, metadata or format change) fall short for images, and how ISCC combines meta, content, data and instance codes so that content codes survive watermarks or format conversion and Hamming distance measures similarity. A beta 'declaration event' binds an ISCC to a signed creator and timestamp using DIDs and an append-only log run on decentralised hubs, enabling content-based discovery, provenance, isolation of AI fakes and annotations resilient to derivatives.

Why it matters. Biodiversity media are copied, converted and increasingly faked; a content-based identifier could keep annotations and provenance attached across copies and formats.

Key ideas

In the room

  • Challenges: AI-generated fakes undermining trust, uncoordinated copies, and metadata/provenance lost when files are renamed or converted.
  • PIDs like DOIs, RORs and ORCIDs work where identifying metadata is easy, but images need their content to distinguish them.
  • Standard hashes such as SHA-256 change completely with any pixel, metadata or format change.
  • ISCC works for images, video, audio and text; a full code concatenates meta, content, data and instance units (typically 64 bits each) with self-describing header (main type, subtype).
  • The instance code behaves like a hash; the data code changes with data changes; the content code stays the same under format conversion or watermarking.
  • Because ISCC codes carry semantics, Hamming distance between codes measures how different two items are, enabling similarity matching.
  • Declaration events (beta, not yet in the standard) bind an ISCC to a signature and timestamp as a FAIR Digital Object, using DIDs and an append-only log on self-hostable hubs rather than blockchain.
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Notable moments

In their words

Transcript

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Programme abstract

Unifying Biodiversity Images: Content-Based Identification with the ISCC standard (ISO 24138:2024)

Wouter Addink

Digital images are vital to modern biodiversity science for e.g. studying specimens, identifying species, and extracting data by AI. However, sharing imagery faces critical challenges: image duplication across repositories, broken data links during reformatting, and AI-generated fakes undermining trust. Traditional PIDs (like DOIs) manage location but ignore content, while standard hashes (SHA-256) break with any pixel modification.

This presentation introduces the International Standard Content Code (ISCC, ISO 24138:2024) as a content-aware verification layer. The ISCC-CODE functions as a digital fingerprint derived from visual media, enabling similarity-based matching so that cropped, compressed, or converted images retain highly similar codes.

Because the code only describes the media itself, the ISCC Discovery Protocol (IDP) is proposed by T. Pan & M. Etzrodt (ISCC Foundation) to enable content-based discovery and provenance. The core of IDP is the Declaration Event - a FAIR Digital Object (FDO) identifying who declared what content at what time via a persistent ISCC-ID. Reference implementation: https://github.com/iscc/iscc-hub.

The IDP solves three critical problems:

Automated Deduplication: Content based clustering of identical or near-identical images across infrastructures without relying on inconsistent metadata. Annotation Resilience: Keeping downstream annotations anchored to an image even when it is manipulated across workflows. AI Fake Detection & Provenance: Using cryptographically signed Declaration Events to verify the origin and authenticity of images, isolating synthetic fakes.

A content-derived fingerprint implementation like IDP offers a scalable, decentralized path to securing trust and interoperability in biodiversity imagery.

Submitted to the conference programme before the talk, so the talk itself may differ.