ZIP-205: Wildlife Photography NFT Standard

Abstract

This ZIP defines a standard for minting verified wildlife photography as NFTs with embedded GPS coordinates, camera hardware attestation, timestamp verification, and species identification metadata. The standard extends ZRC-721 (ZIP-200) with a PhotographyRecord struct that captures the full provenance chain from camera sensor to blockchain. A verification pipeline using Zoo AI (ZIP-401) performs automated species identification, and optional TEE (Trusted Execution Environment) attestation from supported camera hardware proves the image was not AI-generated or digitally manipulated before minting.

Motivation

Wildlife photography is a multi-billion dollar industry, yet digital wildlife images suffer from:

Provenance opacity: No reliable way to prove when, where, or by whom a photo was taken.
AI contamination: AI-generated wildlife images are indistinguishable from real photographs, undermining trust in conservation documentation.
No conservation link: Wildlife photographers profit from species they document but rarely contribute to their protection.
Metadata stripping: Social media and resale platforms strip EXIF data, destroying provenance.

This standard creates a tamper-evident chain from camera to blockchain, ensuring every wildlife photo NFT is verifiably authentic and contributes to conservation.

Specification

1. Photography Record

// SPDX-License-Identifier: CC0-1.0
pragma solidity ^0.8.20;

struct PhotographyRecord {
    // Location
    int256 latitude;             // Scaled 1e7 (e.g., -1.2921 = -12921000)
    int256 longitude;            // Scaled 1e7
    uint256 altitude;            // Meters above sea level
    string locationName;         // Human-readable (e.g., "Masai Mara, Kenya")

    // Timing
    uint64 captureTimestamp;     // UNIX timestamp from camera
    uint64 mintTimestamp;        // On-chain mint time

    // Camera attestation
    bytes32 cameraId;            // Hardware identifier hash
    string cameraModel;          // e.g., "Canon EOS R5"
    bytes32 imageHash;           // SHA-256 of original RAW file
    bytes teeAttestation;        // TEE signature proving camera origin (optional)

    // Species identification
    string speciesCommonName;
    string speciesScientific;
    uint8 aiConfidence;          // 0-100 confidence from ZIP-401
    IUCNStatus iucnStatus;

    // Photographer
    address photographer;
    string photographerName;
    string license;              // CC license identifier
}

2. Core Contract

import {ZRC721Wildlife} from "./ZRC721Wildlife.sol";

contract WildlifePhotographyNFT is ZRC721Wildlife {
    bytes32 public constant PHOTOGRAPHER_ROLE = keccak256("PHOTOGRAPHER_ROLE");

    mapping(uint256 => PhotographyRecord) private _photos;
    mapping(bytes32 => bool) private _usedImageHashes;

    event PhotoMinted(
        uint256 indexed tokenId,
        address indexed photographer,
        int256 latitude,
        int256 longitude,
        string speciesScientific
    );

    function mintPhoto(
        PhotographyRecord calldata record,
        string calldata programId,
        uint16 conservationBps
    ) external onlyRole(PHOTOGRAPHER_ROLE) returns (uint256) {
        require(!_usedImageHashes[record.imageHash], "DUPLICATE_IMAGE");
        require(record.captureTimestamp <= block.timestamp, "FUTURE_TIMESTAMP");
        require(record.captureTimestamp >= block.timestamp - 365 days, "TOO_OLD");
        require(record.aiConfidence >= 70, "LOW_CONFIDENCE");

        _usedImageHashes[record.imageHash] = true;

        // Mint ZRC-721 with species metadata
        SpeciesRecord memory species = SpeciesRecord({
            commonName: record.speciesCommonName,
            scientificName: record.speciesScientific,
            kingdom: "Animalia",
            phylum: "",
            class_: "",
            order_: "",
            family: "",
            genus: "",
            species: "",
            iucnStatus: record.iucnStatus,
            taxonomyHash: keccak256(abi.encodePacked(record.speciesScientific)),
            habitatRegion: record.locationName,
            observationDate: record.captureTimestamp
        });

        uint256 tokenId = mintWildlife(msg.sender, species, programId, conservationBps);
        _photos[tokenId] = record;

        emit PhotoMinted(
            tokenId,
            msg.sender,
            record.latitude,
            record.longitude,
            record.speciesScientific
        );

        return tokenId;
    }

    function photoOf(uint256 tokenId) external view returns (PhotographyRecord memory) {
        return _photos[tokenId];
    }
}

3. Verification Pipeline

Photographer captures image
  |
  v
Camera writes EXIF + TEE attestation (if supported)
  |
  v
Upload to Zoo verification service
  |
  ├── SHA-256 hash computed on original RAW
  ├── GPS coordinates extracted and validated
  ├── Zoo AI (ZIP-401) identifies species + confidence
  ├── TEE attestation verified (if present)
  └── Duplicate hash check against on-chain registry
  |
  v
If all checks pass: mint transaction submitted

4. GPS Privacy Zones

For endangered species, exact GPS coordinates may enable poaching. The protocol supports privacy zones:

IUCN Status	GPS Precision	Rationale
LC, NT	Full precision	Low risk
VU	10km grid	Moderate protection
EN, CR	Country only	High protection
EW, EX	No location	Maximum protection

The full-precision coordinates are stored encrypted on-chain, accessible only to authorized researchers via the Zoo research DAO (ZIP-603).

5. Metadata JSON Extension

{
  "name": "Snow Leopard #17 - Hemis National Park",
  "image": "ipfs://QmPhoto...",
  "photography": {
    "camera_model": "Canon EOS R5",
    "capture_time": "2025-01-10T06:15:00Z",
    "location": {
      "name": "Hemis National Park, Ladakh",
      "latitude_grid": "34.0N",
      "longitude_grid": "77.5E",
      "precision": "10km"
    },
    "image_hash": "0xabcd...",
    "tee_attestation": true,
    "ai_species_id": {
      "species": "Panthera uncia",
      "confidence": 97
    }
  }
}

6. Parameters

Parameter	Value	Governance
Minimum AI confidence for mint	70%	ZooGovernor
Maximum image age for mint	365 days	ZooGovernor
GPS privacy zones	Per IUCN status	ZooGovernor
Minimum conservation split	10% (1000 bps)	ZooGovernor
TEE attestation required	Optional (higher trust tier)	ZooGovernor

Rationale

Why GPS metadata on-chain? Location data proves the photographer was physically present in the habitat. Combined with timestamp verification, this provides stronger authenticity evidence than metadata-free NFTs.

Why privacy zones for endangered species? Exact coordinates of critically endangered species could be exploited by poachers. The graduated privacy model balances scientific utility against species safety.

Why 70% AI confidence minimum? Below 70%, species misidentification is too likely. This threshold ensures taxonomic accuracy without being so strict that it excludes difficult field conditions (poor lighting, partial views).

Why TEE attestation optional? Most existing cameras lack TEE capability. Making it optional allows broader participation while rewarding verified hardware with a higher trust tier.

Security Considerations

Image Manipulation

Without TEE attestation, images could be digitally altered before hashing. The protocol's trust model is gradient: TEE-attested photos have the highest trust, AI-verified photos have moderate trust, and unverified uploads are not permitted.

GPS Spoofing

Camera GPS can be spoofed. Cross-referencing with habitat range data (is this species plausible at this location?) and TEE attestation mitigate this. The Zoo AI pipeline flags geographically implausible species-location combinations.

Poaching Risk

Even with privacy zones, metadata patterns (mint frequency from a region, species sequence) could reveal endangered species locations. The protocol should rate-limit queries and monitor for suspicious access patterns.

Duplicate Detection

The imageHash uniqueness check prevents minting the same image twice. However, slightly modified images produce different hashes. Perceptual hashing via the Zoo AI pipeline provides a secondary duplicate detection layer.

Wildlife Photography NFT Standard