AI Image Moderation and Metadata Policy 2025 — Preventing Misdelivery/Backlash/Legal Risks

TL;DR

• Document and consistently operate "preserve/remove/mandatory" policies for synthetic disclosure and watermarks (C2PA/Content Credentials, etc.)
• Implement pre-distribution inspection from 3 perspectives: PII (personal information)/copyright & licensing/model & property releases
• Metadata loss during automated optimization or CDN conversion easily causes accidents—always include final human review (red pen check)
• Keep audit logs in JSON format, recording exception approvals (emergency publication, etc.) with SLA and recovery procedures
• Operate separate publication criteria by purpose (company site/advertising/marketplace/social)

Internal links: Safe Metadata Removal and Retention Design 2025 — Privacy/Compliance Response, IPTC/XMP Author & License Embedding 2025 — Preserving Information When Sharing, Model/Property Release Management Practices 2025 — IPTC Extension Expression and Operations, Editorial Image Rights and Safe Delivery 2025 — Faces/Minors/Sensitive Information

Introduction: Why "AI Image Safe Operations" Now

With the widespread adoption of generative AI, synthetic image applications have expanded to advertising, e-commerce, media, and SaaS. However, risks of distribution accidents have also increased, including lack of synthetic disclosure, watermark removal, rights attribution loss, and PII contamination (GPS/faces/contact information). Accidents often occur from "metadata or attribution mistakes" rather than "image quality itself," directly leading to re-distribution, backlash, and legal costs. This article summarizes policies and workflows that can be realistically operated as of 2025, from a field perspective.

Policy Pillars (Principles)

• Privacy first priority (GPS/device ID/preview removal)
• Creator/license/credit preservation
• ICC profile maintenance for color consistency

Additionally, AI generation-specific principles: "synthetic disclosure consistency," "unified watermark/manifest handling," and "compliance with redistribution destination policies (platform/ad review/marketplace terms)."

Terms and Assumptions

• Synthetic Disclosure (AI Disclosure): Attribution or metadata indicating an image is AI-generated/synthetic.
• Watermarks/Manifests: Signed metadata including provenance and operation history like Content Credentials/C2PA.
• IPTC/XMP/EXIF: Standardized metadata frameworks. IPTC's DigitalSourceType is useful for explicit synthetic type specification.
• Model/Property Releases: Permission information regarding subjects (people/facilities).

Publication Criteria Design (Separate by Channel)

The same image has different requirements depending on "where it's published." At minimum, prepare publication criteria for these channels:

• Company Site (Owned): Synthetic disclosure via UI display or JSON-LD specification. C2PA preservation recommended.
• Advertising (Various Networks): Mandatory UI display disclosure based on guidelines. Watermark removal not recommended.
• Marketplace (Materials/EC): Full preservation of mandatory IPTC/XMP items (author/license/source/releases).
• Social: Metadata easily lost during recompression. Redundantly include disclosure text outside images (in body text or captions).

Related: Editorial Image Rights and Safe Delivery 2025 — Faces/Minors/Sensitive Information

Metadata Minimum Set (Items to Guarantee Before Distribution)

For AI images, make these IPTC/XMP items mandatory:

• Creator / Credit / CopyrightNotice / WebStatementOfRights (rights page URL)
• DigitalSourceType (e.g., compositeWithAI / trainedAlgorithmicMedia)
• UsageTerms / License (license type/contract ID)
• ModelReleaseStatus / PropertyReleaseStatus (people/facility permission status)
• Instructions / Description (complement disclosure text)
• ICC Profile (display consistency)

Using strip-all in CDN or optimization pipelines removes the above. Specify preservation rules and fix tools/settings.

C2PA/Content Credentials Policy

• Preserve: Enhances source transparency, making investigations easier in case of misdelivery.
• Remove: Only when publication destination lacks compatibility and treats as corrupted, with exception approval. Always record reasons in audit logs.
• Mandate: Recommended for trust-critical contexts like official announcements/advertising. Retry as publication failure when missing.

Workflow (4 Gates)

1. Preparation/Generation: Rights confirmation of prompts/models/materials. Record vendor/contract IDs.

2. Editing/Synthesis: Specify synthesis ratio and sources in Instructions/DigitalSourceType. Decide watermark/manifest preservation feasibility.

3. Optimization/Conversion: Apply metadata preservation settings for compression and resizing. Maintain ICC, remove sensitive PII.

4. Pre-distribution Audit: Final confirmation of disclosure, rights, PII, and image quality through automated checks + human review (red pen).

Automated Check Example (Pseudocode)

function preflight(asset) {
  const report = detectMetadata(asset)
  const issues = []
  if (!report.rights.creator || !report.rights.license) issues.push('rights-missing')
  if (report.ai.isSynthetic && !report.ai.disclosure.marker) issues.push('disclosure-missing')
  if (report.pii.gps || report.pii.deviceId) issues.push('pii-present')
  if (!report.color.iccProfile) issues.push('icc-missing')
  return { ok: issues.length === 0, issues }
}

Audit Log JSON Example

{
  "id": "op-2025-09-23-001",
  "asset": "s3://assets/campaign/kv-hero.jpg",
  "checks": {
    "pii": { "gps": false, "face": true },
    "rights": { "creator": "ACME Studio", "license": "contract-#A123" },
    "aiDisclosure": { "digitalSourceType": "compositeWithAI", "c2pa": "present" }
  },
  "decision": "publish-with-disclosure",
  "reviewer": "u123",
  "timestamp": "2025-09-23T09:00:00Z"
}

Common Pitfalls and Countermeasures

• Automatic optimization with strip-all → Mandatory IPTC/XMP/ICC disappears → Introduce "preservation list" to pipeline settings
• Metadata loss in CDN on-the-fly conversion → Add pass-through settings to conversion layer (specify preservation targets)
• Disclosure missing only from thumbnails → Redundant caption/badge display in UI/template side
• Unauthorized watermark removal plugin application → Detect and block plugin execution in CI, require approval for exceptions
• Culture of keeping generation/synthesis history private → Get agreement with brand/legal, document "how much to disclose"

Reference: Safe Metadata Removal and Retention Design 2025 — Privacy/Compliance Response

Operational Guardrails (SOP)

• Record image "source," "rights," "synthesis ratio," and "disclosure method" fields in templates (Notion/Issue/Git, etc.)
• Exception approvals (emergency projects/agency submissions) get approval IDs and link to audit logs
• Weekly sampling audits (n%) tracking detection/false positive rates and correction SLA
• Incident reviews for breakdowns or backlash include "preventive setting changes"

Metrics (Visualization Indicators)

• disclosure-coverage (percentage with synthetic disclosure attached)
• rights-complete (completion rate of mandatory rights fields)
• pii-incident-rate (PII contamination detection/occurrence rate)
• time-to-fix (median from detection to correction)
• c2pa-preservation-rate (C2PA preservation rate)

Implementation Snippets (Conceptual Examples)

Watermark/Manifest Handling

if (asset.hasC2PA || asset.hasWatermark) {
  if (policy.keep) preserveManifest(asset)
  else if (policy.remove) removeManifest(asset, { reason: 'channel-incompatible' })
  else if (policy.require && !policy.allowMissing) throw new Error('manifest-required')
}

IPTC AI-Related Field Assignment

{
  "Iptc4xmpExt:DigitalSourceType": "compositeWithAI",
  "Iptc4xmpExt:ModelReleaseStatus": "MR-Yes",
  "Iptc4xmpExt:PropertyReleaseStatus": "PR-Unknown",
  "dc:creator": ["ACME Studio"],
  "xmpRights:UsageTerms": "Campaign-2025 / Contract-A123"
}

FAQ

Q1. Should C2PA always be preserved?

Preservation recommended for areas where trustworthiness adds value (official releases/advertising/recruitment PR, etc.). Remove only for channels with compatibility issues, with exception approval.

Q2. Should synthetic disclosure be in UI or metadata?

Both is ideal. At minimum, make machine-readable via metadata (JSON-LD/IPTC) and human-comprehensible via UI (badges/notes).

Q3. What are typical PII examples?

GPS, device IDs, faces/names/contact info, license plates, personal desk information. Use combined automatic detection + manual confirmation.

Q4. How to attribute when stock materials are AI-synthesized?

Follow original material licensing plus set DigitalSourceType to synthesis category. Always preserve credits/licenses.

Q5. How to prevent CDN optimization from removing rights information?

Specify IPTC/XMP/ICC preservation in conversion layer settings. Sample distribution materials with automated tests to detect loss.

Checklist

• [ ] Remove sensitive metadata (GPS/device ID/face thumbnails)
• [ ] Preserve rights information (Creator/Credit/License/UsageTerms)
• [ ] Add DigitalSourceType/disclosure text
• [ ] Comply with C2PA/Content Credentials preservation policy
• [ ] Settings ensure IPTC/XMP/ICC maintained through CDN/optimization
• [ ] Dual automated checks + human review
• [ ] Exception approval and audit log (JSON) setup

Related articles: IPTC/XMP Author & License Embedding 2025 — Preserving Information When Sharing, Model/Property Release Management Practices 2025 — IPTC Extension Expression and Operations, Editorial Image Rights and Safe Delivery 2025 — Faces/Minors/Sensitive Information