A proposal for hybrid watermarks

ChatGPT Feature requests
1

1. Detailed Proposal Draft for OpenAI

1.1. Title Page / Cover

  • Title: Hybrid Image Watermarking and Tamper Detection Proposal for OpenAI-Generated Images
  • Author / Organization: Artem Kazantsev
  • Date: 2025-01-09

1.2. Executive Summary

Executive Summary
This proposal outlines a hybrid watermarking and tamper detection system for images generated by OpenAI models (e.g., DALL·E, ChatGPT’s image plugins, etc.). The system combines metadata-based watermarks, steganographic embedding, cryptographic signing, and machine learning for tamper detection.

We propose to:

  1. Embed transparent and visible metadata to promote ethical AI disclosure and user trust.
  2. Use robust steganographic methods that survive compression and minor edits.
  3. Cryptographically sign images so that perfectly intact images can be verified with a confidence score of 0 (no alteration).
  4. Employ Dempster-Shafer theory and Bayesian learning to generate a tamper-detection score from 0 (no change) to 10 (definitely tampered).

This proposal aims to help OpenAI become an industry leader in responsible AI image generation, aligning with regulatory frameworks and ethical standards.

1.3. Problem Statement

  • Rapid Proliferation of AI-Generated Images: As large language and image models become more ubiquitous, it’s increasingly difficult to distinguish AI-generated content from genuine photos or human-created artwork.
  • Misinformation and Malicious Use: Without strong provenance tracking, malicious actors can misuse or misattribute AI-generated visuals.
  • Regulatory & Ethical Concerns: Emerging regulations (EU AI Act, US legislative proposals, etc.) emphasize transparency in AI-generated content.
  • Technical Challenges: Simple metadata watermarks are easily stripped, while purely visible watermarks can be obtrusive or manipulated.

1.4. Proposed Solution

  1. Metadata Watermarking

    • Embed essential provenance info (e.g., model version, generation date, usage rights) in IPTC/XMP/EXIF fields.
    • Provide user-facing disclaimers (e.g., “Generated by OpenAI’s DALL·E”) for transparent disclosure.

  2. Steganographic Watermarking

    • Hide robust signals in the frequency domain (e.g., DWT/DCT) so they survive typical compression.
    • Use error correction codes (ECC) to preserve signals under minor modifications.

  3. Cryptographic Signing

    • Hash the raw image data (or specific chunks) using a secure hash algorithm (SHA-256 or better).
    • Sign the hash with a private key, store the signature in the metadata or a public blockchain for verifiable integrity checks.

  4. Tamper Detection System

    • AI Analysis: Train a model (CNN or transformer-based) to detect tampering artifacts.
    • Evidence Fusion: Apply Dempster-Shafer theory or Bayesian inference to combine evidence from:
      1. Cryptographic signature validity
      2. Presence/strength of steganographic watermark
      3. AI-based tamper analysis
    • Output a confidence score from 0 (no alteration) to 10 (definitely tampered).

1.5. Technical Approach

  1. Watermark Embedding Pipeline

    • Step 1: Generate the image via DALL·E (or ChatGPT image plugin).
    • Step 2: Insert metadata (authors, date, version, etc.).
    • Step 3: Apply steganographic embedding with redundancy across multiple frequency blocks.
    • Step 4: Compute cryptographic hash and sign the image.

  2. Verification & Detection Pipeline

    • Step 1: Check cryptographic signature. If perfectly valid, confidence score = 0 (untouched).
    • Step 2: Look for expected metadata fields. Missing metadata slightly raises the suspicion score.
    • Step 3: Attempt to extract the steganographic watermark. Evaluate signal integrity.
    • Step 4: Run AI-based analysis for pixel-level anomalies or manipulations.
    • Step 5: Fuse results (Dempster-Shafer or Bayesian) to output a final tamper score.

1.6. Implementation Benefits

  • Trust & Transparency: Users immediately see that the image is AI-generated and can verify origin.
  • Misinformation Mitigation: Harder for malicious actors to pass off AI images as real or to claim ownership.
  • Compliance with Future Regulations: Proactively meets demands for transparency and disclosure.
  • Industry Leadership: Sets a precedent for responsible AI content generation.

1.7. Potential Challenges & Mitigations

  • Performance Overhead: Steganography and cryptographic signing may increase generation time.
    • Mitigation: Optimize algorithms or integrate them at later stages in the pipeline.
  • Extreme Modifications: Heavy cropping, re-encoding, or adversarial attacks could degrade watermarks.
    • Mitigation: Use robust frequency-domain embedding plus ECC, and rely on the AI model to detect suspicious artifacts.
  • Privacy Issues: Overly detailed metadata may expose private data.
    • Mitigation: Only embed non-sensitive, AI-related metadata.

1.8. Implementation Plan & Timeline

Phase Duration Key Activities
Phase 1: Requirements & Design 2–4 weeks Finalize watermarking scheme, data fields, cryptographic approach.
Phase 2: Prototype & PoC 4–6 weeks Implement steganography + metadata embedder, sign images. Integrate a basic tamper detection model.
Phase 3: Testing & Refinement 6–8 weeks Test with varied compression/cropping/edits, refine AI detection.
Phase 4: Production Rollout 4–8 weeks Deploy pipeline across DALL·E/ChatGPT image generators.

1.9. Conclusion & Call to Action

In conclusion, adopting a hybrid watermarking and tamper detection system for all OpenAI-generated images offers strong benefits in security, trust, and ethical compliance. We propose a phased approach to ensure robust testing and smooth integration. We invite OpenAI to collaborate on refining and implementing this solution.