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Validation Results

Status: Not Yet Validated Against Ground-Truth Attacks

Last Updated: 2026-04-01

Executive Summary

AuraLock's protection metrics are proxy measurements that have NOT been validated against real-world mimicry attacks such as DreamBooth, LoRA fine-tuning, or production-grade style transfer systems.

The Protection Score and related metrics measure drift in ResNet18 feature space under robustness transforms. While these proxy metrics are useful for:

  • Relative comparisons within this repository
  • Quick iteration during development
  • Understanding feature-space perturbations

They DO NOT guarantee protection against actual AI style mimicry in production environments.

What Has Been Tested

✅ Proxy Metrics (Feature Space Drift)

  • Embedding similarity using ResNet18 features
  • Style similarity using Gram matrices
  • Robustness under transforms (blur, resize, JPEG compression)
  • Image quality metrics (PSNR, SSIM)

✅ Internal Consistency

  • Profile comparisons show expected trade-offs
  • Quality vs protection curves behave predictably
  • Batch processing produces consistent results

✅ Technical Correctness

  • All tests pass in CI/CD pipeline
  • Code follows best practices
  • Infrastructure is reproducible

What Has NOT Been Validated

❌ Real-World Attack Prevention

  • No validation against actual DreamBooth training
  • No validation against LoRA fine-tuning
  • No validation against commercial style-transfer APIs
  • No validation against adversarial purification techniques
  • No validation with production-scale training datasets

❌ Ground-Truth Effectiveness Metrics

  • No A/B testing with protected vs unprotected training data
  • No style similarity measurements from trained models
  • No human evaluation of generated outputs
  • No comparison with other protection methods (Glaze, Mist, etc.)

❌ Long-Term Robustness

  • No testing against evolving model architectures
  • No testing against adaptive attacks
  • No testing against ensemble methods

Why This Matters

Without ground-truth validation, we cannot make claims about:

  1. Actual Protection Effectiveness: The proxy score may not correlate with real mimicry prevention
  2. Attack Resistance: Unknown behavior against adaptive or sophisticated attacks
  3. Comparative Performance: Cannot reliably compare with other protection tools
  4. Production Readiness: Unclear suitability for protecting valuable artwork

Planned Validation Roadmap

Phase 1: Local Benchmark Infrastructure (Completed)

  • ✅ Proxy metric pipeline
  • ✅ Profile system with quality/protection trade-offs
  • ✅ Batch processing and reporting
  • ✅ Benchmark harness for DreamBooth/LoRA

Phase 2: Dry-Run Testing (Current)

  • ✅ Manifest generation for benchmark jobs
  • ✅ Docker runtime setup
  • ✅ Colab notebook for free GPU access
  • 🔄 Preflight validation and job planning

Phase 3: GPU Ground-Truth Validation (Planned - Requires GPU Access)

  • ⏳ Run baseline DreamBooth/LoRA training on unprotected artwork
  • ⏳ Run protected training with AuraLock-processed images
  • ⏳ Generate outputs from both models with identical prompts
  • ⏳ Compare style preservation using:
    • Human evaluation
    • Automated style metrics (CLIP similarity, FID, etc.)
    • Feature-space analysis
  • ⏳ Document correlation between proxy score and actual effectiveness

Phase 4: Comprehensive Evaluation (Future)

  • ⏳ Multi-dataset validation across art styles
  • ⏳ Comparison with other protection methods
  • ⏳ Adaptive attack testing
  • ⏳ Publication of peer-reviewed results

How to Interpret Current Metrics

Protection Score (0-100)

What it measures: Drift in ResNet18 feature space under robustness transforms

What it does NOT measure:

  • Actual style mimicry prevention
  • Real DreamBooth/LoRA training outcomes
  • Human perceptual similarity of generated art

Interpretation:

  • Higher scores = more feature drift (potentially better protection)
  • Use ONLY for relative comparisons within this repository
  • DO NOT interpret as percentage of protection effectiveness
  • DO NOT compare directly with scores from other tools

Quality Metrics (PSNR, SSIM)

What they measure: Perceptual similarity between original and protected images

What they do NOT measure:

  • Effectiveness of protection
  • Robustness against attacks

Interpretation:

  • Higher PSNR/SSIM = less visible perturbations
  • Use to evaluate quality trade-offs
  • Balance with protection score based on use case

Transparency Statement

This repository prioritizes honest evaluation over marketing claims. We explicitly document:

  • ✅ What we've tested
  • ❌ What we haven't tested
  • 🔬 What we're planning to test

We encourage users to:

  1. Understand the limitations of proxy metrics
  2. Wait for Phase 3 validation before production use
  3. Contribute GPU resources or validation results
  4. Report any real-world testing outcomes

Contributing Validation Data

If you have access to GPU resources and want to help validate AuraLock:

  1. Use our Colab notebook: notebooks/AuraLock_LoRA_Benchmark_Colab.ipynb
  2. Follow the benchmark harness: See src/auralock/benchmarks/
  3. Share results: Open an issue with your findings
  4. Compare methods: Test against unprotected baseline and other tools

We welcome community contributions to ground-truth validation efforts.

References

Disclaimer

AuraLock is a research and learning project. The protection mechanisms are experimental and have not been validated against real-world attacks. Users should:

  • NOT rely solely on AuraLock for protecting valuable or commercial artwork
  • NOT interpret proxy metrics as guarantees of protection
  • NOT assume protection will work against all attack types
  • DO understand this is an educational and research tool
  • DO wait for validated results before production use
  • DO use multiple protection layers and legal safeguards

For production artwork protection, consult with legal professionals and consider using multiple protection methods in combination.