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Artefex v1.0 - Achieved

This document tracked the roadmap from v0.3 to v1.0. v1.0 has been released.

Current State (v1.0.0)

Artefex is a production-ready forensic image analysis and restoration tool:

  • 13 degradation/provenance detectors (JPEG, noise, upscaling, color shift, screenshots, watermarks, EXIF, platform fingerprinting, AI detection, steganography, camera ID, copy-move forgery, re-compression)
  • Classical restoration pipeline with "do no harm" quality guarantee
  • Neural pipeline infrastructure ready (requires GPU-trained models for enhancement - see GPU Training Roadmap)
  • 31 CLI commands, public Python API, FastAPI web UI
  • Video/GIF frame-by-frame support
  • Batch processing, directory watching, quality grading
  • Color accessibility, palette extraction, orientation correction
  • Plugin system, config files, quality gate for CI/CD

Milestone Plan

v0.4 - Pre-trained Model Weights + Model Hub

Goal: Ship working neural models so users get neural restoration out of the box.

  • [ ] Train and validate deblock-v1, denoise-v1, sharpen-v1, color-correct-v1 models
  • [ ] Host model weights (GitHub Releases or dedicated CDN)
  • [ ] artefex models download command to fetch weights on demand
  • [ ] Lazy download on first use with progress bar
  • [ ] Model integrity verification (SHA-256 checksums)
  • [ ] Document model training reproduction steps

v0.5 - Temporal Coherence + Audio

Goal: Make video restoration production-grade with frame-to-frame consistency.

  • [ ] Temporal smoothing across restored video frames (prevent flicker)
  • [ ] Audio track passthrough during video restoration
  • [ ] Multi-format video output (MP4, WebM, MOV)
  • [ ] Configurable keyframe-based analysis (skip similar frames)
  • [ ] Video-specific degradation detection (encoding artifacts, interlacing, frame drops)

v0.6 - Interactive Web UI

Goal: Make the web interface a first-class experience for non-CLI users.

  • [ ] Real-time analysis progress with WebSocket updates
  • [ ] Side-by-side comparison tools (slider, diff overlay, toggle)
  • [ ] Batch upload and management queue
  • [ ] Export reports from web UI
  • [ ] Session persistence and history

v0.7 - Expanded Neural Model Zoo

Goal: Broaden the types of restoration Artefex can perform.

  • [ ] Super-resolution model (2x/4x upscaling)
  • [ ] Inpainting model (watermark/object removal)
  • [ ] Dehazing/defogging model
  • [ ] JPEG artifact removal at various quality levels
  • [ ] Community model submission process and standards

v0.8 - Testing, Benchmarks, and API Stability

Goal: Harden the project for production use.

  • [ ] Integration test suite (end-to-end workflows)
  • [ ] Performance benchmark suite with regression tracking
  • [ ] API surface review and deprecation of unstable interfaces
  • [ ] Type annotations across all public APIs
  • [ ] Documentation coverage for all public functions
  • [ ] Cross-platform CI validation (ARM64 runners)

v1.0 - Stable Release

Goal: Production-ready, stable public API, community ecosystem.

  • [ ] Semantic versioning guarantee on public API
  • [ ] Comprehensive documentation site with tutorials and examples
  • [ ] Community model zoo with at least 10 contributed models
  • [ ] PyPI stable release with long-term support commitment
  • [ ] Migration guide from alpha versions
  • [ ] Security audit of file handling and web UI
  • [ ] Performance targets documented and met (e.g., analysis under 2s for 4K images)

Priorities for Next Steps

The most impactful work to start now:

  1. Model training (v0.4) - The neural pipeline is built but ships without weights. Training and hosting models unlocks the full restoration capability.
  2. Integration tests (v0.8) - The 47 unit tests pass, but end-to-end workflow tests would catch regressions and validate the full pipeline.
  3. API stability review (v0.8) - Lock down the public API surface before more users depend on it.