Detectors

Artefex includes 13 built-in detectors. Each runs independently and returns a Degradation object with confidence and severity scores.

Built-in detectors

Category	Detector	Method
Compression	JPEG artifacts	8x8 block boundary discontinuity analysis
Compression	Multiple re-compressions	Double quantization + ringing detection
Resolution	Upscaling/loss	High-frequency spectral analysis + autocorrelation
Color	Color shift	Channel imbalance + clip ratio analysis
Artifacts	Screenshot remnants	Border uniformity + aspect ratio + dimensions
Noise	Sensor/added noise	Laplacian MAD estimation
Overlay	Watermarks	Tile correlation + histogram peaks + alpha channel
Metadata	EXIF stripping	Metadata presence/completeness checks
Provenance	Platform fingerprint	Dimension/compression/EXIF signatures for Twitter, Instagram, WhatsApp, Facebook, Telegram, Discord, Imgur
Provenance	AI-generated content	Frequency spectrum, histogram smoothness, noise uniformity, patch consistency
Security	Steganography	LSB analysis, chi-square test, entropy, pairs analysis
Provenance	Camera/device ID	Sensor noise PRNU analysis (DSLR, smartphone, webcam, scanner)
Forgery	Copy-move detection	Patch-based feature matching for cloned regions

Adding a custom detector

As a method in the core

1. Add _detect_<name>(self, img, arr, result) -> Degradation | None to DegradationAnalyzer in src/artefex/analyze.py
2. Add the detector to the detectors list in the analyze() method
3. Add a recommendation in src/artefex/report.py
4. Write tests in tests/test_analyze.py

As a plugin

Create a Python package with an entry point:

# In your plugin's pyproject.toml
[project.entry-points."artefex.detectors"]
my_detector = "my_package:MyDetector"

See examples/custom_plugin.py for a complete example.