metadata
license: apache-2.0
tags:
- steganography
- steganalysis
- gguf
- balanced-detector
- image-classification
- computer-vision
- bitcoin
library_name: gguf
pipeline_tag: image-classification
datasets:
- custom
metrics:
- accuracy
- f1
- auc
Model Card: Starlight Balanced Detector (GGUF)
Model Overview
- Task: Steganography detection (and method / bit-order heads)
- Architecture:
BalancedStarlightDetector(multi-stream CNN + fusion gate) - Input streams: pixel, meta, alpha, LSB, palette, format/content features (see GGUF export docs)
- Primary artifact: GGUF for Stargate / Trin (Go)
Artifacts (this repo on HF)
| File | Description |
|---|---|
starlight.gguf |
Primary production weights (GGUF v3 / F32) |
starlight_gguf_map.json |
Tensor name map and export metadata |
README.md |
This model card |
Optional secondary (may appear if uploaded): detector_balanced.pth, detector_balanced.onnx.
Inference
Production: load starlight.gguf via Stargate / Trin GGUF path — not Python ONNX as the primary path.
Download:
https://huggingface.co/macroadster/starlight-prod/resolve/main/starlight.gguf
https://huggingface.co/macroadster/starlight-prod/resolve/main/starlight_gguf_map.json
Training
- Dataset: Combined submissions (grok, gemini, claude, chatgpt, sample, val)
- Checkpoint:
models/detector_balanced.pth(training repo) - Export:
scripts/export_starlight_gguf.py→models/starlight.gguf - Typical recipe: Adam, balanced clean/stego sampling (see training repo
trainer.py)
Steganography coverage
lsb,alpha,exif,eoi/ raw,palette(and related variants in datasets)
Performance
| Metric | Value |
|---|---|
| Accuracy | see training run / leaderboard |
| False positive focus | balanced detector design |
Update metrics after each publish-worthy training run.
License
- Model: Apache 2.0
- Code: see training repository LICENSE