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techjam-aigc/trace-rx-m-v2

sourceHugging Faceupdated 22d agoView on Hugging Face

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TRACE-RX-M v2

TRACE-RX-M v2 is a binary image-level detector for purely AI-generated versus authentic images. It compares normalized DINOv2 patch tokens against a frozen authentic-only prototype memory and classifies directional reconstruction residuals plus retrieval statistics.

This repository contains the frozen TechJam 2026 shipping detector. It is not a standalone copy of the DINOv2 backbone: loading downloads the pinned public facebook/dinov2-base revision recorded in config.json.

Files

—s4_detector.pt: shipping detector heads and checkpoint metadata.
—s3_memory.pt: required frozen authentic prototype memory.
—config.json: pinned model and training configuration.
—s4_validity.json: held-out-generator validity decision.
—s3_capacity.json: authentic-memory capacity audit.
—evaluation/summary.json: clean and official-transform summary.
—evaluation/metrics_by_condition.csv: metrics for all official conditions.

Loading

Clone the public implementation and install its training dependencies:

bash

git clone https://github.com/BenyAlbatross/techjam-aigc.git
cd techjam-aigc
uv sync --group train

Download this model repository, then reconstruct the frozen detector:

python

from pathlib import Path

import torch

from techjam_aigc.trace_rx_m.training import load_detector_checkpoint

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, metadata = load_detector_checkpoint(
    Path("s4_detector.pt"),
    Path("s3_memory.pt"),
    device=device,
)

Images must use the canonical preprocessing defined by the public repository. The detector logit is positive for AIGC; sigmoid(logit) is the exported AIGC confidence score. The current checkpoint is not probability-calibrated.

Evaluation

The frozen checkpoint was evaluated on 6,091 development images under clean pixels and all 15 official TechJam transformation settings, producing 97,456 paired endpoints.

Evaluation	ROC-AUC	Average precision	Normalized pAUC@5%	Balanced accuracy
Clean	0.9035	0.9063	0.7794	0.8852
Macro across transformed conditions	0.9011	0.9043	0.7708	0.8839
Worst condition: Gaussian noise sigma 0.10	0.8936	0.8973	0.7461	0.8734

The evaluation did not use the organizer demonstration-only set or the locked split for model selection.

Important limitations

—Cross-generator generalization is not solved. Gemini Flash Image development ROC-AUC is 0.3279, despite strong performance on FLUX, SDXL, and GPT Image 2.
—The current TechJam robustness evaluation transforms neutralized 224-pixel BMP inputs rather than original source-resolution files.
—The model targets purely generated images. AI-edited and partially composited images are outside its trained scope.
—Scores are not calibrated probabilities and no production operating threshold is claimed.
—The detector must not be treated as sole evidence for moderation or provenance decisions.

Reproducibility and provenance

—Detector SHA-256: f811c4641a644e1eaed30891f9c075932a1de8680dce812adaf03c9a7daaf25e
—Authentic memory SHA-256: 71f0bf9edeedc5af21de1b22e3705560a1b0e8adef0c1312266cfff3eb59a7a2
—Backbone: facebook/dinov2-base
—Backbone revision: f9e44c814b77203eaa57a6bdbbd535f21ede1415

Training, augmentation, evaluation, and inference source code is maintained at https://github.com/BenyAlbatross/techjam-aigc.