viyog 0.1.3

Training-free, gradient-free detector that separates adversarial (ADV) from out-of-distribution (OOD) inputs via first-layer dormant-band roughness.

Viyog

▶ Try the interactive leaderboard — Viyog vs 9 pytorch-ood baselines across 20 architectures.

Separate adversarial (ADV) inputs from out-of-distribution (OOD) inputs — in one forward pass, with no training and no gradients.

Safety-critical systems must respond differently to two kinds of anomaly: OOD inputs call for abstention, adversarial inputs demand rejection. Standard detectors collapse both into a single anomaly score and cannot tell them apart. Viyog is a training-free, post-hoc second stage that makes the distinction by reading the dormant-band roughness of a model's first convolutional layer.

The idea: gradient-based attacks inject broadband high-frequency residue into the first-layer channels that are otherwise quiet on in-distribution data, making them spatially jagged. Natural inputs — both ID and OOD — leave those channels smooth. Viyog measures that roughness as a single scalar V(x):

• higher V(x) → more likely ADV
• lower V(x) → more likely OOD / ID

It adds no parameters, never touches the backward pass, and stores only O(C) bytes of state (the dormant-channel ranking + one ID mean) — roughly 0.3 KB, versus 4.5–40 MB for feature-distance detectors such as Mahalanobis / KNN / ViM.

Install

pip install viyog                 # core: torch + numpy
pip install "viyog[metrics]"      # + scikit-learn, for viyog_metrics()

Quickstart

from viyog import Viyog

v = Viyog(model)          # attaches a forward hook to the first conv layer
v.fit(id_loader)          # one ID pass: learn the dormant band + ID mean
scores = v.score(loader)  # per-sample roughness V(x); HIGHER => more adversarial
v.close()

Or as a context manager (hooks are removed automatically):

from viyog import Viyog, viyog_metrics

with Viyog(model) as v:
    v.fit(id_loader)
    ood_scores = v.score(ood_loader)
    adv_scores = v.score(adv_loader)

# separability report (OOD vs ADV): AUROC / AUPR / FPR@95 / AUTC
print(viyog_metrics(ood_scores.cpu().numpy(), adv_scores.cpu().numpy()))

With a real model (timm)

Viyog auto-detects the first conv layer (resnet50.conv1 here), so any torch.nn.Module works out of the box:

import torch, timm
from viyog import Viyog

model = timm.create_model("resnet50", pretrained=True, num_classes=10).eval()

with Viyog(model) as v:
    v.fit(id_loader)                 # your in-distribution DataLoader
    print(v.layer_name_, v.n_channels_, v.dorm_idx_.numel())   # conv1 64 6
    adv_scores = v.score(suspect_loader)   # higher V(x) => more adversarial

Pass device="cuda:0" for GPU, or layer=<module or dotted name> to hook a specific layer instead of the auto-detected first conv.

How it works

For each input, Viyog hooks the first conv layer's activation map a of shape (B, C, H, W) and computes, per channel, a magnitude-normalised total variation (average absolute change between neighbouring pixels):

tv = (|Δ_h a| + |Δ_w a|) / (mean|a| + eps)      # spatial roughness, per channel
V(x) = mean over the dormant channels of tv     # the Viyog score

The dormant band is the quietest dorm_pct (default 10%) of channels ranked by their mean absolute activation on ID data, restricted to alive channels (permanently-dead channels — common in e.g. DenseNet first convs — are excluded so the statistic stays meaningful). .fit() learns that band and the ID mean of V in a single pass; .score() returns V(x) for new inputs.

Conv2d maps use 2-D total variation; Conv1d maps (B, C, L) use 1-D total variation, so the detector also applies to 1-D signal models.

API

Viyog(model, device=None, layer=None, dorm_pct=0.10, dead_thresh=1e-4)	Wrap a model; auto-detects the first conv layer (prefers a conv1 attribute). Pass layer= (module or dotted name) to override.
.fit(id_loader) -> self	One ID pass: selects the dormant band, records id_score_mean_.
.score(x, center=False) -> Tensor	Per-sample V(x) for a batch or a whole loader. Higher = more adversarial. center=True subtracts the ID mean (monotone; AUROC-preserving).
.score_loader(loader, center=False)	Convenience wrapper over a loader.
Viyog.bounded_score(scores, temperature=1.0)	Optional monotone squash to (0, 1) for thresholding/display.
.close() / context manager	Remove the forward hook.
viyog_metrics(neg, adv, recall_level=0.95)	AUROC / AUPR_IN / AUPR_OUT / FPR95 / DetectionError / AUTC for two score populations. Needs viyog[metrics].

Fitted attributes: dorm_idx_, id_profile_, id_score_mean_, n_channels_, layer_name_.

Results

Across 20 architectures on CIFAR-100 (ResNet/DenseNet/ConvNeXt/Swin/ViT and edge backbones), the dormant-band roughness score reaches AUROC ≈ 0.966 for adversarial detection (ID vs ADV) and ≈ 0.824 for OOD vs ADV — where logit detectors (Energy/MSP/MaxLogit/GEN) are near-blind to adversarials (≤ 0.69) and feature-distance detectors cost 4.5–40 MB of state versus Viyog's ~0.3 KB. See the accompanying paper.

License

MIT — see LICENSE.

Citation

If you use Viyog in academic work, please cite the accompanying paper "Viyog: Separating Adversarial and Out-of-Distribution."