Engines

Antidote ships with seven engines. They share the same UI shell, progress stream, threshold graph, and healing hand‑off, but each produces its own result schema. Pick the one that matches your data type and the kind of problem you’re worried about. This page explains every engine from a usage perspective. Algorithmic details (model choices, calibration math, defaults) live in our internal references; the operational fields below are what you’ll actually triage against.

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Quick reference

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Mislabel 2D

Use it when you have a standard image classification dataset (class_a/, class_b/, …) and suspect some images were mislabeled. Inputs. Folder‑structured image dataset (JPEG, PNG, WebP, TIFF). Per‑image fields: What to expect. A ranked list of suspected mislabels, each with a proposed correct label and a confidence score. Triage from highest gap to lowest.

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Mislabel 3D (medical imaging)

Use it when you’re working with CT, MRI, or ultrasound data organized per patient or per volume. Inputs.

• .nii.gz volumes.
• NIfTI‑style PNG stacks (label_*/case001_0042.png).
• Optional segmentation metadata to restrict analysis to an anatomical region of interest.

Per‑slice fields: everything Mislabel 2D returns, plus:

• Case‑level probability, whether the whole volume is suspect.
• Neighbor review, whether slices directly above and below disagree.
• Multi‑label probability, probability across the full label set for multi‑label cases.

What to expect. Flags are grouped by case. Singleton flags (only one suspect slice in an otherwise consistent case) are automatically suppressed unless the neighborhood agrees.

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Mislabel Broad (OOD + mislabel)

A superset of Mislabel 2D. Besides wrong labels, it finds out‑of‑distribution images, things that don’t belong in the dataset at all (logos, screenshots, unrelated photos, corrupted files). Use it when the dataset is from mixed sources, scraped from the web, or otherwise possibly contaminated. Issue types. Every flag is tagged with exactly one:

• mislabel, definitely the wrong class.
• ood, does not belong in the dataset.
• low_quality, the model is uncertain but no clear alternative.

Result categories in the UI:

• Mislabeled, high confidence wrong label.
• MislabeledCandidate, probable but less certain.
• MislabeledCandidateWeak, weak signal.
• Outlier, OOD, not a mislabel.

What to expect. A cleaner picture than Mislabel 2D alone, since it separates “wrong class” from “should not be in the dataset” before you triage.

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Poisoning

Use it when you’re ingesting data from untrusted sources (scraped web, user uploads, third‑party dumps) before training a production model. Catches. Backdoor triggers, adversarial patches, Glaze and Nightshade style‑transfer attacks, and any image tampered with to manipulate training. Per‑image fields: Tunable sensitivity: What to expect. Suspect images come with a visual explanation: highlighted trigger regions, feature heatmaps, and a plain‑language summary of which signals fired.

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Text Analysis

Use it when you’re building fine‑tuning, RAG, or instruction datasets and need to sanitize them before training or publishing. Catches three problem classes in one pass:

1. Leaked secrets and PII, API keys, tokens, passwords, emails, IPs.
2. Prompt injection attempts, system‑prompt overrides, jailbreaks.
3. Topic outliers, paragraphs that don’t belong with the rest.

Per‑finding fields: Built‑in patterns. GitHub PATs, AWS access keys, JWTs, generic api_key=… / password=…, emails, IPv4, IPv6, plus a high‑entropy token heuristic. Built‑in injection phrases. A curated list of 12 phrases (ignore previous instructions, reveal the system prompt, disregard the earlier rules, …) plus jailbreak and role‑reassignment heuristics. What to expect. A searchable table of findings per document, with the match highlighted in context. Triage feedback (confirm / dismiss) is remembered across subsequent scans, so repeat patterns stop nagging you.

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Bias & Shortcut

Use it when you’re about to train a model and want to know whether it will actually learn the task or cheat on a spurious cue. Catches:

• Background color correlates with label.
• Watermarks or logos leak the class.
• Resolution, aspect ratio, or file metadata is informative.
• A confounder column (site, scanner, date) determines the outcome.

Per‑hypothesis fields: Each hypothesis is a concrete claim (for example, “images of class A are brighter than class B”): Per‑image flags. is_bias (subtle), is_shortcut (strong), plus the underlying score. Confounder columns. Drop an extras.csv alongside your images with columns like site, scanner, date, patient_id. The engine tests each one for label correlation and train/test shift. What to expect. A dedicated bias results page (/datasets/:id/bias) listing every hypothesis with its decision and visualizations. Confirmed shortcuts come with specific mitigations.

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Mask Quality

Use it when you’re preparing data for semantic or instance segmentation. Catches. Missing labels, inconsistent sizing, class imbalance, invalid pixel values. What to expect. Per‑mask flags with a textual description of what’s wrong and an overlay preview against the original image.

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Picking the right engine