What are category‑based blacklists in video content moderation? In short: they are policy-driven rules that identify and act on specific risk categories—like counterfeit goods, weapons, and pharmaceuticals—based on a defined taxonomy rather than simple keyword hits. They work by fusing multiple signals from the video (visuals, audio, on‑screen text, links) and applying jurisdictional and context rules to enforce platform policies.

Think of it as a customs checkpoint, not a spam net: instead of catching anything that “looks suspicious” by a word match, a category blacklist applies clear rules for each risk class and triggers precise actions (remove, age‑gate, limit reach, escalate).

What it is — and what it isn’t

• What it is: A taxonomy‑led enforcement layer that flags and sanctions specific prohibited or restricted categories in video (e.g., counterfeit sales pitches, firearm promotions or illegal modifications, unauthorized prescription drug sales).
• What it isn’t: A generic keyword blocklist, a single AI model, or a universal legal list. Platform rules differ by policy and jurisdiction; enforcement often blends automation with human review.

Why these categories matter now

• Legal and compliance pressure is rising. Very Large Online Platforms in the EU must run annual risk assessments, undergo independent audits, and increase transparency—requirements set out under the 2024 enforcement of the Digital Services Act. See the European Commission’s overview in the DSA questions and answers (2024).
• Public safety and commerce integrity. Platforms widely prohibit trading weapons and prescription drugs and ban counterfeit goods across their services, as reflected in Meta’s Community Standards on restricted goods and services.
• Scale and harm. Counterfeit trade remains a significant global problem, documented in the joint OECD/EUIPO Mapping Global Trade in Fakes 2025 report.

How category‑based blacklists differ from keyword blocklists

• Scope: Category blacklists encode policy logic (e.g., “sale of firearms is prohibited”) rather than words alone.
• Signals: They use multimodal inputs—computer vision for objects/actions, ASR for speech, OCR for on‑screen text, NLP for context, and link analysis—rather than text fields only.
• Outcomes: They enable nuanced actions (auto‑remove vs. age‑gate vs. send to review) aligned to risk tiers and context exceptions.
• Governance: They explicitly accommodate exceptions (news, education, verified sellers) and regional overlays.

Platforms’ public rules point in this direction. For example, TikTok’s policies prohibit trade and marketing of regulated goods and define “trade” to include contact sharing and redirections, across modalities, per the TikTok Regulated Commercial Activities section (2024–2025). YouTube documents restricted and regulated content (including weapons) with enforcement levers like age‑restrictions and demonetization in the YouTube Community Guidelines policy hub.

How enforcement works in video: a practical pipeline

1. Ingest and segment

• Sample frames and audio across the timeline; detect scene changes for coverage.

1. Multimodal analysis

• Computer vision: detect objects (e.g., guns, pill bottles, brand logos), scenes (shooting range vs. classroom), and actions (assembly, loading, handling).
• ASR: transcribe speech; extract entities (drug names, brand names) and sale phrases (e.g., “DM to buy,” “no prescription needed”).
• OCR: read on‑screen text from labels, prices, URLs, WhatsApp numbers, overlays.
• NLP/context: classify policy relevance; infer context such as news reporting, educational safety training, or reviews.
• Link analysis: resolve links/QRs in description/overlays; map to verified sellers or prohibited domains.

1. Fusion and risk scoring

• Combine model confidences with policy rules and jurisdiction overlays (geo, language, local law). Assign a risk score per category.

1. Actioning

• High confidence + prohibited intent: auto‑remove; apply account penalties.
• Mid confidence or sensitive edge cases: route to human review; consider age‑gating or limited distribution.
• Allowed context (e.g., news/education) or verified sellers: allow with restrictions or labels, where policy permits.

1. Logging and feedback

• Record decisions for audit and transparency; monitor precision/recall; feed reviewer outcomes back to model updates.

Research and industry practice increasingly integrate these multimodal components, including ASR/OCR‑fed policy classification and human‑in‑the‑loop review, as explored in a 2023 evaluation of LLMs for moderation tasks in the arXiv assessment of LLMs for content moderation.

Deep‑dive by category

1) Counterfeits

• Policy intent: Protect consumers and rights holders by prohibiting the promotion or sale of fake goods; aligned with commerce and IP rules across major platforms (see Meta Commerce Policies – Prohibited Content).
• Primary signals
• CV: brand/logo detection, packaging and label layout patterns.
• OCR: brand names and common misspellings; SKU/serials; prices; “authentic” claims.
• ASR: mentions of brands with sale language; discounts far below market.
• Link/metadata: affiliate links, contact info overlays, off‑platform checkout.
• Example decision
• ASR: “Authentic LV for $60, DM to buy.” OCR: “Louis V.” in overlay; CV: logo confidence 0.82. Fusion score crosses removal threshold → Remove, strike account, and (if policy supports) refer to rights‑owner program.
• Pitfalls and exceptions
• Legitimate reviews/unboxings; anti‑counterfeit educational content; satire. Use context classification and remove only when there is clear sale intent.
• Why it’s high‑priority
• Global counterfeit trade scale and consumer risk are highlighted in the OECD/EUIPO 2025 study on fakes.

2) Weapons

• Policy intent: Ban the sale or promotion of firearms/ammunition and illegal modifications; permit certain depictions with restrictions (e.g., age‑gating) for context such as news, education, or recreation. See the YouTube Community Guidelines hub and the advertiser‑oriented YouTube firearms‑related content page for examples of restricted treatments.
• Primary signals
• CV: detection of firearms, magazines, suppressors; action recognition (assembly, loading).
• ASR: phrases indicating sale (“buy now,” “ships without serial”), illegal mods.
• Links: vendor redirects; discount codes; import/export claims.
• Example decision
• CV: AR‑15 with magazine insertion; ASR: “ships without serial, no paperwork.” Fusion score high with illegal sale intent → Remove; escalate to Trust & Safety for potential law‑enforcement referral according to policy.
• Edge cases
• News segments, historical footage, safety training; sports shooting channels. Apply age‑gating or limited distribution where allowed, not removals, provided there’s no sale or illegal instruction.
• Regional overlays
• Local laws vary widely on weapons; ensure geo‑specific policy application consistent with platform standards like TikTok’s prohibited trade of regulated goods.

3) Pharmaceuticals

• Policy intent: Prohibit unauthorized sale or promotion of prescription drugs and controlled substances; allow verified pharmacies or authorized ads under strict rules; curb misleading claims.
• Primary signals
• ASR: drug names (brand/generic), dosage and claims; “no prescription needed,” “overnight shipping.”
• CV/OCR: pill bottles, blister packs, pharmacy leaflets; on‑screen prices and contact info.
• Links: checkout domains; messaging app handles; coupon codes.
• Example decision
• OCR: “Oxycodone 30 mg.” ASR: “No prescription needed—message me.” Link resolver: external checkout domain with prior blocks → Remove; block domain in link safety list; apply account penalty. If a verified pharmacy badge is present per policy, allow with restrictions.
• Regulatory anchors
• U.S. regulators warn against rogue online pharmacies and provide compliance expectations; see the FDA overview of illegal online pharmacies and the DEA Diversion Control portal for controlled substances guidance.

Governance and fairness

• Precision/recall tradeoffs
• Set category‑specific thresholds; auto‑remove only at high confidence with explicit sale/illicit‑instruction signals. Route mid‑range scores (e.g., 0.6–0.8) to human review.
• Contextual exceptions
• Allow policy‑recognized contexts (news reporting, education, harm‑reduction) with labels, age‑gating, or limited reach instead of removal.
• Verified entities
• Permit only verified sellers for certain regulated categories (e.g., licensed pharmacies) as allowed by platform rules.
• Transparency and auditability
• Log actions, provide user notices, and disclose enforcement metrics. DSA obligations for very large platforms—risk assessments, independent audits, and greater transparency—raise the bar for governance, as summarized in the Commission’s DSA Q&A (2024).
• Industry taxonomies
• Brand safety tiering can guide suitability decisions and adjacencies; the WFA’s GARM Brand Safety Floor & Suitability Framework (2022) remains influential even after GARM operations ceased in 2024.

Implementation checklist (practitioner’s view)

• Policy and taxonomy
• Define categories with examples, prohibited intents, and contextual allowances by jurisdiction.
• Multimodal detection
• Deploy CV (objects/actions), ASR, OCR, NLP context models; ensure multilingual support.
• Link and commerce signals
• Resolve short links/QRs; maintain verified seller allowlists and prohibited domain lists.
• Risk scoring and thresholds
• Calibrate per category; set auto‑remove and review bands; include geo overlays.
• Human‑in‑the‑loop
• Build reviewer guidance and exception labels; enable escalation paths.
• Governance
• Log decisions; enable appeals; publish transparency metrics and documentation.
• Evaluation
• Run A/B evaluations; monitor drift; retrain with adjudicated cases.

Metrics that matter

• Prevalence by category (uploads, views) and proactive removal rate.
• Precision, recall, false positive/negative rates by category at operating thresholds.
• Time‑to‑action (p95) for removals and age‑gates; reviewer agreement and throughput.
• Appeals rate and overturn rate by category.
• Geographic compliance alignment and audit findings.
• For benchmarks and public reporting patterns, examine platform transparency hubs such as the YouTube Community Guidelines enforcement report.

Common pitfalls to avoid

• Keyword‑only enforcement: misses visual‑only cues and implicit sale tactics. Use multimodal fusion.
• Ignoring context: remove educational/news content by mistake. Introduce explicit exception labeling.
• Overlooking off‑platform signals: fail to process overlays and links that move the sale elsewhere.
• One‑size‑fits‑all thresholds: treat weapons and pharma the same despite different legal risks.
• Jurisdictional blindness: apply uniform rules where local law diverges.

Key takeaways

• Category‑based blacklists operationalize policy as taxonomy, not just text filters.
• Effective video enforcement depends on multimodal detection, robust link analysis, and context‑aware rules.
• Governance—thresholds, human review, appeals, and transparent reporting—is as important as detection accuracy.
• For high‑risk categories like counterfeits, weapons, and pharmaceuticals, align enforcement with platform policies (e.g., TikTok and Meta), regulatory expectations (e.g., DSA in the EU), and public‑safety guidance (e.g., FDA/DEA) to protect users and the integrity of your platform.