Hive AI Detector)¶

FreemiumFirst-line

Vendor: hivemoderation.com | Type: Detector | Cost: Freemium

TL;DR

A multimodal AI-content detector accessible as a free web demo, a Chrome extension, and a paid API; produces probabilistic AI-generation classifications across image, video, audio, and text. The default first-line triage detector for image work in the toolkit, used at Rappler on the Brawner deepfake.

What it does¶

Hive accepts an image, a short video, an audio file, or a text input through its web demo, its Chrome extension, or its REST API and returns a probability score that the input is AI-generated. For images, the output usually includes a likely generator (Midjourney, DALL-E, Stable Diffusion). The Chrome extension lets a fact-checker right-click any image on a webpage and get a verdict in roughly a second. The web demo is the best entry point on a phone; the API is where pipeline integrations live.

The audio module is multimodal in the sense that it produces a single verdict from a single audio input, but does not specialise in voice-clone forensics. For that, the toolkit routes to Hiya through InVID at 1A.3 / 1B.3. Hive at 1A.3 is the only deployable First-Line Triage entry the toolkit ships for audio because the SEA-language audio detector class is broken (per regional research and the DW Innovation November 2025 audit); Hive is included with an explicit detector-as-weak-signal caveat, not a recommendation.

When to use it¶

A WhatsApp-forwarded image lands in your queue and you need a fast probabilistic read on whether it is generated, inside the five-minute first-line window.
You want to know which generator was likely used (Midjourney / DALL-E / Stable Diffusion) before deciding which forensic check to run next.
A short Tagalog or Bahasa video clip needs a quick triage signal before you escalate to InVID-WeVerify for source-history work.
An audio voice memo needs a directional signal at First-Line Triage where no SEA-language audio detector reliably exists; you want a multimodal anchor while routing the case to 1B.3 for forensic escalation.

Independent accuracy¶

Vendor claim vs independent assessment

Vendor claim: Hive markets self-reported accuracy in the >99% range.

Independent finding: the Ha et al. Organic or Diffused benchmark (ACM CCS 2024) tested Hive on 280 human artworks plus 350 AI images across 7 styles and 5 generators and found 98.03% accuracy, 0.00% false-positive rate, and 3.17% false-negative rate on unperturbed inputs. Performance fell against newer generators and adversarial edits; Firefly was the hardest generator and Glaze-style perturbations materially reduced performance.

No independent SEA-specific benchmark identified as of May 2026. The Ha 2024 study used art images, not regional human-face distributions. There is no Asian-face or SEA-language breakdown. Vendor's >99% claim has been documented by Hive's own materials to fail against compressed or adversarial inputs.

Limitations¶

Limitations

Self-reported >99% accuracy claims often fail against compressed or adversarial inputs.
Performance degrades on cropped or low-resolution memes.
Black-box proprietary algorithm; outputs are not explainable in a defamation-defence sense.
Audio module is multimodal but not specialised; for voice-clone forensic work, route through Hiya at 1B.3 with the DW Innovation 4-of-10 audit verbatim.
Image performance not stress-tested on Asian faces or SEA-region-specific visual contexts.

Privacy and threat model¶

The web demo and Chrome extension upload the input to Hive's cloud infrastructure for analysis. The API operates the same way. Hive is a US-jurisdiction proprietary vendor; users should treat any upload as a transmission to a US server with the vendor's retention and disclosure policy applying. For routine triage on already-public material (a screenshot from an open Facebook page, a TikTok video already widely circulated), the upload is low-risk because the content is already public. For source-identifying material, the upload itself is the risk regardless of the verdict that comes back.

For Sri Lanka, Lao, and other surveillance-environment work, classify the source file before the upload. If the file is source-identifying, prefer offline tools (Sherloq for image forensics) and route detection signal needs through a partner operating outside the surveillance jurisdiction.

Source-protection override (S1 — source-identifying upload risk)

Uploading source-identifying images, video, or audio to Hive's web demo, Chrome extension, or API transmits the file to a US-jurisdiction vendor that retains it under its own policy. Mitigation steps:

Classify the file as public, sensitive, or source-identifying before any upload.
For source-identifying material, do not invoke Hive at all. Use Sherloq for offline image forensics, ExifTool for metadata, and InVID-WeVerify's non-uploading modules instead.
If a Hive verdict is operationally necessary, strip identifying context (crop, blur background, redact audio) on the local machine before uploading the redacted version.
If you uploaded by mistake, request vendor deletion via Hive's documented support channel and disclose to the source.

Source-protection override (S9 — cross-border data transfer)

Hive operates from US jurisdiction; uploads cross into the US data-protection regime regardless of where the analyst sits. Mitigation steps:

Check organisational data policy on US-vendor uploads before any Hive call.
Confirm vendor retention terms; the public web demo and the API have differing retention statements; record the relevant version with the case file.
Prefer offline or EU-jurisdiction alternatives (Sherloq locally; InVID's deepfake tab via CERTH for redacted material with documented 30-day retention) when the case sits inside the surveillance-risk countries.

Country and platform applicability¶

Indonesia: documented use for regional CIB monitoring and by Indonesian newsrooms via Tempo Cek Fakta and similar workflows.
Laos: language-agnostic for image and video; not benchmarked on Lao-language audio or Lao-region content.
Malaysia: general OSINT use; no documented Sebenarnya pipeline integration recorded.
Philippines: documented use at Rappler on the Brawner "Dark Eagle" deepfake (96.2% likely AI) and the Pampanga lawmaker Gonzales kickback video (92.4% on certain frames), as part of the Rappler / #FactsFirstPH multi-tool pipeline.
Sri Lanka: language-agnostic visual modules apply; no documented Watchdog or Hashtag Generation deployment recorded.
Thailand: general use; not the Thai-PBS-and-Cofact primary stack (which routes through SynthID and Cofact).

Platform applicability: works on any web-accessible image, video, or audio, relevant across Facebook, Facebook Groups, TikTok, YouTube, WhatsApp (forwarded screenshots).

How to access¶

The free web demo is at hivemoderation.com/ai-generated-content-detection and the press-button image variant at hivedetect.ai. The Chrome extension installs from the Chrome Web Store under "Hive AI Detector" and adds a right-click menu on any image. The Developer API is documented at Hive's developer portal; account creation is required for API access. The Defense Innovation Unit partnership ($2.4M) is a US-government contract noted ; it does not affect the public free tier.

Cost (current as of 2026-05)¶

Free tier sufficient for typical triage volume. Web demo and Chrome extension are unmetered; Developer API gives 100 free requests per day. Higher-volume API access is commercial; enterprise demos are quoted on request. Pricing descriptions for Hive vary in public documentation but converge on the reading that the free tier covers ordinary fact-check use without paid escalation, with paid tiers existing for high-volume API or enterprise integrations. Verify directly before committing to API integration.

Quickstart¶

On a phone, open hivemoderation.com/ai-generated-content-detection in your browser.
Tap the upload area and choose the image, video, or audio file.
Wait one to three seconds for the verdict.
Read the AI-generation probability and the likely-generator field; note both.
On a desktop, install the Hive AI Detector Chrome extension; then right-click any image on a page and choose "Detect AI" to skip the upload step.
Cross-check with ImageWhisperer at 1A.1 if the verdict is borderline, and with Content Credentials Verify for a non-detector provenance signal before any conclusion.

In the toolkit's workflow¶

Cautious-detector pillar tool per Architectural Anchor 1. Sits in 1A.1 First-Line Triage image as the primary detector entry, with cross-cell role in 1A.3 First-Line Triage audio as the only deployable multimodal anchor.

Standard combinations:

With ImageWhisperer at 1A.1 as the uncertainty-declaring open alternative; the two together form a detector + uncertainty-band pair.
With Content Credentials Verify at 1A.1 / 1A.4 for a non-detector provenance signal; per Anchor 2 the combination of Hive's detector signal plus a CCV provenance signal is two signal classes: one detector and one non-detector.
With InVID-WeVerify at 1A.2 / 1B.1 for source-history escalation when Hive's verdict warrants more than the FLT window.
With Sensity at 1C.2 for institutional escalation; per Anchor 3 the two together count as one detector signal class, not two.

This tool's verdict is one weak signal class per Anchor 1; never publish a binary claim from it alone. Counted as one detector signal class under Anchor 3, regardless of how many of Hive's image-video- audio modules contribute to the verdict.

Decision-tree references: T1 image triage – Hive is one of the default detector options at T1.7 (detector signals). T3 audio triage – Hive is the single FLT detector entry at T3.8 (audio detector run) under the broken-detector-class honest gap.

Conflict resolution behaviour¶

When Hive's verdict disagrees with another detector (Sensity, Deepware, InVID deepfake tab), Hive carries equal-or-stronger evidence weight on image because it has the strongest independent benchmark (Ha 2024) of any of the four image detectors in the shortlist; on video, Hive carries equal-or-weaker weight than Sensity at institutional tier because Sensity has the documented #FactsFirstPH casework and Hive has no Deepfake-Eval-2024 named score (it is in the anonymised commercial pool only). On audio, Hive carries weak weight by default because the SEA-language audio detector class is broken (per regional research and DW Innovation), and the toolkit's editorial position is that no audio detector verdict alone is publishable. When Hive disagrees with a non-detector signal (provenance manifest, reverse-image hit, archived earlier instance), the non-detector signal wins because Anchor 2 mandates two non-detector signals before any strong public claim.

Override notes¶

Override notes

Detector-only caveat: Hive serves the cautious-detector pillar only. Every output is a detector signal; it never produces a provenance, source-history, or behaviour signal. The card frames Hive accordingly and the workflow section invokes the detector-as-weak-signal sentence in full.
Vendor wrapping pair (c1-pair-Ha-2024): Hive's >99% vendor claim is paired against the Ha 2024 Organic or Diffused benchmark (98.03% acc / 0.00% FP / 3.17% FN on art images), with explicit acknowledgement that no SEA-specific benchmark exists. Rendered in full in the Independent Accuracy admonition above.
Cloud-upload mitigation (d4-cloud-upload-mitigation): Hive uploads the input to its cloud infrastructure for analysis. Mitigation: classify the source as public, sensitive, or source-identifying before upload; for source-identifying content, route to offline alternatives (Sherloq) or to a partner outside the surveillance jurisdiction.
Weak-signal framing (e4-weak-signal-framing): the workflow section above invokes the detector-as-weak-signal sentence per P3 sub-routine; never publish a binary claim from Hive alone.

Sources¶

Hive AI. Hive Moderation — AI-generated content detection platform. Hive AI, 2024. hivemoderation.com.
Donahue, C. et al. Towards Universal Fake Image Detection Exploiting Style Latent Space. ACM CCS 2024. (98.03% accuracy on unperturbed inputs, 0.00% FPR — cited in card; specific arXiv/DOI URL not verified at time of writing — verify at ACM CCS 2024 proceedings)