Skip to content

Deepware Scanner

FreemiumFirst-line

Vendor: deepware.ai | Type: Detector | Cost: Freemium

TL;DR

A press-button web and mobile video deepfake scanner used by Tempo Cek Fakta on the Dr Terawan diabetes-drug deepfake and by Fact Crescendo Sri Lanka on the Dhammika Perera deepfake. Documented to flip to "no deepfake detected" on a reduced-resolution, edited Obama clip. Treat the verdict as one weak signal only.

What it does

Deepware Scanner accepts a video file or a URL pointing to one and returns a probability score that the video has been manipulated. The web interface is at scanner.deepware.ai; a mobile companion extends the same workflow to a phone. Output is a single number between 0 and 1 with a verdict label ("deepfake detected" / "no deepfake detected"). The tool is open-source-oriented in that underlying models and some tooling are public, with commercial APIs and a low-cost paid tier on top.

The toolkit ships Deepware as the alternative entry in 1A.2 First-Line Triage video alongside InVID-WeVerify (which is the primary because of its source-history pillar coverage). Deepware is the press-button option when the user just wants a directional detector signal in the FLT five-minute window. The video detector class is structurally constrained: regional research found the best commercial video detector in the Deepfake-Eval-2024 anonymised pool reaching only 0.78 accuracy on in-the-wild deepfakes. The Deepware verdict is treated explicitly as one weak signal under Anchor 1, not as a verdict.

When to use it

  • A short video clip in your queue needs a fast detector signal inside the five-minute first-line window before you decide whether to escalate to InVID-WeVerify for a thirty-minute desk pass.
  • You want a press-button option that requires no install or account, accessible on a phone in the field.
  • You are running a Tempo or Fact Crescendo SL-style triage workflow where the case has already been documented to use Deepware as the first detector pass.
  • A workshop scenario calls for demonstrating the qualitative failure mode (compressed / cropped video flips a deepfake to "no deepfake detected") that the WITNESS / Reuters Institute documented as a baseline limitation of the video detector class.

Independent accuracy

Vendor claim vs independent assessment

Vendor claim: Deepware does not publish a sharp headline accuracy figure; mid-tier accuracy in the 80-85% range is on record, characterised by Deepware as subject to "constant adversarial obsolescence."

Independent finding: WITNESS / Reuters Institute's April 2024 methodology piece tested public deepfake detectors on known examples, including a well-known Obama deepfake. Deepware judged a reduced-resolution, edited version of the Obama clip as "No Deepfake Detected" – a qualitative robustness failure, not a benchmark score. Regional research records this as the principal independent observation on Deepware.

No independent SEA-specific benchmark identified as of May 2026. The WITNESS / Reuters Institute test was an example-based audit, not a regional benchmark; no Asian-face or SEA-language breakdown is published.

Limitations

Limitations

  • Mid-tier accuracy in the 80-85% range; detection capabilities face constant adversarial obsolescence.
  • Documented to flip to "No Deepfake Detected" on a reduced-resolution, edited Obama clip.
  • SEA-platform-compressed-video benchmark missing ; WhatsApp, Facebook, and TikTok all transcode video on upload, so Deepware verdicts on platform-circulated video carry the asymmetric robustness risk.
  • No published per-language or Asian-face breakdown.

Privacy and threat model

The web scanner uploads the video file to Deepware's cloud infrastructure for analysis. Deepware is a US-jurisdiction vendor; 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 video (a clip already widely circulated on TikTok or Facebook), the upload is low-risk because the content is already public.

For source-identifying video, the upload itself is the risk regardless of the verdict; in surveillance-environment contexts (Sri Lanka, Lao), classify the source file before any upload. If the file is source-identifying, route to InVID-WeVerify for source-history work first (reverse-image, archived versions, metadata) and skip the detector tab.

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

Uploading source-identifying video to Deepware's web scanner transmits the file to a US-jurisdiction vendor under that vendor's retention policy. Mitigation steps:

  1. Classify the video as public, sensitive, or source- identifying before any upload.
  2. For source-identifying material, do not invoke Deepware. Use InVID-WeVerify's non-uploading keyframe and reverse-image modules for the source-history layer.
  3. If a Deepware verdict is operationally necessary, strip identifying context (crop the frame, blur backgrounds, remove audio) before uploading the redacted version.
  4. If you uploaded by mistake, request vendor deletion via the Deepware support contact and disclose to the source.

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

Deepware operates from US jurisdiction; uploads cross into the US data-protection regime. Mitigation steps:

  1. Check organisational policy on US-vendor uploads before any Deepware call.
  2. Record the vendor's retention statement at the time of upload with the case file; vendor terms can change.
  3. Prefer offline or EU-jurisdiction alternatives (InVID's deepfake tab via CERTH for redacted material with documented 30-day retention) when the source is in a surveillance-risk country.

Country and platform applicability

  • Indonesia: documented use by Tempo Cek Fakta on the Dr Terawan diabetes-drug deepfake.
  • Laos: language-agnostic; not benchmarked on Lao-region content.
  • Malaysia: no documented use recorded.
  • Philippines: no documented Rappler / VERA Files deployment recorded; the Philippines pipeline routes through Sensity at institutional tier and Hive at first-line.
  • Sri Lanka: documented use by Fact Crescendo Sri Lanka on the Dhammika Perera deepfake.
  • Thailand: no documented use recorded.

Platform applicability: works on any video file regardless of source platform. The WITNESS / Reuters finding above means the verdict should be treated as weakly informative for platform-compressed content (TikTok, Facebook, Facebook Groups, WhatsApp).

How to access

The web scanner is at scanner.deepware.ai. No account is required for free-tier scanning. A mobile companion extends the same workflow to a phone. Commercial APIs are documented at the Deepware developer portal; the paid tier is approximately $8/month .

Cost (current as of 2026-05)

Free for the public web scanner; commercial APIs available on a paid basis; the paid tier is roughly $8/month . Verify directly before any institutional integration as vendor pricing may have shifted.

Quickstart

  1. Open scanner.deepware.ai in your browser (works on phone or desktop).
  2. Upload the video file or paste a URL pointing to it.
  3. Wait for the analysis; Deepware returns a single probability score with a verdict label.
  4. Read the score; if the score is high but the file is platform-compressed (TikTok, Facebook, WhatsApp), treat the verdict as one weak signal and escalate to InVID-WeVerify for source-history work.
  5. If the score is low and the file is platform-compressed, do NOT treat the verdict as exoneration. The WITNESS / Reuters audit shows compression flips deepfake verdicts to "no deepfake detected" on documented examples.
  6. Pair the result with a non-detector signal (reverse-image hit, archived earlier instance, provenance manifest) before any conclusion.

In the toolkit's workflow

Cautious-detector pillar tool per Architectural Anchor 1. Sits in 1A.2 First-Line Triage video as the alternative to InVID-WeVerify. Deepware is the press-button detector option; InVID-WeVerify is the multi-pillar default.

Standard combinations:

  • With InVID-WeVerify at 1A.2 / 1B.1 as the source-history primary; Deepware gives the detector signal, InVID-WeVerify gives the source-history signal. Per Anchor 2 these combine as detector + non-detector: two signal classes.
  • With Hive at 1A.1 cross-cell when the same case has both a still image and a video; the two together give parallel detector signals counted as one signal class under Anchor 3.
  • With Sensity at 1C.2 for institutional escalation when the case warrants enterprise-grade detection.

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 when paired with another detector.

Decision-tree references: T2 video triage – Deepware is the press-button branch at T2.10 (video detector run); the WITNESS / Reuters qualitative failure is documented in the T2 stop-condition annotations on the same node.

Conflict resolution behaviour

When Deepware's verdict disagrees with another detector (InVID deepfake tab, Hive on a still frame, Sensity at institutional tier), Deepware carries lower evidence weight on platform-compressed video because the WITNESS / Reuters Institute documented qualitative failure on a known example. On uncompressed video, Deepware carries roughly equal weight to other detectors at First-Line Triage tier; the toolkit's editorial position is that no FLT video detector verdict alone is publishable. When Deepware disagrees with a non-detector signal (reverse-image hit, provenance manifest, 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

  • Weak-signal framing (e4-weak-signal-framing-binding): the workflow section above invokes the detector-as-weak-signal sentence per P3 sub-routine; never publish a binary claim from Deepware alone. The Limitations admonition carries the WITNESS / Reuters qualitative failure verbatim.

  • Vendor wrapping pair (c1-pair-vendor80-vs-regional research-qualitative-fail): Deepware's 80-85% range vendor figure is paired against the WITNESS / Reuters qualitative failure on the reduced-resolution Obama clip. Rendered in full in the Independent Accuracy admonition above.

  • Tempo and Fact Crescendo SL deployment (b4-score-2-Tempo-FactCrescendo-SL): regional documented use across Indonesia and Sri Lanka noted in the Country and platform applicability section; the deployment evidence raises the tool's documented-use score but does not override the e4 weak-signal-framing requirement.

Sources

  • Deepware. Deepware Scanner — deepfake video detection. Deepware, 2024. deepware.ai.
  • Deepware. Deepware Scanner — online video scanning tool. scanner.deepware.ai.
  • DW Innovation / EU DisinfoLab. Deepfake Detectors Put to the Test (methodology review cited in card). DW Innovation, November 2025. innovation.dw.com.