Best Deepfake Detectors

Ranked: best deepfake detectors

This ranking reflects free-tier availability, published use in media-verification and research contexts, and breadth of manipulation types covered. Every detector's accuracy degrades on re-compressed or low-resolution video.

1. Hive Moderation — Enterprise-grade AI-generated content detector covering images, video, and audio. Free web demo accepts uploads or URLs and returns a probability score with no account required. Widely cited in academic papers and newsroom verification workflows. Covers a broader range of manipulation types than most free tools. Limitation: full API and batch processing require an enterprise agreement.
2. Sensity — Purpose-built deepfake detection platform focused on face-swap, identity replacement, and lip-sync manipulation. Designed for media organisations, fraud investigators, and trust-and-safety teams. Trial access available. Limitation: enterprise pricing for production use; more specialised than Hive for general AI-media detection.
3. Deepware Scanner — Consumer-facing free scanner. Accepts a video URL (including YouTube and some social platforms) and returns a deepfake probability score with a breakdown of analysed frames. No account required; straightforward interface. Limitation: consumer-focused; less granular output than enterprise tools; accuracy may be lower on face-swap variants not in its training set.
4. FactHeck — AI-detection module that flags synthetic media as part of a broader fact-check pipeline. Submit a TikTok, Instagram, or YouTube URL and FactHeck returns both a deepfake/AI-generated probability and a claim-by-claim verdict on the factual content of the post. Best when you want both checks in one step rather than running each separately. Limitation: requires a public social post URL; does not accept standalone video files; free tier is five checks per day.

Side-by-side comparison

Why deepfake detection is hard

Deepfake detectors look for statistical artefacts introduced by AI generation — subtle inconsistencies in facial boundaries, lighting physics, or temporal coherence between frames. The core problem is that re-compression partially destroys these artefacts. Every time a video is uploaded to a social platform, forwarded via WhatsApp, or saved and re-shared, the encoding strips away the fine-grained signal detectors rely on. A video that has passed through several platforms before you see it may be impossible to detect with high confidence even if it was originally highly synthetic.

A second problem is the adversarial arms race: each new generation of deepfake models is trained partly to evade the current detection methods. Academic benchmarks from 2021 or 2022 do not reliably predict a tool's performance on deepfakes generated by 2024 or later models. Treat any accuracy figure you see as a snapshot, not a guarantee.

Independent evaluation is limited. The Deepfake Detection Challenge, run by Facebook AI Research, AWS, Microsoft, and academic partners, produced one of the largest public benchmarks; the top-performing models achieved around 65% accuracy on the withheld test set — well above random, but far from reliable.

Visual checks to complement detection tools

No tool should be your only check. Visual inspection remains useful, particularly for:

• Hairline and ear edges: AI face-swaps often blur or flicker where the synthetic face meets the original hair.
• Lighting mismatches: The imposed face may carry lighting from its training images rather than the scene it has been placed in.
• Lip-sync drift: A fraction-of-a-second lag between mouth movement and audio is a common tell.
• Eye reflections: Natural eyes have consistent catchlights; AI-generated faces sometimes miss or distort them.

For a more detailed walkthrough of visual warning signs, see the guide on how to spot a deepfake video.

Source verification: the strongest defence

The single most reliable way to identify a deepfake is to find the original, credible source of the footage. If a video claims to show a specific event and no established news organisation has covered that event, that absence is itself a strong signal. Reverse image search a key frame via Google Lens or InVID/WeVerify to find earlier or alternative appearances. A genuine viral event will generally have multiple sources; a deepfake will often have only the single account that posted it.

Frequently asked questions