AI Video Detectors, Compared

An AI video detector estimates whether a video was made or altered by a machine. The useful ones split into two groups, because two very different problems share the name.

The first problem is a fully generated scene that never happened, the kind Sora or Veo produces. The second is a deepfake, where a real recording has a swapped or puppeted face. Provenance checks handle the first well. Trained artifact detectors handle the second.

Hive AI and Reality Defender are the two most-compared detectors in 2026. This page sets them side by side, covers the free options, and gives you an ordered process for checking a clip before you accuse anyone of anything.

Hive AI vs. Reality Defender: Side-by-Side

Two Problems Share One Name

AI video detection covers two separate technical problems, and a tool built for one is often weak at the other. Knowing which one you have decides which tool you reach for.

A fully generated clip has no camera and no original footage. Every pixel came out of a model, so the strongest check is provenance: the major generators mark their own output, and those marks are far more reliable than any guess made from pixels.

A deepfake is different. Real footage exists, and a model has replaced or driven a face inside it. There is no generator mark to find, so you need a detector trained to spot the seams where synthetic pixels meet real ones.

• Fully generated scene: check provenance first, then a detector as backup.
• Face swap on real video: provenance will find nothing useful, so go straight to a detector.
• Mixed cases exist: a generated clip can be re-edited, and a real clip can be partly regenerated.
• Most tool roundups blur these two, which is why their rankings disagree with each other.

How to Tell If a Video Is AI Generated

Work in a fixed order: provenance, then detector, then manual inspection. Each step is cheaper and more reliable than the one after it, so skipping ahead wastes effort and invites mistakes.

Step one is provenance. Download the original file rather than screen-recording it, then run it through a Content Credentials check and a SynthID check. A signed manifest or a detected watermark is real evidence, not a probability.

Step two is a detector, and only if provenance came back empty. Feed the highest-quality copy you can get, because compression destroys the fine artifacts these models read. Note the score, but treat it as a lead rather than a finding.

Step three is manual inspection, which still catches things software misses. Watch the clip at quarter speed and full screen, then check the tells listed further down this page. If two independent steps agree, your call is much safer.

• 1. Get the original file. Ask the source for it, or download it rather than re-recording the screen.
• 2. Check provenance. Look for C2PA Content Credentials and a SynthID watermark before anything else.
• 3. Run a detector. Use one built for the problem you have, generated scene or face swap.
• 4. Inspect manually. Slow the footage down and look at hands, text, physics, lighting, and lip sync.
• 5. Check the source. Who posted it first, and does any other camera show the same event?

Why Provenance Beats Detection for Generated Video

Provenance is the strongest check for a fully generated clip, because the generator itself left the signal instead of a third party guessing. Two systems matter in 2026.

C2PA is the open standard behind Content Credentials, a cryptographically signed manifest that travels inside the file and records how the content was made. OpenAI states that Sora output carries C2PA metadata alongside its visible moving logo. Whether the copy in front of you still carries that manifest is a separate question, so test the actual file with a C2PA checker such as Content Credentials Verify rather than assuming the mark made it through.

SynthID is Google DeepMind's invisible watermark, applied across Google's generative models and readable in the Gemini app. Google's own support page says you can upload one file at a time, under 100 MB and under 90 seconds of video, and ask whether it was made with Google AI.

For which generators actually mark their output, use our roster at which AI models watermark their output rather than trusting a tool's marketing page. Background on the standard sits in our C2PA and content provenance guide, and the checker walkthrough is in our AI watermark detector guide.

• Content Credentials Verify: free, browser-based, reads the C2PA manifest attached to a file.
• Gemini SynthID check: free in the app, covers Google-made images, video, and audio only.
• Coverage gap: neither one detects output from a generator that never marked it.
• Sora specifics: see our Sora watermark guide for what that mark looks like and where it appears.

No Metadata Does Not Mean Not AI

A missing Content Credential proves almost nothing, because ordinary handling strips file metadata. This is the single most common mistake people make when checking a video.

Most social platforms re-encode every upload. That process rewrites the container and routinely drops the C2PA manifest, so a genuine Sora clip reposted twice can arrive with a clean file and no provenance at all.

Embedded signals survive better than file metadata, which is the practical difference between the two approaches. Google says the SynthID watermark generally persists through rescaling, recoloring, and compression, while also warning that after many alterations it stops being detectable.

Deliberate removal is quick as well. NewsGuard, testing Sora in October 2025, reported that a free online tool stripped the visible Sora watermark from an uploaded clip in about four minutes, leaving a file that could pass as authentic to an unsuspecting viewer. Marks can also go missing without anyone trying: what survives depends on how a file was downloaded and re-shared, so treat provenance as something to confirm on the earliest copy you can obtain rather than something a generator guarantees.

So treat an empty provenance result as an unknown, never as a clearance. Move to a detector and manual inspection instead of declaring the video real. Stripping marks deliberately is a separate topic, covered in our AI video watermark removers comparison.

• Re-uploading, trimming, and screen-recording all commonly remove file metadata.
• An embedded watermark can outlive the metadata, but not indefinitely.
• Absence of a mark is weak evidence. Presence of a valid signed mark is strong evidence.
• Always test the earliest, highest-quality copy you can obtain.
• A clip marked at the source can still reach you clean, either through ordinary re-encoding or through deliberate stripping.

Hive AI vs. Reality Defender

Hive AI and Reality Defender both cover generated video and manipulated faces, and they differ mainly in who they are built for and what they hand back. Both read pixel and frame artifacts rather than provenance metadata.

Hive AI sells detection as part of a broader moderation stack used by digital platforms. Its deepfake model locates faces in each frame and classifies whether each one is manipulated, and Hive also publishes free access through a browser checker, a Chrome extension, and a social bot.

Reality Defender targets fraud rather than moderation, with products aimed at contact centers, video meetings, and identity checks. Its differentiator is the report: each result explains which artifacts were found and where in the media they appear, which matters when a decision has to be defended later.

Both vendors advertise accuracy figures. Treat those as vendor claims, since independent testing consistently produces lower and more variable numbers than marketing pages do, especially on compressed or short clips.

• Pick Hive AI if you are screening high volumes of user uploads, or you just want a free browser check.
• Pick Reality Defender if a fake could authorize a payment, unlock an account, or reach an executive.
• Both cover video, image, and audio, and both sell API access.
• Neither reads C2PA or SynthID, so run a provenance check separately.

Sensity, Deepware, AI or Not, and Others

Beyond the two leaders, three more tools show up in most 2026 shortlists, each serving a narrower job. None of them replaces a provenance check.

Sensity AI is positioned for forensic and investigative work across video, image, and audio, with API and on-premise deployment for organizations that cannot send files to a public endpoint. That deployment option is often the deciding factor for newsrooms and legal teams handling sensitive material.

Deepware Scanner is the long-running free video scanner: submit a link or a file and get a verdict at no cost. It is video-only, and it publishes no independent benchmark of its current accuracy, so treat it as a second opinion rather than a primary check.

AI or Not is the fastest casual option, covering images, audio, and MP4 video with a simple result and limited free checks. It is well suited to a quick sanity test and poorly suited to anything you need to defend.

• Sensity AI: forensic depth, on-premise option, built for investigators and enterprises.
• Deepware Scanner: free, video-only, no published independent benchmark, useful as corroboration.
• AI or Not: fast and simple, limited free tier, thin on explanation.
• For still frames: our AI image detectors comparison covers pulling a frame and testing it separately.

Manual Tells That Still Work

Human inspection still catches fakes that detectors miss, especially on short clips where models have little to read. Five checks earn their time.

Hands and object contact remain the weakest point in generated video. Watch fingers as they grip, release, or cross in front of each other, and watch where a hand meets a cup, a door, or another person.

Text inside the frame is the second giveaway. Signage, badges, screens, and number plates often drift, warp between frames, or resolve into letter-shaped nonsense when you pause.

Physics and lighting continuity round it out. Look for shadows that point the wrong way, reflections that do not match the scene, and hair or clothing that moves without a cause. On deepfakes specifically, watch the jawline during fast head turns and check whether lip movement lands exactly on the audio.

• Hands: finger counts changing, grips passing through objects, joints bending oddly.
• Text in frame: signs and screens that shimmer or spell nothing readable.
• Physics: liquids, cloth, crowds, and collisions that behave inconsistently between shots.
• Lighting: shadow direction, reflections, and color temperature that shift without reason.
• Audio sync: consonants that do not match mouth shapes, and a voice with no room tone.

Getting It Wrong Hurts Both Ways

A wrong call in either direction causes real damage, which is why a probability score should never be your final word. Both failure modes are common.

Call a real video fake and you can destroy a person's credibility, discard genuine evidence, or hand a wrongdoer an easy defence. This failure grows more likely as detectors get applied to compressed, cropped, and reposted footage, which is most footage online.

Call a fake video real and you get the fraud outcome: an authorized payment, a leaked credential, or a false story that spreads faster than any correction. Finance and executive impersonation is where this bites hardest.

The workable rule is to require two independent signals before acting, and to state your confidence in words rather than percentages. A verified provenance manifest plus a clean manual inspection is a decision. A single 87% score is not.

• Detectors produce false positives on genuine footage, especially low-quality footage.
• Detectors miss fakes made with newer models than they were trained on.
• Require two agreeing signals before you act on the result.
• Record what you checked and when, since tools and model versions change monthly.

Which AI Video Detector Should You Use?

Pick by the problem you have and by what happens after the result. Four situations cover almost everyone.

For a one-off suspicious clip, start free: run a Content Credentials check, ask the Gemini app about SynthID, then use Hive's free checker and inspect the footage yourself. That costs nothing and answers most questions.

For a platform moderating uploads, Hive AI fits, because usage-based API detection sits alongside the rest of the moderation stack you already need. For a fraud or identity team, Reality Defender fits, because artifact-level reporting supports decisions people will challenge.

For newsroom and investigative work, weigh Sensity AI for its on-premise deployment, and keep provenance as the first step in every workflow. Verify current pricing on each vendor's own page before committing, since plans and limits change often.

• One-off check: free provenance tools plus a free detector plus your own eyes.
• Platform scale: Hive AI, integrated with existing moderation models.
• Fraud and identity: Reality Defender, for real-time coverage and defensible reports.
• Investigations: Sensity AI, where files must not leave your infrastructure.

The Verdict

There is no single best AI video detector, because generated scenes and deepfaked faces are different problems with different evidence. Provenance signals answer the first far better than any detector, and trained artifact models are the only real option for the second.

Among detectors, Hive AI suits platforms moderating volume and anyone who wants a capable free check, while Reality Defender suits fraud and identity teams who need to explain a result rather than just report a number. Sensity AI covers investigative work needing on-premise handling, and Deepware Scanner and AI or Not serve as quick free second opinions.

Run the sequence rather than trusting one score: get the original file, check Content Credentials and SynthID, run a detector suited to your problem, then inspect hands, on-screen text, physics, lighting, and lip sync yourself. Treat an empty provenance result as unknown rather than clean, and require two agreeing signals before you act on any clip.