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Liveness Detection
Certified Selfie Liveness That Proves a Real Person
Shufti confirms a real person is in front of the camera, blocking printed photos, replayed video, 3D masks and AI-generated faces. Passive liveness runs from a single frame with nothing asked of the user, while 40+ ensemble models read skin texture, 3D depth and contour, plus synthetic patterns, injection and screen replay. Active liveness adds blink or head-turn prompts for extra assurance. iBeta Level 3 conformance, 0% false acceptance on spoofs, with a pass or fail returned by API.
A selfie can look genuine and still be a spoof. Printed photos, replayed videos, 3D masks, screen attacks, and AI-generated faces can all be used to fool basic selfie checks.
We built Shufti Liveness Detection to help businesses confirm that a real person is actually in front of the camera, while keeping the verification experience simple.
Passive Liveness Check liveness from a single frame without asking the user to blink, turn their head, or follow instructions.
40+ Ensemble Models Analyse skin texture, 3D depth, facial contours, synthetic patterns, injection attempts, and screen replays.
Active Liveness Add blink or head-turn prompts when an extra level of assurance is needed.
Built to Detect Modern Spoofs Protect against printed photos, replayed videos, 3D masks, and AI-generated faces.
iBeta Level 3 Conformance Independently tested against presentation attacks, with 0% false acceptance on the tested spoofs.
Simple API Results Get a clear pass or fail result along with anomaly flags to support the next step in your verification flow.
Built for Flexible Verification
Not every customer or use case needs the same level of liveness checking. Passive liveness can keep straightforward journeys fast, while active liveness can add more assurance when the risk requires it.
Who is it for?
Shufti Liveness Detection is built for banks, fintechs, marketplaces, regulated businesses, and identity verification teams that need to confirm users are real and reduce the risk of presentation attacks and synthetic identities.
If you're working with selfie verification today, we'd love to hear what you're seeing most often: replay attacks, AI-generated faces, injected captures, or traditional presentation attacks?
“Certified Selfie Liveness That Proves a Real Person”
Liveness Detection was submitted on Product Hunt and earned 0 upvotes and 1 comments, placing #61 on the daily leaderboard. Shufti confirms a real person is in front of the camera, blocking printed photos, replayed video, 3D masks and AI-generated faces. Passive liveness runs from a single frame with nothing asked of the user, while 40+ ensemble models read skin texture, 3D depth and contour, plus synthetic patterns, injection and screen replay. Active liveness adds blink or head-turn prompts for extra assurance. iBeta Level 3 conformance, 0% false acceptance on spoofs, with a pass or fail returned by API.
On the analytics side, Liveness Detection competes within API, Artificial Intelligence and Security — topics that collectively have 581.5k followers on Product Hunt. The dashboard above tracks how Liveness Detection performed against the three products that launched closest to it on the same day.
Who hunted Liveness Detection?
Liveness Detection was hunted by Aroosa Virk. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Liveness Detection including community comment highlights and product details, visit the product overview.
Aroosa Virk
Hello Product Hunt!
I’m Aroosa Virk from Shufti, and I’m excited to introduce Shufti Liveness Detection.
A selfie can look genuine and still be a spoof. Printed photos, replayed videos, 3D masks, screen attacks, and AI-generated faces can all be used to fool basic selfie checks.
We built Shufti Liveness Detection to help businesses confirm that a real person is actually in front of the camera, while keeping the verification experience simple.
Why Shufti Liveness Detection?
Passive Liveness
Check liveness from a single frame without asking the user to blink, turn their head, or follow instructions.
40+ Ensemble Models
Analyse skin texture, 3D depth, facial contours, synthetic patterns, injection attempts, and screen replays.
Active Liveness
Add blink or head-turn prompts when an extra level of assurance is needed.
Built to Detect Modern Spoofs
Protect against printed photos, replayed videos, 3D masks, and AI-generated faces.
iBeta Level 3 Conformance
Independently tested against presentation attacks, with 0% false acceptance on the tested spoofs.
Simple API Results
Get a clear pass or fail result along with anomaly flags to support the next step in your verification flow.
Built for Flexible Verification
Not every customer or use case needs the same level of liveness checking. Passive liveness can keep straightforward journeys fast, while active liveness can add more assurance when the risk requires it.
Who is it for?
Shufti Liveness Detection is built for banks, fintechs, marketplaces, regulated businesses, and identity verification teams that need to confirm users are real and reduce the risk of presentation attacks and synthetic identities.
If you're working with selfie verification today, we'd love to hear what you're seeing most often: replay attacks, AI-generated faces, injected captures, or traditional presentation attacks?
Shufti Liveness Detection is live on Product Hunt. Give it a try and let us know what you think.