Deepfake Detection
Catch the deepfake before it becomes a customer
3 followers
Catch the deepfake before it becomes a customer
3 followers
Shufti catches AI-generated faces, face swaps, synthetic identities and document deepfakes using 40+ ensemble models, not basic image scanning. The engine reads skin texture, motion, lighting and depth irregularities, and detects the digital fingerprints unique to AI generators. Accuracy holds after compression, screenshots and stripped metadata. Three layers defend the session: capture integrity, liveness and forensics. iBeta Level 3 conformance, 0% APCER and 0% BPCER.
Launch Team
Framer AI AgentsDesign and publish professional sites with AI
Promoted
Hello Product Hunt!
I’m Aroosa Virk from Shufti, and I’m excited to introduce Shufti Deepfake Detection.
AI-generated faces, face swaps, and synthetic identities are becoming harder to distinguish from genuine users. Checking whether an image simply looks real isn't always enough, especially when manipulated content can pass basic image and OCR checks.
We built Shufti Deepfake Detection to look beyond the surface and identify signals that can reveal AI-generated or manipulated identities.
Why Shufti Deepfake Detection?
40+ Ensemble Models
Combine multiple detection models instead of relying on a single image-scanning approach.
Look Beyond the Image
Analyze signals such as skin texture, motion, lighting, and depth irregularities that can indicate manipulation.
Detect AI Fingerprints
Identify digital patterns associated with AI-generated content and synthetic faces.
Resilient to Compression
Detection remains effective when images or videos have been compressed, screenshotted, or stripped of metadata.
Three Detection Layers
Combine capture integrity, liveness, and forensic analysis to examine different parts of the verification process.
Catch What OCR Can Miss
Detect AI-generated IDs, document manipulation, and splicing that may not be visible through text extraction alone.
Built for Identity Verification
Deepfake Detection works as part of the wider identity verification process, helping teams identify manipulated faces and documents before they become part of a customer journey.
Shufti Deepfake Detection has iBeta Level 3 conformance and reports 0% APCER and 0% BPCER under the relevant evaluation conditions.
Who is it for?
Shufti Deepfake Detection is built for banks, fintechs, marketplaces, regulated businesses, and fraud teams that need to detect synthetic identities and manipulated verification attempts.
As AI-generated fraud becomes more accessible, we'd love to hear from the community: what do you think will be the hardest deepfake attack to detect over the next few years?
Shufti Deepfake Detection is live on Product Hunt. Give it a try and let us know what you think.