Industries
Dating apps
Keep fake profiles, AI-generated photos, and deepfaked video calls off your platform — before they turn into romance scams.
$1.1B
reported U.S. losses to romance scams in a single yearSource: FTC
- AI-generated profile photo detection
- Deepfake screening on verification selfies
- Flag synthetic personas early
Where dating apps get hit
Scammers build trust with fabricated personas — AI-generated profile pictures, deepfaked video calls — then steer matches toward money or fake investments. The whole con depends on the fake looking real.
How ScamAI helps
Screen profile photos for AI generation at signup, check verification selfies for deepfakes and presentation attacks, and surface synthetic-persona risk signals early — protecting users while the con is still warming up.
Safety duties are shifting from optional to statutory
The UK's Online Safety Act puts duties on user-to-user services to assess and mitigate illegal content, and romance fraud — a crime built on fake personas — sits squarely inside that duty, while the EU's Digital Services Act requires platforms to act on illegal content and be transparent about moderation. Every one of these regimes presumes the platform can tell fabricated personas from real ones. ScamAI supplies that: AI-generation screening on profile photos, deepfake and presentation-attack detection on verification selfies, and risk signals your trust and safety team can document.
From signup to verified profile
At signup, every profile photo is scored for AI generation — one REST API call in your upload path — catching the fabricated faces that anchor scam personas before a profile reaches the match queue. At verification, Eva V1.6 screens the badge selfie for deepfakes and presentation attacks, so the badge means what users think it means instead of laundering a fake into credibility. Real-time screening inside your own in-app video calls needs the embeddable on-device agent, still in development; today the checkpoints are photo upload and verification.
/ROI
What this is worth
- Loss avoidance: as an illustration, at 50,000 checks a month, catching just 0.2% more synthetic media is ~100 frauds stopped — at a $10k average loss, that's ~$1M a month that never walks out the door.
- Review time: evidence-backed results cut manual review from minutes to seconds, so analysts handle the small flagged fraction instead of screening everything.
- Compliance posture: explainable, auditable results give regulators and auditors evidence, not a black-box score.
Common questions
How do dating apps detect fake profiles that use AI-generated photos?
By scoring photos at upload rather than waiting for user reports. Generated faces carry statistical artifacts that detection models identify even when the image looks flawless. ScamAI's Eva V1.6 screens every profile photo at signup and on photo changes, returning a result before the profile enters matching. Front-door screening matters because AI-generated faces defeat the older defense: reverse-image search only catches photos stolen from somewhere else. A generated face has no source to find; detection must examine the image itself.
Can deepfakes be detected on live video calls inside a dating app?
Real-time in-call screening is what the loophole demands — victims are told to "insist on a video call," and scammers answer with face-swapped video that passes inspection — but embedding it inside your own video stack requires the on-device agent, which is in development. What runs today is screening at the checkpoints you already control: profile photos at upload and verification selfies, both scored through the REST API for face swaps and presentation attacks. Platforms use those signals to gate a persona for review, warn the other user, or feed the account's risk score.
What does the UK Online Safety Act mean for dating platforms?
The Online Safety Act puts duties on user-to-user services, dating apps included, to assess the risk of illegal content and take proportionate measures against it, with romance fraud among the harms in scope. Larger categorized services face expectations to offer identity-verification options. Practically, a platform must demonstrate systems that find fabricated personas: photo screening for AI generation, verification selfies checked for deepfakes, and documented enforcement. Detection results with evidence support the mitigation itself and the risk-assessment paperwork the regime requires.
See ScamAI on Dating apps
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