Deepfake Detection Software Philippines: 6 Checks
Deepfake detection software checks whether AI swapped, generated, or altered a face in a selfie, video, or ID photo. For Philippine banks, e-wallets, and lenders, the right tool must also catch fakes injected straight into the camera feed. So evaluate attack coverage, capture channel, local test results, outputs, speed, and data handling before you sign.
What Deepfake Detection Software Does and Does Not Do
Deepfake detection software looks for signs that AI produced or manipulated a face. For example, typical targets are face swaps, fully synthetic faces, and edited ID portraits. Then the software returns a score or label, and your onboarding flow decides what to do with it.
It is one layer, though, not the whole defense. Our buyer’s guide to fraud detection software maps the rest of the stack. A deepfake detector does not prove that a person is physically present. It also does not confirm that the identity is real. For that reason, buyers usually pair it with the checks below.
| Control | What it answers | What it cannot answer |
|---|---|---|
| Deepfake detection | Did AI generate or alter this face? | Is a live person in front of the camera right now? |
| Liveness detection | Is this a live person, not a photo, screen, or mask? | Does this person own the ID they submitted? |
| Face match | Does the selfie match the ID portrait? | Is either image genuine? |
| Device checks | Is the app running on an emulator, rooted phone, or hooked camera? | Who the user is |
In practice, most deepfake attacks on onboarding mix these gaps. A fraudster may show a synthetic face to a hooked camera on an emulator, so a single control rarely stops the whole attempt. Our explainer on deepfake fraud in the Philippines walks through how these schemes unfold.
Why Philippine Firms Are Shopping for Deepfake Detection
Pressure comes from two directions, and both are growing. First, regulators keep warning the public. The SEC has issued advisories against deepfake videos of business leaders promoting fake investments, and in 2024 the BSP cautioned the public about AI-manipulated videos and audio used in scams. Because of those warnings, customers now expect their bank or app to spot such tricks.
Second, the law raised the stakes for institutions. Under RA 12010, the Anti-Financial Account Scamming Act, social engineering schemes are crimes, and institutions must protect account access with adequate controls. Institutions that fall short can also be liable to restore funds lost to scams. Our AFASA compliance checklist maps those duties to controls.
Where do deepfakes hit first? Usually at account opening and account recovery. Take an e-wallet that lets users reset a device with a selfie. If a fraudster can pass that selfie step with a face swap, every other control behind it no longer matters.
Six Checks Before You Buy Deepfake Detection Software
Vendor demos tend to look alike. The differences show up only when you ask specific questions, so use these six checks to compare tools on the same terms.
1. Attack Coverage Beyond Face Swaps
Ask which attack types the vendor tests against. At minimum, the list should include face swaps, synthetic faces, edited ID portraits, and replayed video. Also ask how often the vendor retrains its models, because new generation tools appear every few months.
2. Capture Channel and Injection Attacks
Here, the key question is where the image comes from. An API that scores uploaded images cannot see whether the camera was real. By contrast, an SDK running inside your app can check for emulators, virtual cameras, and hooking tools before the camera captures the image. For high-risk flows, you need both.
3. Results on Your Own Philippine Data
Generic accuracy claims tell you little. Instead, run the tool on your own traffic: entry-level Android phones, dim rooms, and selfies taken next to a PhilID or driver’s license. Then measure two things separately. How many fakes got through, and how many real customers did the tool block?
4. Outputs Your Team Can Act On
A bare pass or fail makes tuning impossible. So look for a score, a reason code where possible, and a way to set your own thresholds. That lets you build a review band, where borderline cases reach a human reviewer instead of an outright rejection.
5. Speed and Integration Effort
Because every second at onboarding costs sign-ups, speed matters. Therefore, check response times under load, SDK size, and support for both Android and web. Also ask whether deepfake, liveness, and face match run in one call or three, since each extra call adds delay.
6. Data Handling Under the Data Privacy Act
Face images are personal information, and a biometric template can count as sensitive data. Under the 2012 Data Privacy Act, you remain accountable for how a vendor processes them. So ask where the vendor processes and stores images and how long it keeps them. Also ask whether it signs a data sharing or outsourcing agreement.
How to Pilot Deepfake Detection in 30 Days
In most cases, a short pilot beats a long RFP. The plan below fits most banks, lenders, and e-wallets.
- Week 1, set the baseline. Pull a sample of past onboarding selfies, including cases your fraud team already confirmed as fake.
- Week 2, run in shadow mode. Score live traffic without blocking anyone, so you see real false rejection rates.
- Week 3, red-team it. Have your team try face swaps and replayed videos through the actual app, not just uploaded files.
- Week 4, set thresholds. Pick an auto-approve line, a review band, and a reject line, then document why.
Keep the pilot honest by tracking manual review load as well. A tool that catches more fakes but doubles your review queue may still cost more than it saves.
Where Verihubs Fits in a Deepfake Defense
For example, Verihubs covers several of the layers above in one stack. Its deepfake detection that flags AI face manipulation checks submitted face images for swaps and synthetic faces. Meanwhile, liveness detection for selfie onboarding confirms a live person, while face matching ties that person to the ID they submitted.
For the capture channel, the VeriSecure SDK combines liveness and deepfake analysis with checks for emulators, rooted phones, hooking frameworks, and intercepted traffic. That combination matters most for account recovery and device changes, where injected video is the usual attack. For how liveness works on local phones, see our liveness SDK guide.
Frequently Asked Questions
What is deepfake detection software?
It is software that checks whether AI generated or altered a face in an image or video. It returns a score or label that your system uses to approve, review, or reject.
Can liveness detection stop deepfakes on its own?
Not fully. Liveness detection stops photos, screens, and masks, but a deepfake injected into the camera feed needs device checks and deepfake analysis as well.
How do you test deepfake detection software?
Run it in shadow mode on your own onboarding traffic, then red-team it through the real app. Measure missed fakes and blocked real customers separately.
Does Philippine law require deepfake detection?
No law names deepfake detection specifically. However, RA 12010 requires institutions to protect account access with adequate controls, and deepfakes are now a common route to account takeover.
Does deepfake detection work on ID photos?
Many tools can score edited or synthetic ID portraits. Still, pair them with document checks and a record lookup, since a forged card can carry a real face.
Choosing Deepfake Detection Software for Philippine Onboarding
The best deepfake detection software is the one that holds up on your own users and your own attack paths. So test capture-channel defenses, not just image scoring. Measure false rejections as carefully as missed fakes, and settle data handling before any images leave your systems. Get those right and the tool becomes part of a layered defense, not a checkbox.
Comparing deepfake detection tools for your app? Run a deepfake detection pilot with Verihubs on your own onboarding traffic.
