Protect Your Business From Deepfake Fraud Across the Entire Identity Verification Flow

Deepfake detection is the process of identifying AI-generated or manipulated media, such as synthetic faces, face swaps, replayed videos, or injected video streams. In identity verification, deepfake detection helps confirm that a remote user is a real person and not an AI-generated impersonation.

Regula’s deepfake detection approach combines liveness checks, face matching, document authenticity verification, and injection risk indicators. This layered flow helps detect deepfakes in real time and reduce the risk of AI fraud during remote onboarding, account recovery, and high-risk transactions.

Deepfake detection technology can help identify several types of attacks used in identity fraud. These include AI-generated faces, face swaps, puppet deepfakes, pre-recorded videos, screen replays, 3D masks, and synthetic identity attempts.

Regula focuses on detecting deepfakes across three key layers: presentation attacks on the physical camera, injection attacks on the data stream, and identity-level attacks that combine manipulated faces, forged documents, or other synthetic content. This helps organizations address both biometric spoofing and deepfake fraud.

Liveness detection helps verify that the person in front of the camera is physically present during the verification session. It helps detect presentation attacks such as printed photos, screens, masks, or replayed videos. For deepfake fraud prevention, liveness checks are important because many attacks try to replace a real live user with synthetic content. Active liveness may use user interaction or challenge-response prompts, while passive liveness works without requiring extra actions from the user. Regula uses liveness detection as one signal in a broader deepfake detection solution.

Presentation attack detection, or PAD, focuses on attacks shown to a real camera. Examples include printed photos, screen replays, masks, or other physical substitutes used to bypass identity checks. Injection attack detection, or IAD, focuses on attacks against the data stream. In these cases, fraudulent media may be fed into the verification flow through a virtual camera, emulator, or injected stream. Effective deepfake detection software should address both PAD and IAD because deepfake attacks can target either the camera or the digital capture path.

Yes. Deepfake detection can work on-premises when the solution supports deployment inside the customer’s own infrastructure. This is important for organizations that need strict control over biometric data, identity documents, and verification results. Regula supports on-premises deployment, helping businesses keep sensitive identity data within their own security perimeter. For regulated industries, on-premises deepfake detection software can support data sovereignty, internal compliance requirements, and AI fraud prevention without relying only on cloud processing.

To detect deepfakes in identity verification, organizations need more than a visual check. Deepfake detection tools analyze liveness results, facial consistency, motion patterns, biometric matching, document checks, and capture integrity indicators. Regula combines these signals into a layered decision flow instead of relying on a single visual or biometric check.

Common deepfake detection techniques include liveness detection, face matching, presentation attack detection, injection attack detection, motion and consistency checks, and document authenticity verification. Some methods focus on the face, while others focus on the session, the device path, or the identity document. Regula uses multiple deepfake detection techniques together. Liveness checks help confirm real human presence. Face matching compares the captured face with the ID document portrait. Document verification checks whether the identity document is authentic. Injection risk checks help identify signs of virtual cameras, emulators, or manipulated streams. Together, these methods support detecting deepfakes in real identity verification scenarios.