FaceID and FacePay: How to Protect Yourself from Attacks?
The evolution of facial recognition technology has brought about numerous benefits in terms of security and convenience, but it has also introduced new vulnerabilities that must be addressed. In the realm of authentication, where systems like FaceID and FacePay are becoming increasingly common, the need for protection against various fraudulent attacks is paramount.
A system that enables facial recognition for authentication purposes (such as app logins, door access, payments, transaction verifications, or document signings for government or banking services, etc.) requires protection against fraudsters' attacks.
Among the various types of attack, the most prevalent are:
These attacks can be countered using Liveness technology, as well as at an organizational level (by implementing two-factor authentication, involving employees for additional verification in high-risk scenarios, etc.).
3DiVi Algorithms for Counteracting Fraudulent Attacks
Depending on the level of threat, a suitable method of attack detection or even a combination of methods is chosen:
Image-based detection from the camera feed
This method identifies various anomalies in the image, such as borders of printed photos, edges of smartphones or tablets, rough edges of paper masks or cut contours, pixelation (on devices), blurriness (on generated images), lighting inconsistencies (on silicone masks), etc.
This approach is the simplest and most versatile, as it does not require the use of special equipment on the user's end.
Using depth map from depth sensor
This method employs an additional infrared depth sensor to determine volume. It serves as an additional control factor in specialized biometric access control terminals or at checkout counters in stores.
User activity analysis
This method operates solely on video. Users are prompted with changing scenarios (smile, wink, turn, or tilt head) to discern whether it's not a presented photo or mask and not a pre-recorded video.
Face color change detection based on terminal screen illumination
This method also operates solely with video. The device screen flashes different colors in a specific sequence. The conformity of the skin color change to the specified sequence is evaluated to discern whether it's not a presented photo or mask and not a video injection with deepfake substitution.
User session environment analysis
Information is gathered about all previous user actions (IP, browser, device, country of connection, etc.), and deviations trigger an increased threat level. When a threat is detected, a live operator is engaged in the operation. Applied to banking and governmental systems where the detection of fraudulent activities is crucial.
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