As technology increases, so does our dependence on it. With iPhone X, you can unlock your phone using your face without using any other forms of passwords or fingerprints. The potential for using online facial recognition technology goes far beyond unlocking our phones.
A lot of verification services have already incorporated Facial Recognition Technology in their authentication processes which make the process more secure and reliable.
As with most technologies, the negative implications come hand-in-hand. With every new innovation in technology comes a new form of cyberattack. With face verification, a number of fraudulent ways to bypass it, have been invented successfully. The most popular way to deceive a face recognition software is through a “spoof attack”.
Whats a Spoof Attack?
A spoofing attack is an attempt to acquire someone else’s identity to get access or benefits of that identity. This can be done through the use of a photo, video or any other replacement for the authorized person’s face.
Some types of spoofing attacks are explained here.
- Print Attack
In print attack, the attacker uses someone else’s photo. The image is either used on a digital device or is printed.
- Replay/video attack
This is a more advanced way to outwit the system in which usually a looped video of a victim’s face is used. Through this method, the behavior of the attacker looks more natural which tricks the system into authenticating them. This is a much exceptional way of spoofing compared to holding someone’s photo.
- 3D mask attack
In this type of attack, the scammer uses a 3D mask as the tool for spoofing. A 3D mask attack is much more effective in tricking the system compared to the looped video attack. Through a 3D mask, the scammer is able to deceive the software’s extra layers of protection like depth sensors.
Facial Recognition to Combat Spoof Attacks
Facial Recognition alone is unable to combat spoof attacks. User photos are easily available on social media and used to trick the facial recognition software. Using paper photographs, screenshots or 3D facial reconstruction, it becomes quite easy for the fraudsters to hack someone’s identity.
This is why it becomes immensely important for organizations to have face anti-spoofing systems in place to protect sensitive data, mitigate identity thefts and combat frauds. Through anti-spoofing technologies, online facial recognition software is enhanced and their ability to detect fraud is increased.
The Need for Anti-Spoofing Solutions
The reliable solutions need to be much more accurate and precise, require lesser time and enhance user experience. And most importantly, these solutions need to be integrated with Facial Recognition Technology. These solutions include liveness detection, 3D depth analysis, emotion detection to authenticate the identity of the user.
- Liveness Detection
Liveness detection test includes the verification of the identity by checking the liveness of the person. One example of this is the “Eye Blink Detection.” It is an incredibly accurate process to verify that the person is authentic. Natural blinking is a sure-fire way of determining whether the face is live or not. The average human being blinks about 15 to 30 times per minute.
The eye remains closed for about 250 milliseconds during a blink. Through modern cameras, videos with far smaller intervals between frames can be recorded (50 milliseconds at 30 frames per second). Videos can be used to find frames with closed eyes and then can be counted to get the conventional numbers.
- Challenge-Response Technique
In the Challenge-Response Technique, the user is asked to perform one or two special actions called challenges. These challenges can include smiles, facial expressions of happiness or sadness or certain head movements. This method works to authenticate that the challenges occurred during a video sequence. Although this is an effective method, it can slightly influence the user experience.
- 3D Depth Analysis
3D depth analysis is one of the most dependable means of anti-spoofing. In this method, the precise pixel depth information can provide extraordinary accuracy against the spoofing attacks. Through the 3D depth analysis, the difference between a face and a picture can be easily observed.
- Active Flash
Through the use of active flash, spoofing can be detected. This is done by discovering light reflections on a face. The active flash technique involves using a changing light ambiance provided by the supplementary light that comes from a device’s screen. The white light of the screen produces a relevant reflection on the face.
What’s The Future of Anti-Spoofing?
Anti-spoofing techniques are the future of online facial recognition software. Technology is a constantly involving matter and with each passing day, new innovations are being explored. As the scammers and fraudsters continue evolving and incorporating newer technology, it is important to better the technologies to mitigate spoofing attacks, identity thefts, and frauds.