As per the FBI’s Internet Crime Report 2021 (IC3-2021), spoofing and impersonification-based cybercrimes led to a loss of $82.2 million! Thus, businesses need to implement anti-spoofing solutions for better protection against heinous cybercrimes.
Spoofing is the act of replicating a user to access a system unethically. These days, hackers have adopted techniques to fake a person’s liveness to bypass platforms secured with biometric identification.
This blog will discuss the best anti-spoofing techniques using Convolutional Neural Network (CNN), eye blink detection, and other methods.
What is Anti-Spoofing?
Anti spoofing is the filtration of IP addresses on a network’s entry point. This ingress filtration should be implemented across all networks to prevent spoofing. The technique blocks spoofed or illegitimate packets by verifying the IP address’s authenticity. A firewall rule determines each incoming packet and checks its source address. Also, using email authentication protocols confirms the identity of the sender and the legitimacy of emailed messages.
Firewall rules evaluate the control information in every packet by blocking or allowing them as per the rules set. Firewall rules are directed to computers or policies that are assigned to a computer or collection of computers.
Benefits of Anti-Spoofing Solutions
Usual security methods like passwords are breachable. Biometrics strengthens security, but accurate matching is harmful if biometrics is spoofed by hackers. They can use tools available in the black market available for as less as $100 to attempt spoofing. You’d be surprised to know they can subscribe to tutorials on constructing attacks for just $5!
Thus, investing in anti spoofing technology is extremely important if you deploy biometrics for verification purposes. It ensures only an authorized live person is trying to access a system and not a bad actor using 2D or 3D representations.
This security practice prevents misuse of your personal data like photos, videos, financial details, social security numbers, medical details, official records, email accounts, etc.
Anti-Spoofing Solutions for Biometric Spoofing Attacks
Biometric spoofing is a cyberattack where hackers unethically break into a device by impersonating biometrics like facial recognition, fingerprint scanning, voice recognition, etc. Out of these, facial recognition is used the most for spoofing attacks. There are two common types of facial spoofing attacks; 2D presentation attacks and 3D presentation attacks. These are further categorized as static and dynamic.
In static 2D presentation attacks, photographs, flat papers, or masks are used, whereas, in dynamic 2D presentation attacks, multiple photos in a sequence or videos are used.
Pictures and sculptures are used in static 3D presentation attacks, whereas in dynamic 3D presentation attacks, advanced robots help malicious actors.
What is Liveness Detection?
Before moving on to anti spoofing solutions for biometric-based spoofing attacks, you need to know what liveness detection is.
Liveness detection is a technique on which all the biometric-based anti-spoofing solutions are based. It uses computer vision technology to detect if facial biometrics are alive or replicated. It can be active or passive.
In this, liveness is detected by establishing communication between face recognition systems. You’ve to stand in front of a camera and perform actions like smiling or nodding. It’s effective and hard to bypass as actions are random; you don’t know what comes up (nor do hackers).
In passive liveness, you’re unaware that a system is testing whether your facial biometrics are genuine or replicated. It’s more reliable than active liveness.
Anti-Spoofing Techniques for Biometric Spoofing Attacks
Anti spoofing solutions should be reliable and have the best accuracy. Here are some methods that are commonly used.
Eye Blink Detection
Natural blinking is used as an effective anti-spoofing technique to check the liveness of a face. On average, a human being blinks 15-30 times in a minute, and their eyes remain shut for about 250 milliseconds during a blink.
These days, cameras record videos with very short intervals between frames, like 50 milliseconds at 30 frames per second. This new-age camera ability help find frames with closed eyes and count the number of times you blinked. This technology is used for face landmark analysis and finding the surface area of eyes as an anti spoofing solution.
Convolutional Neural Network
Let’s see what is the anti spoofing solution using Convolutional Neural Network or CNN. It’s a deep learning technique that traces the distinction between real and spoofed graphics used by cybercriminals. CNN is based on the concept of Artificial Intelligence or AI and calculates pixel data for anti-spoofing acts.
However, this method’s accuracy percentage is low; there isn’t a fixed set of features that CNN evaluates. The model works on hoping it’d detect things that human eyes can’t. So, it’s only viable in narrow use cases.
Another workable anti spoofing technique includes challenges and responses where certain actions detect spoofed graphics and videos. These include:
- Facial expressions like that of sadness or happiness
The user experience can get damp as it demands additional inputs. Thus, it might not be a viable anti spoofing solution for some businesses.
Businesses can also use a photo editor to add a visible company logo or signature to their emails, making it easier for recipients to identify legitimate emails and deter spoofing attempts
3D cameras are concluded as one of the most practical and well-founded anti-spoofing solutions as the precise pixel depth information gives accurate results. It helps determine the difference between a face and a flat shape (like photos), thus averting access using fake representations.
Active flash spots spoof activities using light reflections on a face. It’s based on the concept that changing the lighting environment produces reflection on the human face.
It segregates real faces from replicated ones by comparing the before and after flash versions of faces by calculating the pixel depth.
Anti-Spoofing Solutions for General Spoofing Attacks
Other types of spoofing attacks are- email spoofing, caller ID spoofing, IP spoofing, Man-in-the-Middle or MitM attacks, etc. Let’s check out some ways to prevent them.
Refrain from Using Public Networks
Public networks aren’t safe as threat actors can position themselves between you and the network source. They can access and intercept work-related data stored in your device or even inject malware to steal financial details, social security numbers, etc. Thus, it’s suggested to use VPN.
Apply Multi-factor Authentication
Multi-factor authentication or MFA adds additional layers of security. So, even if hackers steal your password, they won’t be able to bypass security. MFA methods include OTP, biometric detection, ‘allow’ notification on the phone, etc.
Using Email Authentication protocols
Implementing email authentication protocols like SPF, DKIM, and DMARC can help prevent spoofing attacks done using your email domain. If you already use SPF, it’s recommended to use an SPF checker regularly to know if an unauthorized entity is misusing your domain to send fraudulent emails.
Hover Over a URL Before Clicking it
Another anti spoofing technique is to avoid clicking an unrecognized or dubious link directly. It’s better to hover your cursor over it without clicking it. You can see the URL on the bottom left of the screen; visit it only if you feel it’s taking you to a safe website.
Anti spoofing refers to the practice of barring malicious IP addresses on a network’s entry point. The technique blocks spoofed or illegitimate packets by verifying the IP address’s authenticity. Some standard and viable anti-spoofing techniques are based on the concept of eye blink detection, CNN, 3D cameras, flashlights, etc.
You must refrain from using public networks and oversharing information online. Also, invest in the DMARC tool that prevents spoofing attacks done using your email domain. You can reach out to PowerDMARC for everything related to DMARC.
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