What is image spoofing and how do I prevent it?
How easy it is these days, just look into your device and access it!
No need to type long passwords, right? But do you think it’s a 100% foolproof method, especially when you use it for operational purposes in your business?
No, biometric facial recognition systems are vulnerable to impersonation attacks because bad actors can use your photos and videos to bypass security settings. There have been many cases that prove why facial recognition alone is not a reliable method.
In 2020, due to numerous fraudulent claims for unemployment benefits, Washington State had to briefly suspend the program. Fraudsters stole social security numbers using image spoofing techniques, which resulted in the loss of $1.6 million. Scary, right?
But if you think it’s a modern technique, let us take you back to its dawn. Facial recognition was actually developed by Woody Bledsoe, Helen Chan Wolf, and Charles Bisson in the 1960s;
However, it could not be released due to internal complications. Between 1960 and 2000, it experienced progress with the contribution of multiple experts. It was in the 2000s that the US government began to use facial recognition for various purposes, and new developments also allowed it to enter private companies.
Currently, efforts are directed towards making facial recognition 100% foolproof, but the number of image spoofing the attacks are constantly increasing. What is Image Spoofing and how does it work, by the way?
If your business model relies on facial recognition, we encourage you to read this blog until the end.
What is Impersonation?
Image theft, also known as facial spoofing, is a form of cyberattack in which criminals attempt to gain access to a device or system through facial biometrics. They usually use a photo or video to replace the identity of the original owner and carry out a face spoofing attack.
Face spoofing techniques can be used for crimes related to banking, social media, email, etc., which can be detrimental to businesses using facial recognition to run their operations. Hackers can access devices and systems that have facial recognition enabled to steal information or send messages, emails, and similar requests on behalf of the business.
Malicious actors usually target small and medium businesses to spoof facial recognition crimes because they have weaker or non-existent security systems installed on their devices. Did you know that 30% of small and medium businesses operating in the United States do not have a robust system against image theft?
Types of Face Spoofing
Now that you know more about image spoofing, let’s move on to its types. There are two common types; 2D presentations and 3D presentations (static or dynamic).
2D presentation attacks
static 2D image spoofing is done using photographs, flat paper or masks. While multiple frames in a sequence or screen video are used for dynamic attacks.
3D presentation attacks
In its static form, cybercriminals use 3D images and sculptures. On the other hand, advanced bots are used for dynamic 3D presentation attacks.
How to prevent image spoofing?
There are several ways to prevent image spoofingand all fall under the liveness detection technique.
What is liveness detection?
It basically detects whether facial biometrics are alive or replicated. The process is undertaken using computer vision technology which prevents image spoofing by prohibiting representations such as photographs, videos or masks.
The liveness detection technique can be active or passive. Let’s dig a little deeper to understand each of them.
- active liveliness: It works by checking liveliness by establishing communication between facial recognition systems. In this interactive and effective approach to catching a face parodyusers must stand in front of a camera and perform certain actions (like smiling or nodding) guided by the system.
In most cases, the actions are random, which means that the usurpers cannot detect what is happening. This makes it difficult for them to circumvent this security feature.
- Passive liveliness: Active liveness detection protocols may not be suitable for some systems because they are based on user interaction. In such cases, passive liveness detection comes to the rescue.
Here, users are completely unaware that they are being tested; it is therefore a robust and foolproof way to prevent image spoofing.
Common anti-spoofing techniques
To learn more about how to prevent image spoofingLet’s discuss liveness detection methods based on texture, color, motion, shape, or reflectance.
Eye blink detection
The natural eye blink test is considered very accurate. A human being blinks 25 to 30 times per minute and the eyes remain closed for almost 250 milliseconds with each blink.
New state-of-the-art cameras capture video with minimal gaps between consecutive frames. Thus, the number of flashes is counted, which makes it possible to identify spoof facial recognition.
This preventive technique image spoofing uses a trained convolutional neural network (CNN) to locate the difference between the real and fake graphs. CNN is an artificial intelligence based technique for determining pixel data.
This method consists of validating the biometrics of the user, based on challenges such as the smile. However, it requires additional inputs, which hinders the overall user experience. So if your business model involves facial recognition and you want to deploy this technology, it might not be successful.
It is concluded as one of the most reliable methods to avoid face impersonation. This works by determining pixel depth information. The pixel depth of a face differs from a flat shape, which helps prevent the entry of misrepresentations.
This supports the anti-image spoofing exercise using the reflection of light on a face. Low-light environments are used here with supplemental light coming only from the device screen. This catches fake faces because the white light emits a reflection.
The system is trained to analyze the before and after flash versions of the face and then calculate the pixel depth.
Number of impersonation attacks increased significantly after the first peak of Covid-19. In 2020, $4.1 billion was lost to bad actorswhich makes it imperative to recognize email spoofing, DNS spoofing, IP address spoofingand other cyberattacks.
Currently, deep learning is considered the most reliable method for capturing 2D and 3D presentations, and the next spoof facial recognition applications and software should only use this. However, it is necessary to create sensible metrics if we want to get 100% accurate results from these software and applications.
What is image spoofing and how do I prevent it? appeared first on EasyDMARC.
*** This is an EasyDMARC Security Bloggers Network syndicated blog written by EasyDmarc. Read the original post at: https://easydmarc.com/blog/what-is-image-spoofing-and-how-to-prevent-it/