Facial recognition systems can be considered a controversial technology. On the one hand, this technology affects people’s privacy. On the other hand, it assists in preventing or detecting violence. And now, in light of the global pandemic, it helps to deter the spread of coronavirus.
Nonetheless, like any other technology, facial recognition isn’t impeccable, but has vulnerabilities that make it possible to bypass a system. As it has been recently discovered, it’s possible to create sunglasses designed to circumvent facial recognition systems. Deep learning algorithms applied to most of these systems are vulnerable to different types of attacks that impact the three objectives of security: confidentiality, integrity and availability.
Why is facial recognition security worth considering?
Facial recognition systems include two huge and diverse applications. One aims at detecting a person, and is currently used in various surveillance applications, such as CCTV cameras. Another ensures that a person is successfully recognized, referring to biometric applications, such as mobile phones or smart home locks.
Thus, there are two different types of attacks. The first one allows attackers to escape cameras by using various forms of camouflage. The second one is more complicated and enables face changes, recognition bypass related to biometric systems, and home invasion as a result.
What kinds of facial recognition solutions exist?
I’ve mentioned two main goals for hacking facial recognition systems. In addition to them, a facial recognition system consists of numerous machine learning algorithms. Each has its own features, properties and security vulnerabilities. Below is a list of machine learning algorithms commonly used in facial recognition solutions.
- Facial detection.
- Face recognition.
- Face tracking.
Facial recognition solutions may be physical and digital. They can be implemented on the internet and media platforms to track photos, and the same solutions can be brought into the real world and placed inside cameras. Take a look at industries that are able to apply facial recognition systems and algorithms they use.
What industries are prone to attacks?
Facial recognition technology was first applied for professional purposes. The idea of involving facial recognition networks in identifying offenders in the city streets was implemented by law enforcement.
ATMs can recognize cardholders through biological characteristics, and this helps to better secure transactions and prevent fraud.
3. Internet Search Engines
Image search engines are also based on this technology. The facial recognition principle is applied to social networks to help to find people with user-provided images.
4. Self-Payment Systems
Face identification technology can make the payment process much easier. Not so long ago, the FamilyMart supermarket chain implemented the system to let people pay without the assistance of cashiers. In March 2019, Shenzhen metropolitan also introduced a payment method based on facial recognition.
5. Airports And Custom Offices
Airports and customs offices plan to apply face recognition techniques. The solution could help to solve the issue of never-ending lines of people and long-lasting control measures.
6. Smart Homes
Smart home solutions may use facial recognition as a biometric system to check if a person can enter the facility.
The above industries are not the only areas to use facial recognition in order to protect sensitive data. To prevent any attacks on these systems, only appropriate and timely facial recognition security can be an effective solution.
How can attacks be carried out?
Currently, there are close to 100 research papers describing the ways to bypass AI algorithms of in facial recognition systems. Hackers can use all of them to develop practical hacking tools and launch espionage, sabotage or fraud attacks.
In fact, attack approaches depend on a target application. For example, if a target application is digital (such as online photos uploaded on social networks), hackers may use various techniques to make imperceptible changes to pixels responsible for some semantic area (such as hair color or make-up changes). If an attacker’s goal is to break a physical facial recognition system, they may try to put patches on some faces. These patches may be of different forms that seem realistic (sunglasses, bandanas and band-aids). Hackers sometimes invent hats projecting pictures or patches on one face to make it look like another face. The same approach was used in Hong Kong during protests.
What should be done to protect facial recognition?
Facial recognition security is a complex process involving the analysis of different security areas.
AI infrastructure: Attacks on hardware, platform, applications and frameworks.
AI model security: Attacks on AI algorithms, such as adversarial, poisoning, backdoor, etc.
AI privacy assessment: Attacks on infrastructure and algorithms focusing on privacy, such as model extraction, membership inference and others.
It’s clear now that IoT devices should have no software or hardware vulnerabilities. While people take care of hardware, software or wireless security only, there’s not enough attention paid to algorithm security. Therefore, machine learning and deep learning algorithms remain unaddressed.
Securing deep learning models in facial recognition systems is complicated, but at least some steps should be followed. It’s necessary to determine the main threats and issues for a particular AI solution, conduct white-box or black-box AI security assessments and implement protection measures before criminals will take advantage of your security issues.
Original post: https://www.forbes.com/sites/forbestechcouncil/2020/06/23/facial-recognition-systems-security/
46 comentários em “Facial Recognition Systems Security”
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