Seeing is Not Believing: Bypassing Facial Liveness Detection by Fooling the Sensor

Summary

In this engaging presentation, Ken Milo discusses the critical role of facial liveness detection in identity verification, particularly for financial institutions. He explores various identity verification methods, the vulnerabilities of facial recognition systems to presentation attacks, and the sophisticated techniques attackers employ, such as video injection. Milo outlines the importance of implementing robust countermeasures, including passive and active liveness detection, to ensure user authenticity. Ultimately, he emphasizes the need for continuous improvement in detection technologies to combat identity fraud effectively.

Takeaways

• 😀 Facial liveness detection is crucial in identity verification to ensure users are who they claim to be.
• 🔒 Identity verification is essential for financial institutions as part of KYC (Know Your Customer) and AML (Anti-Money Laundering) efforts.
• 🖼️ Common identity verification methods include knowledge-based questions, multifactor authentication, and biometric data like facial recognition.
• 🎭 Presentation attacks, such as 2D spoofing and video replay attacks, exploit weaknesses in facial recognition systems.
• 👁️ Facial recognition captures and compares facial features to verify identity, but attackers can spoof this process.
• ⚖️ Facial liveness detection distinguishes between real users and impersonators by ensuring biometric samples come from live individuals.
• 🔍 There are two types of liveness detection: passive (no user interaction required) and active (users must perform specific actions).
• 💻 Attackers are adapting techniques, such as video injection attacks, to bypass liveness detection systems.
• 📱 Virtual cameras can be used to simulate live video feeds, tricking identity verification systems into accepting spoofed identities.
• 🔐 Future solutions need to focus on enhancing detection methods, implementing layered security approaches, and possibly including manual verification in high-stakes environments.

Q & A

What is the main focus of Ken Milo's talk?

The talk focuses on facial liveness detection as a critical aspect of identity verification.

What are the common methods of identity verification mentioned?

Common methods include knowledge-based authentication, multi-factor authentication (SMS/OTP), and the submission of government IDs and biometric data.

How does facial recognition work?

Facial recognition captures a user's facial features and compares them against a database to verify their identity.

What are presentation attacks?

Presentation attacks involve spoofing a facial recognition system using methods like printed photos, video replay, or silicone masks to impersonate legitimate users.

What is the difference between passive and active facial liveness detection?

Passive liveness detection does not require user interaction, making it easier for users, while active detection requires users to perform specific tasks like blinking or moving their heads.

What new attack method is introduced in the talk?

The new method introduced is the video injection attack, where high-quality video feeds are directly injected into the system, bypassing traditional display methods.

What is the purpose of remote image attestation?

Remote image attestation is used to ensure the authenticity of video captures by signing them with private keys, allowing verification of their integrity.

What are some challenges identity verification providers face?

Challenges include sophisticated presentation attacks, ensuring the effectiveness of liveness detection, and keeping up with attackers' evolving techniques.

Why is manual verification important in high-stakes environments?

Manual verification adds an additional layer of security, particularly in sensitive areas like banking, where the risks of identity fraud are significant.

How can organizations detect the use of virtual cameras?

Organizations can detect virtual cameras by checking their software signatures, resolution capabilities, and other hardware characteristics that differ from physical cameras.