Identify Individuals: What's the Method to Separate Authentic Humans from Artificial Intelligence Creations?
In an effort to keep the public informed about the latest advancements in AI and identity verification, Sumsub has announced the launch of a new bi-weekly Q&A series. The series will be led by Pavel Goldman Kalaydin, the Head of AI/ML at Sumsub.
This week, the focus of the Q&A series will be on the topic of how AI can bypass facial recognition. Pavel Goldman Kalaydin will delve into this subject matter, providing valuable insights and answering questions from the audience.
The Q&A series will feature experts from various fields, including legal and tech, and is open to questions from the public. Audience members are encouraged to submit their questions in advance to ensure they are addressed during the session.
Sumsub's Advanced AI-driven Facial Recognition Techniques
Sumsub employs several advanced AI-driven patterns in facial recognition to ensure the highest level of security. These techniques include liveness detection, face match on vector face masks, artifact analysis to detect deepfakes, and biometric template creation.
Through liveness detection, Sumsub analyses real-time signs of life, such as blinking, subtle facial movements, skin texture, and reflections of light on the skin. This ensures that the captured face is from a live human rather than a photo, video, or deepfake.
The face match on vector face masks method allows Sumsub to perform facematch using a vector face mask representation rather than storing the actual photo. This helps verify the user's identity while protecting privacy and contributes to proving humanity and uniqueness.
Artifact analysis is used to detect inconsistencies or artifacts typical of deepfake videos or images that try to mimic real facial features and movements, thereby stopping sophisticated AI-generated fraud.
Biometric template creation involves analysing unique facial features such as the distance between eyes and cheekbone shape, which are then compared against their database for verification.
Together, these AI patterns form a multi-layered approach that combines liveness checks, biometric verification, and anti-spoofing techniques to ensure that the person on the camera is a live human and not an AI-generated fake or replay of previously recorded material.
Integrating AI Capabilities within Sumsub's KYC/AML Platform
Sumsub integrates these capabilities within its KYC/AML platform, enabling identity verification and fraud prevention across Web3 and traditional finance use cases. Their approach also supports issuing attestations proving humanity and uniqueness without retaining biometric images.
Join the Q&A Series
The Q&A series can be found on The Sumsuber and Sumsub's social media platforms. Tune in every other Thursday to learn more about the latest advancements in AI and identity verification. Don't forget to submit your questions in advance to ensure they are addressed during the session.
For further learning, articles on deepfakes and different methods of bypassing facial recognition are available. Stay informed and stay secure with Sumsub's Q&A series.
- The Q&A series, led by Pavel Goldman Kalaydin, will delve into the topic of how artificial-intelligence can bypass facial recognition by discussing Sumsub's advanced AI-driven patterns in facial recognition, such as liveness detection, face match on vector face masks, artifact analysis, and biometric template creation.
- In its KYC/AML platform, Sumsub integrates these AI capabilities, employing a multi-layered approach combining liveness checks, biometric verification, and anti-spoofing techniques to ensure the highest level of security for identity verification and fraud prevention, while also supporting the issuance of attestations proving humanity and uniqueness without retaining biometric images.
