Differentiating a Real Person from an AI-Generated Model: Inquiries from Sumsubers
Every other Thursday, Sumsub is hosting a new bi-weekly Q&A series on its social media platforms and The Sumsuber. This Q&A series is designed to address the most frequently asked questions about Sumsub's services, with a focus on compliance and automated solutions.
This week's Q&A session is being led by Pavel Goldman Kalaydin, the Head of AI/ML at Sumsub. Pavel will be discussing an important and timely topic: how AI can bypass facial recognition.
Sumsub's expertise in this area is well-established. The company detects deepfakes and bypassing of facial recognition by employing advanced AI-driven liveness checks and deepfake detection technologies. Their system combines real-time biometric verification with AI-based screening to ensure the authenticity of a user's identity, preventing fraud attempts that use synthetic or altered faces.
Addressing the Challenge of Deepfakes
In order to combat the increasing sophistication of deepfakes, Sumsub employs several AI-based approaches.
Liveness Detection
Sumsub uses AI to verify that a live person is present during identity verification rather than a static or pre-recorded image. This involves detecting natural facial movements and subtle responses that deepfakes and AI-generated videos often fail to replicate convincingly.
Deepfake Detection Models
Sumsub's technology employs machine learning models trained to detect artifacts and inconsistencies characteristic of deepfakes, such as unnatural textures, irregular lighting, or subtle distortions that arise from generative AI face synthesis or manipulation.
Robustness Against Evolving Deepfake Techniques
Because deepfake methods rapidly improve, Sumsub leverages recent research findings and continuously updates its detection algorithms to recognize new deepfake patterns, such as those generated by the latest generative models.
Multi-factor Verification
Sumsub integrates facial recognition matching with contextual data (like document verification and behavioral biometrics) to improve fraud detection accuracy beyond relying on facial recognition alone.
Zero-Shot and Few-Shot Learning Approaches
Recent research referenced by Sumsub suggests using visual language models and advanced AI systems capable of zero-shot detection—identifying deepfakes without prior training on those specific kinds of fakes—enhancing adaptability in real-world conditions.
These AI-based approaches are necessary because traditional methods such as manual visual inspection or barcode scans cannot reliably detect AI-generated images or deepfakes, which have become increasingly sophisticated and widely used for identity fraud.
Learning More About Sumsub's Services
The Q&A series is intended for anyone interested in learning more about Sumsub's services. Articles on deepfakes and methods of bypassing facial recognition can be found for further learning.
Stay tuned for the Q&A session with Pavel Goldman Kalaydin, where he will delve deeper into these topics and answer your questions. Don't miss out on this opportunity to gain insights into Sumsub's innovative approach to combating deepfakes and ensuring secure identity verification processes.
Artificial-intelligence plays a significant role in Sumsub's services, particularly in combating deepfakes and bypassing facial recognition. Sumsub employs advanced AI-driven liveness checks and deepfake detection technologies to ensure the authenticity of a user's identity.
During this week's Q&A session, Pavel Goldman Kalaydin, Head of AI/ML at Sumsub, will discuss how AI can bypass facial recognition and the strategies Sumsub uses to combat the increasing sophistication of deepfakes, such as liveness detection, deepfake detection models, and robustness against evolving deepfake techniques.
