Yoti warns of generative AI identity theft

Digital identity company Yoti has described efforts to combat the potential threats created by generative AI (gen AI) as “an arms race.”

The firm details the threats, such as deepfakes, in first generative AI white paper.

“No-one really understands how generative AI will develop,” it said. “Once evading detection is added to a generative AI model as an objective during training, the situation will very quickly get a lot more difficult, particularly with images. Methods to detect generative AI videos still have some longevity as they have the advantage of being able to detect inconsistencies in the temporal domain. It’s currently very difficult to achieve temporal consistency when creating generative AI videos.”

In the paper, Yoti points out that deepfakes are being used for high-value transaction authentication, or account takeovers.

“Firstly, the identity verification process to set up an account has been fairly well understood, transitioning from in-person, in-branch document checks and document signing to well established online processes involving AI technology and ‘remote’ human checking.

“The emerging risk is around account takeovers and high-value transactions, where the potential reward for bad actors can be very high. Therefore the resources applied can be significant. Here, despite layers of security, deepfakes or injection attacks can override current systems, particularly where an account holder’s personal information has been compromised. There is also the growing threat of SIM swaps, where a bad actor can take over a user’s mobile phone account, thereby comprising an additional layer of authentication.”

Detecting gen AI threats

Yoti said its strategy for detecting gen AI threats targets two attack vectors: presentation attacks (direct) and injection attacks (indirect), with a focus on early detection during the verification or authentication process, citing the integration of Yoti’s MyFace and SICAP, along with ongoing advancements.

• Liveness technology – Yoti’s passive liveness detection technology, MyFace, is compliant with iBeta ISO PAD Level 2. It performs passive liveness and detects presentation attacks (be those images presented on a screen, a printed image or 3D masks).
• SICAP – Yoti has developed SICAP, short for Secure Image Capture, a proprietary technology that works alongside Yoti’s liveness detection capabilities. SICAP can identify and prevent sophisticated injection attacks, ensuring that the images captured during a verification process are genuine and remain untampered with.