The following diagram summarizes key attack points for deepfakes in remote identity verification (IDV). It follows from earlier work on codifying possible presentation attack points in biometric systems as included in ISO/IEC 30107-1:2016 and other industry research, such as that done by Stephanie Schuckers at CITeR.

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Workflow Summary

This diagram provides an overview of a standard remote IDV process, including automated capture of biometrics (e.g., selfie photo) and identity documents. Capture of biometric and identity document data is followed by a biometric-to-document comparison and / or comparison with stored data in the host system. These comparisons are followed by an IDV decision. 

If the automated IDV attempt is rejected or if a user opts-out of the automated check, the system will revert to a manual check. In the case of a manual review, the adjudicator may still use software tools to help ensure the authenticity of presented identity documents. In either case, if used, the manual IDV workflow will also result in acceptance or rejection. 

Deepfake Attack Points

Deepfakes attacks are related in many ways to biometric presentation attacks, but are cause for concern in more than one capture channel in remote IDV. This diagram highlights three opportunities for deepfake attacks at front-end image capture:

1. Automated Workflow - Live Biometric Capture: Deepfakes can either be used as a part of a physical presentation attack (point 1 in the diagram) or injected into the capture software interface (point 2 in the diagram). 

2. Automated Workflow - Identity Document Capture: Likewise, deepfakes can either be used as a face inserted within a physical document, or as a part of a fully synthetic (but physically manifested) document (point 3 in the diagram), or injected as a digital image or video into the capture software interface (point 4 in the diagram). It should be noted that systems which use a “photo bucket” or “photo picker” for identity document upload are particularly susceptible to attacks at point 4. 

3. Manual Workflow - Live Video Chat: As many IDV systems use a live video chat for manual reconciliation or support, this video channel becomes an attack point for injected video simulating a webcam (point 5 in the diagram). 

In addition to these three front-end capture attack points, the host (backend) system may be susceptible to deepfake attacks as well, including: 

1. Stored Data: Deepfaked face images, deepfaked faces within identity documents, or fully synthetic identity documents may be stored as references in a backend system (point 6 in the diagram) due to lack of proper detection mechanisms at the time of original capture or ingestion. 

2. Insider Threats: The potential for human subversion of a host system (point 7 in the diagram) is an ever-present risk. Even if originally captured data was authentic, nefarious actors could replace face and / or identity document records with deepfakes if the proper protection and detection protocols are not in place.