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Explanation of what they are, how they work, how they’re used in IDPV today
Do we need to explain the current state of the art, or will that be handled in the other topic areas about defences and attacks?
How are these different to how a human verifies identity?
Computer vision
Optical character recognition
Extracting text from documents (to read information from them)
Object detection
Recognising features on documents (to determine authenticity)
Biometrics (face, fingerprint, palm, voice??)
Matching unique features of a subject against enrolled individuals
Some open source face verification tools which include explanations of how they work:
A leaderboard of face verification models
Just shows accuracy - would be great to augment with robustness
Models available via the DeepFace (PyPi | GitHub) library (with % score against Labeled Faces in the Wild dataset):
VGG-Face (97.78%)
Google FaceNet (99.63%)
OpenFace (93.8%)
Facebook DeepFace (97.35%)
DeepID (99.15%)
Dlib (99.38%)
ArcFace (99.40%)
Models available via FaceTorch:
MagFace+UNPG
AdaFace
Also includes a deepfake detector that might be worth looking into?
Liveness detection
Pattern / anomaly detection
Behavioural analysis
Risk scoring
Natural language processing
Language translation
Disambiguating and “fuzzy matching” (against data sources)
Sentiment analysis??
GANs & Diffusion Models
Creation of realistic image/video/audio
Deepfakes
Where might this go
Speculation about how things might evolve and whether that could lead to new impacts on the IDPV sector
Consider teeing this up here, but the detail should be in the "Attacks" section
Liaise with Heather on future scenario development
Leave the impression that the only certainty is change going forward
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