Background material

Articles & explainers

• https://ig.ft.com/generative-ai/

• https://towardsdatascience.com/transformers-in-depth-part-1-introduction-to-transformer-models-in-5-minutes-ad25da6d3cca

Videos

• https://www.youtube.com/watch?v=zjkBMFhNj_g

  • Very thorough and clear explainer of how LLMs work

• https://www.youtube.com/watch?v=2eWuYf-aZE4

  • Goes through now neural networks work, and how they’re used in LLMs, image generators, etc

• https://www.youtube.com/watch?v=4RixMPF4xis

Courses & tutorials

• https://developers.google.com/machine-learning

• https://www.cloudskillsboost.google/course_templates/536

• https://www.cloudskillsboost.google/course_templates/539

• https://www.cloudskillsboost.google/course_templates/556

• https://huggingface.co/learn/nlp-course/chapter1/1

What should we talk about

Background

• We don’t need yet another “introduction to AI” - people can read that elsewhere

• Perhaps a simple reminder that “AI” used to be called “machine learning” and before that it was called “data science” and before that it was called “statistics”

• At its core, it is about pattern recognition and extrapolation / prediction

• Why is this a hot topic now? Availability of compute, data and research has caused a massive acceleration (but not a whole timeline)

• Probably need to introduce 2 major innovations that have a direct bearing on the subject:

  • Transformer models - neural networks that learn context, trained on very large data sets (“foundation models”) - leading to many new applications in NLP (e.g. ChatGPT)

    • Consider adding background on what a neural network is too

  • Generative Adversarial Networks - pairing of two neural networks (a generator and a discriminator) - creates very realistic new content (including deepfakes)

• The goal is to enable consumers of this information to have sufficient context to understand how the different modalities work and in turn how they apply to IDV

• Consider the output as diagrams / infographics rather than defaulting to a whitepaper format for this material

Relevant modalities

• 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%)

• OpenCV Face Recognition

• face.evoLVE

• Models available via FaceTorch:

  • MagFace+UNPG

  • AdaFace

  • Also includes a deepfake detector that might be worth looking into?

Liveness detection

Some background information from FaceTec

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

Who should we get to contribute?