Develop a functional solution that incorporates the security of the ML model.

Source summary

Protect browser-delivered facial-authentication ML models against tampering and reverse engineering through obfuscation, cryptography or another proposed distribution approach. The source describes roughly 5–7 MB flatbuffer models, downloaded initially and cached until updates, and permits ONNX web runtime or Tensor.js. Avoid substantial size increases on mobile networks. Supply backend preparation either at release time or just before download, with capacity for population-scale transactions.