Phil 4.20.2022

Planting Undetectable Backdoors in Machine Learning Models

  • Given the computational cost and technical expertise required to train machine learning models, users may delegate the task of learning to a service provider. We show how a malicious learner can plant an undetectable backdoor into a classifier. On the surface, such a backdoored classifier behaves normally, but in reality, the learner maintains a mechanism for changing the classification of any input, with only a slight perturbation. Importantly, without the appropriate “backdoor key”, the mechanism is hidden and cannot be detected by any computationally-bounded observer. We demonstrate two frameworks for planting undetectable backdoors, with incomparable guarantees.
  • First, we show how to plant a backdoor in any model, using digital signature schemes. The construction guarantees that given black-box access to the original model and the backdoored version, it is computationally infeasible to find even a single input where they differ. This property implies that the backdoored model has generalization error comparable with the original model. Second, we demonstrate how to insert undetectable backdoors in models trained using the Random Fourier Features (RFF) learning paradigm or in Random ReLU networks. In this construction, undetectability holds against powerful white-box distinguishers: given a complete description of the network and the training data, no efficient distinguisher can guess whether the model is “clean” or contains a backdoor.
  • Our construction of undetectable backdoors also sheds light on the related issue of robustness to adversarial examples. In particular, our construction can produce a classifier that is indistinguishable from an “adversarially robust” classifier, but where every input has an adversarial example! In summary, the existence of undetectable backdoors represent a significant theoretical roadblock to certifying adversarial robustness.

Book

  • Work on the interview section. Ask about forms of bias, and how using the machine to find bias could help uncover patterns of it in humans as well. The idea of asking the same question a thousand times and getting a distribution of answers. Done! At least the first draft
  • Add something to the Epilogue about the tension between authoritarian and egalitarian governments
  • Play around with titles

SBIRs

  • 9:00 ITM discussion
  • Continue code generation. Need to make the BoardMonitor and BoardMonitorChild classes, then start running/stepping code within tool. I’d like to figure out tabs so that the JSON and hierarchy views could share the same screen space. Done!
Progress!
  • And remarkably, everything still works. Need to wire up the output of the dictionary

GPT Agents

  • Make a flier, email, and informed consent
  • Poke around at getting more technical keywords for things like science papers