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Undetectable Backdoors in Machine-Studying Fashions

New paper: “Planting Undetectable Backdoors in Machine Learning Models:

Summary: Given the computational value and technical experience required to coach machine studying fashions, customers could delegate the duty of studying to a service supplier. We present how a malicious learner can plant an undetectable backdoor right into a classifier. On the floor, such a backdoored classifier behaves usually, however in actuality, the learner maintains a mechanism for altering the classification of any enter, with solely a slight perturbation. Importantly, with out the suitable “backdoor key”, the mechanism is hidden and can’t be detected by any computationally-bounded observer. We reveal two frameworks for planting undetectable backdoors, with incomparable ensures.

First, we present plant a backdoor in any mannequin, utilizing digital signature schemes. The development ensures that given black-box entry to the unique mannequin and the backdoored model, it’s computationally infeasible to seek out even a single enter the place they differ. This property implies that the backdoored mannequin has generalization error comparable with the unique mannequin. Second, we reveal insert undetectable backdoors in fashions skilled utilizing the Random Fourier Options (RFF) studying paradigm or in Random ReLU networks. On this development, undetectability holds in opposition to highly effective white-box distinguishers: given a whole description of the community and the coaching information, no environment friendly distinguisher can guess whether or not the mannequin is “clear” or comprises a backdoor.

Our development of undetectable backdoors additionally sheds gentle on the associated problem of robustness to adversarial examples. Particularly, our development can produce a classifier that’s indistinguishable from an “adversarially sturdy” classifier, however the place each enter has an adversarial instance! In abstract, the existence of undetectable backdoors signify a big theoretical roadblock to certifying adversarial robustness.

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