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Inference using deep neural networks is often outsourced to the cloud sinceit is a computationally demanding task. However, this raises a fundamentalissue of trust. How can a client be sure that the cloud has performed inferencecorrectly? A lazy cloud provider might use a simpler but less accurate model toreduce its own computational load, or worse, maliciously modify the inferenceresults sent to the client. We propose SafetyNets, a framework that enables anuntrusted server (the cloud) to provide a client with a short mathematicalproof of the correctness of inference tasks that they perform on behalf of theclient. Specifically, SafetyNets develops and implements a specializedinteractive proof (IP) protocol for verifiable execution of a class of deepneural networks, i.e., those that can be represented as arithmetic circuits.Our empirical results on three- and four-layer deep neural networks demonstratethe run-time costs of SafetyNets for both the client and server are low.SafetyNets detects any incorrect computations of the neural network by theuntrusted server with high probability, while achieving state-of-the-artaccuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognitiontasks (75.22%).

SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud