Title: SafetyNets: Verifiable Execution of Deep Neural Networks on an Untrusted Cloud,
Abstract: Inference using deep neural networks is often outsourced to the cloud since
it is a computationally demanding task. However, this raises a fundamental
issue of trust. How can a client be sure that the cloud has performed inference
correctly? A lazy cloud provider might use a simpler but less accurate model to
reduce its own computational load, or worse, maliciously modify the inference
results sent to the client. We propose SafetyNets, a framework that enables an
untrusted server (the cloud) to provide a client with a short mathematical
proof of the correctness of inference tasks that they perform on behalf of the
client. Specifically, SafetyNets develops and implements a specialized
interactive proof (IP) protocol for verifiable execution of a class of deep
neural networks, i.e., those that can be represented as arithmetic circuits.
Our empirical results on three- and four-layer deep neural networks demonstrate
the run-time costs of SafetyNets for both the client and server are low.
SafetyNets detects any incorrect computations of the neural network by the
untrusted server with high probability, while achieving state-of-the-art
accuracy on the MNIST digit recognition (99.4%) and TIMIT speech recognition
tasks (75.22%).