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Deep learning (DL) systems are increasingly deployed in safety- andsecurity-critical domains including self-driving cars and malware detection,where the correctness and predictability of a system's behavior for corner caseinputs are of great importance. Existing DL testing depends heavily on manuallylabeled data and therefore often fails to expose erroneous behaviors for rareinputs.  We design, implement, and evaluate DeepXplore, the first whitebox frameworkfor systematically testing real-world DL systems. First, we introduce neuroncoverage for systematically measuring the parts of a DL system exercised bytest inputs. Next, we leverage multiple DL systems with similar functionalityas cross-referencing oracles to avoid manual checking. Finally, we demonstratehow finding inputs for DL systems that both trigger many differential behaviorsand achieve high neuron coverage can be represented as a joint optimizationproblem and solved efficiently using gradient-based search techniques.  DeepXplore efficiently finds thousands of incorrect corner case behaviors(e.g., self-driving cars crashing into guard rails and malware masquerading asbenign software) in state-of-the-art DL models with thousands of neuronstrained on five popular datasets including ImageNet and Udacity self-drivingchallenge data. For all tested DL models, on average, DeepXplore generated onetest input demonstrating incorrect behavior within one second while runningonly on a commodity laptop. We further show that the test inputs generated byDeepXplore can also be used to retrain the corresponding DL model to improvethe model's accuracy by up to 3%.

DeepXplore: Automated Whitebox Testing of Deep Learning Systems