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The testing of Deep Neural Networks (DNNs) has become increasingly importantas DNNs are widely adopted by safety critical systems. While many test adequacycriteria have been suggested, automated test input generation for many types ofDNNs remains a challenge because the raw input space is too large to randomlysample or to navigate and search for plausible inputs. Consequently, currenttesting techniques for DNNs depend on small local perturbations to existinginputs, based on the metamorphic testing principle. We propose new ways tosearch not over the entire image space, but rather over a plausible input spacethat resembles the true training distribution. This space is constructed usingVariational Autoencoders (VAEs), and navigated through their latent vectorspace. We show that this space helps efficiently produce test inputs that canreveal information about the robustness of DNNs when dealing with realistictests, opening the field to meaningful exploration through the space of highlystructured images.

SINVAD: Search-based Image Space Navigation for DNN Image Classifier Test Input Generation