Dual adversarial attacks: Fooling humans and classifiers

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Adversarial samples mostly aim at fooling machine learning (ML) models. They often involve minor pixel-based perturbations that are imperceptible to human observers. In this work, adversarial samples should fool both humans and ML models, which is important in two-stage decision processes. We perform changes on a higher abstraction level so that a target sample exhibits properties of a desired sample. Technically, we contribute by deriving a regularization scheme for autoencoders incorporating a classifier loss for smoothly interpolating between wildly different samples. The realism and effectiveness of generated samples are confirmed with a user study and other evaluations. Our experiments consider neural networks of four architectures, assessed on MNIST, FashionMNIST, QuickDraw and CIFAR-10. Results show that our scheme leads to superior performance compared to existing interpolation techniques: on average, other methods have an 11% higher failure rate when producing a sample that is of any of two interpolated classes. Furthermore, our attacks work in both white- and black-box settings.