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With the rapid advancement and increased use of deep learning models in imageidentification, security becomes a major concern to their deployment insafety-critical systems. Since the accuracy and robustness of deep learningmodels are primarily attributed from the purity of the training samples,therefore the deep learning architectures are often susceptible to adversarialattacks. Adversarial attacks are often obtained by making subtle perturbationsto normal images, which are mostly imperceptible to humans, but can seriouslyconfuse the state-of-the-art machine learning models. What is so special in theslightest intelligent perturbations or noise additions over normal images thatit leads to catastrophic classifications by the deep neural networks? Usingstatistical hypothesis testing, we find that Conditional VariationalAutoEncoders (CVAE) are surprisingly good at detecting imperceptible imageperturbations. In this paper, we show how CVAEs can be effectively used todetect adversarial attacks on image classification networks. We demonstrate ourresults over MNIST, CIFAR-10 dataset and show how our method gives comparableperformance to the state-of-the-art methods in detecting adversaries while notgetting confused with noisy images, where most of the existing methods falter.

Detecting Adversaries, yet Faltering to Noise? Leveraging Conditional  Variational AutoEncoders for Adversary Detection in the Presence of Noisy  Images