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Deep learning has become an integral part of various computer vision systemsin recent years due to its outstanding achievements for object recognition,facial recognition, and scene understanding. However, deep neural networks(DNNs) are susceptible to be fooled with nearly high confidence by anadversary. In practice, the vulnerability of deep learning systems againstcarefully perturbed images, known as adversarial examples, poses a diresecurity threat in the physical world applications. To address this phenomenon,we present, what to our knowledge, is the first ever image set basedadversarial defence approach. Image set classification has shown an exceptionalperformance for object and face recognition, owing to its intrinsic property ofhandling appearance variability. We propose a robust deep Bayesian image setclassification as a defence framework against a broad range of adversarialattacks. We extensively experiment the performance of the proposed techniquewith several voting strategies. We further analyse the effects of image size,perturbation magnitude, along with the ratio of perturbed images in each imageset. We also evaluate our technique with the recent state-of-the-art defencemethods, and single-shot recognition task. The empirical results demonstratesuperior performance on CIFAR-10, MNIST, ETH-80, and Tiny ImageNet datasets.

Deep Bayesian Image Set Classification: A Defence Approach against Adversarial Attacks