Deep product quantization networks (DPQNs) have been successfully used in image retrieval tasks, due to their powerful feature extraction ability and high efficiency of encoding high-dimensional visual features. Recent studies… Click to show full abstract
Deep product quantization networks (DPQNs) have been successfully used in image retrieval tasks, due to their powerful feature extraction ability and high efficiency of encoding high-dimensional visual features. Recent studies show that deep neural networks (DNNs) are vulnerable to input with small and maliciously designed perturbations (a.k.a., adversarial examples) for classification. However, little effort has been devoted to investigating how adversarial examples affect DPQNs, which raises the potential safety hazard when deploying DPQNs in a commercial search engine. To this end, we propose an adversarial example generation framework by generating adversarial query images for DPQN-based retrieval systems. Unlike the adversarial generation for the classic image classification task that heavily relies on ground-truth labels, we alternatively perturb the probability distribution of centroids assignments for a clean query, then we can induce effective non-targeted attacks on DPQNs in white-box and black-box settings. Moreover, we further extend the non-targeted attack to a targeted attack by a novel sample space averaging scheme (
               
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