  An adversarial query is an image that has been modified to disrupt
content-based image retrieval (CBIR) while appearing nearly untouched to the
human eye. This paper presents an analysis of adversarial queries for CBIR
based on neural, local, and global features. We introduce an innovative neural
image perturbation approach, called Perturbations for Image Retrieval Error
(PIRE), that is capable of blocking neural-feature-based CBIR. PIRE differs
significantly from existing approaches that create images adversarial with
respect to CNN classifiers because it is unsupervised, i.e., it needs no
labelled data from the data set to which it is applied. Our experimental
analysis demonstrates the surprising effectiveness of PIRE in blocking CBIR,
and also covers aspects of PIRE that must be taken into account in practical
settings, including saving images, image quality and leaking adversarial
queries into the background collection. Our experiments also compare PIRE (a
neural approach) with existing keypoint removal and injection approaches (which
modify local features). Finally, we discuss the challenges that face multimedia
researchers in the future study of adversarial queries.
