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Access to online visual search engines implies sharing of private usercontent - the query images. We introduce the concept of targeted mismatchattack for deep learning based retrieval systems to generate an adversarialimage to conceal the query image. The generated image looks nothing like theuser intended query, but leads to identical or very similar retrieval results.Transferring attacks to fully unseen networks is challenging. We showsuccessful attacks to partially unknown systems, by designing various lossfunctions for the adversarial image construction. These include loss functions,for example, for unknown global pooling operation or unknown input resolutionby the retrieval system. We evaluate the attacks on standard retrievalbenchmarks and compare the results retrieved with the original and adversarialimage.

Targeted Mismatch Adversarial Attack: Query with a Flower to Retrieve the Tower