  Image copy detection and retrieval from large databases leverage two
components. First, a neural network maps an image to a vector representation,
that is relatively robust to various transformations of the image. Second, an
efficient but approximate similarity search algorithm trades scalability (size
and speed) against quality of the search, thereby introducing a source of
error. This paper improves the robustness of image copy detection with active
indexing, that optimizes the interplay of these two components. We reduce the
quantization loss of a given image representation by making imperceptible
changes to the image before its release. The loss is back-propagated through
the deep neural network back to the image, under perceptual constraints. These
modifications make the image more retrievable. Our experiments show that the
retrieval and copy detection of activated images is significantly improved. For
instance, activation improves by $+40\%$ the Recall1@1 on various image
transformations, and for several popular indexing structures based on product
quantization and locality sensitivity hashing.
