  Unsupervised near-duplicate detection has many practical applications ranging
from social media analysis and web-scale retrieval, to digital image forensics.
It entails running a threshold-limited query on a set of descriptors extracted
from the images, with the goal of identifying all possible near-duplicates,
while limiting the false positives due to visually similar images. Since the
rate of false alarms grows with the dataset size, a very high specificity is
thus required, up to $1 - 10^{-9}$ for realistic use cases; this important
requirement, however, is often overlooked in literature. In recent years,
descriptors based on deep convolutional neural networks have matched or
surpassed traditional feature extraction methods in content-based image
retrieval tasks. To the best of our knowledge, ours is the first attempt to
establish the performance range of deep learning-based descriptors for
unsupervised near-duplicate detection on a range of datasets, encompassing a
broad spectrum of near-duplicate definitions. We leverage both established and
new benchmarks, such as the Mir-Flick Near-Duplicate (MFND) dataset, in which a
known ground truth is provided for all possible pairs over a general, large
scale image collection. To compare the specificity of different descriptors, we
reduce the problem of unsupervised detection to that of binary classification
of near-duplicate vs. not-near-duplicate images. The latter can be conveniently
characterized using Receiver Operating Curve (ROC). Our findings in general
favor the choice of fine-tuning deep convolutional networks, as opposed to
using off-the-shelf features, but differences at high specificity settings
depend on the dataset and are often small. The best performance was observed on
the MFND benchmark, achieving 96\% sensitivity at a false positive rate of
$1.43 \times 10^{-6}$.
