  Image copy detection (ICD) aims to determine whether a query image is an
edited copy of any image from a reference set. Currently, there are very
limited public benchmarks for ICD, while all overlook a critical challenge in
real-world applications, i.e., the distraction from hard negative queries.
Specifically, some queries are not edited copies but are inherently similar to
some reference images. These hard negative queries are easily false recognized
as edited copies, significantly compromising the ICD accuracy. This observation
motivates us to build the first ICD benchmark featuring this characteristic.
Based on existing ICD datasets, this paper constructs a new dataset by
additionally adding 100, 000 and 24, 252 hard negative pairs into the training
and test set, respectively. Moreover, this paper further reveals a unique
difficulty for solving the hard negative problem in ICD, i.e., there is a
fundamental conflict between current metric learning and ICD. This conflict is:
the metric learning adopts symmetric distance while the edited copy is an
asymmetric (unidirectional) process, e.g., a partial crop is close to its
holistic reference image and is an edited copy, while the latter cannot be the
edited copy of the former (in spite the distance is equally small). This
insight results in an Asymmetrical-Similarity Learning (ASL) method, which
allows the similarity in two directions (the query <-> the reference image) to
be different from each other. Experimental results show that ASL outperforms
state-of-the-art methods by a clear margin, confirming that solving the
symmetric-asymmetric conflict is critical for ICD. The NDEC dataset and code
are available at https://github.com/WangWenhao0716/ASL.
