Image copy detection is a task of detecting edited copies from any image within a reference database. While previous approaches have shown remarkable progress, the large size of their networks and descriptors remains disadvantage, complicating their practical application. In this paper, we propose a novel method that achieves a competitive performance by using a lightweight network and compact descriptors. By utilizing relational self-supervised distillation to transfer knowledge from a large network to a small network, we enable the training of lightweight networks with a small descriptor size. We introduce relational self-supervised distillation for flexible representation in a smaller feature space and applies contrastive learning with a hard negative loss to prevent dimensional collapse. For the DISC2021 benchmark, ResNet-50/EfficientNet-B0 are used as a teacher and student respectively, the micro average precision improved by 5.0%/4.9%/5.9% for 64/128/256 descriptor sizes compared to the baseline method.
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