Copy Detection

Copy detection aims to identify instances of image duplication, even with modifications like resizing or compression, within vast databases. Recent research heavily utilizes deep convolutional neural networks trained with contrastive learning methods, often incorporating large memory banks and self-supervised techniques to generate robust image representations. These advancements significantly improve accuracy, particularly when combined with techniques that optimize image indexing and reduce quantization loss, leading to more efficient and effective copy detection systems. This has significant implications for content moderation, copyright infringement detection, and other applications requiring large-scale image similarity analysis.