Image Hashing

Image hashing techniques generate compact binary codes representing images, enabling efficient similarity search and retrieval. Current research focuses on improving the accuracy and robustness of these codes, exploring architectures like convolutional neural networks (CNNs), transformers, and hyperdimensional computing, often incorporating contrastive learning and self-supervised training methods to enhance performance. These advancements are crucial for applications such as copyright detection, tamper detection, and large-scale image retrieval, offering significant improvements in speed and storage efficiency compared to traditional methods. The development of more robust and versatile hashing methods continues to be a significant area of investigation.