  Perceptual image hashing methods are often applied in various objectives,
such as image retrieval, finding duplicate or near-duplicate images, and
finding similar images from large-scale image content. The main challenge in
image hashing techniques is robust feature extraction, which generates the same
or similar hashes in images that are visually identical. In this article, we
present a short review of the state-of-the-art traditional perceptual hashing
and deep learning-based perceptual hashing methods, identifying the best
approaches.
