A Survey of Perceptual Hashing for Multimedia

Perceptual hashing is a cutting-edge technique in the field of digital multimedia security, which maps the perceptual content of multimedia information to a fixed-length hash sequence to achieve content authentication. This survey provides a systematic overview of the definition, basic steps, main characters, and application scenarios of perceptual hashing. According to the different authentication objects, representative schemes of perceptual image hashing, perceptual video hashing, and perceptual hashing for neural network models are introduced, respectively. Both perceptual image hashing and perceptual video hashing can be divided into classical methods-based schemes and learning-based schemes, where learning-based schemes can be subdivided into supervised and unsupervised. Classical methods-based image hashing schemes can be divided into four categories: local feature-based, transformation-based, statistical feature-based, and dimensionality reduction-based. Classical methods-based video hashing schemes are mainly categorized into spatial feature-based schemes and spatial-temporal feature-based schemes. Additionally, we introduce the dataset composition and hash distance metric strategies for perceptual hashing and analyze the performance of some representative schemes. Finally, we summarize the existing schemes and offer prospects for future research directions and development trends.