From the PDF:
"The system generates NeuralHash in two steps. First, an image is passed into a convolutional neural network to generate an N-dimensional, floating-point descriptor. Second, the descriptor is passed through a hashing scheme to convert the N floating-point numbers to M bits. Here, M is much smaller than the number of bits needed to represent the N floating-point numbers. NeuralHash achieves this level of compression and preserves sufficient information about the image so that matches and lookups on image sets are still successful, and the compression meets the storage and transmission requirements.
The neural network that generates the descriptor is trained through a self-supervised training scheme. Images are perturbed with transformations that keep them perceptually identical to the original, creating an original/perturbed pair. The neural network is taught to generate descriptors that are close to one another for the original/perturbed pair. Similarly, the network is also taught to generate descriptors that are farther away from one another for an original/distractor pair. A distractor is any image that is not considered identical to the original. "
Image classification on the other hand cares about if the image contains say a stop sign or a trash can. That’s useful for self driving cars etc.
Aka classification you might want to match two different bands playing the same song as identical. Where perception hashing would want them to be classified differently.
The perceptual hashing is based on AI techniques. “The system computes these hashes by using an embedding network to produce image descriptors and then converting those descriptors to integers using a Hyperplane LSH (Locality Sensitivity Hashing) process.”
The difference is AI classification is based on defining something as say a “Cat” and then the AI spits some association with how cat like the image is. This extracts features from an image then compares lists of features to specific images.