An Image Perceptual Hashing Algorithm Based on Convolutional Neural Networks

The conventional perceptual hashing algorithms are constrained to a singular global feature extraction algorithm and lack efficient scalability adaptation. To address this problem, an image-perceptual hashing algorithm based on convolutional neural networks is proposed in this paper. First of all, the entire image is convolved by the backbone network to obtain a feature map. The Region Proposal Network (RPN) is employed to generate multiple-sized proposal frames at each location by using sliding windows. Considering the complexity and diversity of the object, proposal boxes of various sizes and shapes are formulated, and the local features are comprehensively exploited in an image, thereby, generating a perceptual hash code that can represent the semantic features of an image strongly. Moreover, The Mean Square Error (MSE) loss is incorporated into the optimization process to evaluate the coincidence between the proposal frame and the actual frame, generating more representative hash codes. Finally, an image perceptual hash code with high intuitive features can be formulated through iterative training of the proposed convolutional neural networks. Extensive experimental results demonstrate that the proposed image perceptual hashing algorithm based on a convolutional neural network surpasses other state-of-the-art methods.