Abstract: With the rapid development of multimedia and network technology, the security of digital image content is becoming more and more prominent. In this paper, we propose a deep perceptual image authentication hashing schema based on window self-attention feature fusion, that can effectively detect whether the perceptual content of the original image has changed. It can be applied to content authentication, tampering recognition, copy detection, and other similar scenarios. This model uses a convolutional neural network architecture that integrates a window self-attention mechanism to build a hashing model that encompasses global and local image features. The model chunks the shallow features obtained from the backbone network and extracts the corresponding window features, then calculates the correlation between each intermediate local feature and the global feature to filter out the final local features, and finally inputs the local features and global features into the hash generation module for fusion and compression to obtain the final image hash code. In the training process, an integrated loss function based on hash loss and classification loss is used to constrain the model to improve the robustness and discrimination. The experimental results show that this scheme can achieve superior image content authentication performance compared with existing typical perceptual authentication hashing schemes.