In order to overcome such defects as low robustness and low detection rate induced by the sensitivity of current hash image authentication algorithm to the rotation operation, the compact image hash algorithm based on an improved local binary pattern (LBP) operator and the dynamic update transform is proposed. The linear interpolation technique is introduced to preprocess the input image and improve the scaling robustness of the hash sequence. The Ring division is used to transform the interpolation image into the secondary image. A H-LBP operator with strong descriptive ability was designed when the Heaviside function is embedded into traditional LBP operator. Considering the difference between the center pixel characteristics and the adjacent pixels, the anti-rotation robustness features are extracted. The compression sensing is introduced to reduce the dimensions of high-dimensional feature vector, and a compact transition hash array is output. A dynamic update transform mechanism is designed with the chaotic transform idea to encrypt the transition hash array, and the image hash sequence is obtained. Finally, the hash similarity between the input image and the detected image is calculated based on Hamming distance to achieve the authenticity of the image through the optimized decision threshold. The experimental results show that the proposed algorithm generates a smaller hash sequence and has better perceptual robustness to rotation, noise and other operations than the current hash algorithms.