The work aims to solve the problem that the current image forgery detection algorithm matches the feature points by mainly relying on the global search during the image forgery detection, thus leading to low detection efficiency; and to solve such deficiencies as low detection accuracy and detection error likely to occur during the detection of complex forgery image. An image forgery detection algorithm based on the nearest neighbor search coupling neighbor loss clustering was proposed. Firstly, the image was pre-processed by the method of integral image, and then the feature points were extracted by Hessian matrix determinant. The feature points were used to construct the circular region, and the feature descriptors of the feature points were obtained by calculating the Haar wavelet response in the circular region. Then, the KD tree index was established by the feature descriptor, and the nearest neighbor search method was used instead of the global search method in SURF to improve the SURF and complete the matching of feature points. Finally, the nearest neighbor function value was obtained with the nearest neighbor relation between the feature points, and then the feature points were clustered by the nearest neighbor function values, and the image forgery detection was completed. The experimental results showed that the proposed algorithm had higher detection efficiency and higher detection accuracy compared with the current image forgery detection algorithm. The proposed algorithm has high detection accuracy and good application value in the field of printing anti-counterfeiting and information security.