As the existing image tampering detection algorithms based on camera s ensor pattern noise have high false alarm rate in strong texture areas,a novel detection algorithm using adap tive thresholding adaptive to the texture complexity is proposed.According to Nyman Pierson criteria,the correla tion decision thresholds for different texture complexity are determined. Furthe rmore,the relationship between the correlation threshold and the texture complexity is obtained. While detecting,the correlati on coefficients of each pair of non-overlapping blocks from the noise residual of the test image and the refere nce sensor pattern noise of the inspected camera are computed first.The tampered areas are roughly localized ba sed on the correlation matching determined by the adaptive thresholds.Then,the fast zero mean normalized cross c orrelation (ZNCC) algorithm is used to calculate the correlation of the corresponding pixels in the roughly localized regions between the reference sens or pattern noise and the noise residual of the test image,and to realize the accurate localization.The experi mental results on the mobile phone image database show that the detection rate of the proposed algorithm is 98.8%,while the false alarm rate is only 1.897%.Compared with the existing methods using a f ixed threshold,the proposed algorithm using adaptive thresholds can effectively reduce the false alarm rate in the complex texture re gion ,and can accurately locate the tampered region.At the same time,the proposed algorithm can improve the effici ency by 26times compared with the conventional sliding-window-based algorithms.