Many image hashing algorithms have been proposed to detect the malicious tampering for content authentication. However, their tampering localization performance degrades dramatically on images with content-preserving distortion, as these algorithms cannot distinguish the malicious tampering from content-preserving distortion. A novel framework for existing hashing algorithms to improve their performance on tampering localization in distorted images is proposed in this paper. High precision of tampering localization in distorted images is achieved by controlling the robustness of extracted features. By experimenting with classical image hashing algorithms, the correctness of the proposed framework is proved.