Image tampering, being readily facilitated and proliferated by today’s digital techniques, is increasingly causing problems regarding the authenticity of images. As the most popular multimedia data, JPEG images can be easily tampered without leaving any clues; therefore, JPEG-based forensics, including the detection of double compression, interpolation, rotation, etc., has become an active research topic in multimedia forensics. Nevertheless, the interesting issue of detecting image tampering and its related operations by using the same quantization matrix has not been fully investigated. Aiming to detect such forgery manipulations under the same quantization matrix, we propose a detection method by using shift-recompressionbased reshuffle characteristic features. Learning classifiers are applied to evaluating the efficacy. Our experimental results indicate that the approach is indeed highly effective in detecting image tampering and relevant manipulations with the same quantization matrix.