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One of the most common practices in image tampering involves cropping a patch from a source and pasting it onto a target. In this paper, we present a novel method for the detection of such tampering operations in JPEG images. The lossy JPEG compression introduces inherent blocking artifacts into the image and our method exploits such artifacts to serve as a 'watermark' for the detection of image tampering. We develop the blocking artifact characteristics matrix (BACM) and show that, for the original JPEG images, the BACM exhibits regular symmetrical shape; for images that are cropped from another JPEG image and re-saved as JPEG images, the regular symmetrical property of the BACM is destroyed. We fully exploit this property of the BACM and derive representation features from the BACM to train a support vector machine (SVM) classifier for recognizing whether an image is an original JPEG image or it has been cropped from another JPEG image and re-saved as a JPEG image. We present experiment results to show the efficacy of our method.