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Motivated by the image rescaling estimation method proposed by Gallagher (2nd Canadian Conf. Computer & Robot Vision, 2005: 65-72), we develop an image rotation angle estimator based on the relations between the rotation angle and the frequencies at which peaks due to interpolation occur in the spectrum of the image's edge map. We then use rescaling/rotation detection and parameter estimation to detect fake objects inserted into images. When a forged image contains areas from different sources, or from another part of the same image, rescaling and/or rotation are often involved. In these geometric operations, interpolation is a necessary step. By dividing the image into blocks, detecting traces of rescaling and rotation in each block, and estimating the parameters, we can effectively reveal the forged areas in an image that have been rescaled and/or rotated. If multiple geometrical operations are involved, different processing sequences, i.e., repeated zooming, repeated rotation, rotation-zooming, or zooming-rotation, may be determined from different behaviors of the peaks due to rescaling and rotation. This may also provide a useful clue to image authentication.