  Image forensics has become increasingly crucial in our daily lives. Among
various types of forgeries, copy-move forgery detection has received
considerable attention within the academic community. Keypoint-based
algorithms, particularly those based on Scale Invariant Feature Transform, have
achieved promising outcomes. However, most of keypoint detection algorithms
failed to generate sufficient matches when tampered patches were occurred in
smooth areas, leading to insufficient matches. Therefore, this paper introduces
entropy images to determine the coordinates and scales of keypoints based on
Scale Invariant Feature Transform detector, which make the pre-processing more
suitable for solving the above problems. Furthermore, an overlapped entropy
level clustering algorithm is developed to mitigate the increased matching
complexity caused by the non-ideal distribution of gray values in keypoints.
Experimental results demonstrate that our algorithm achieves a good balance
between performance and time efficiency.
