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.

翻译：图像取证在我们的日常生活中日益重要。在各种伪造类型中，复制-移动伪造检测受到了学术界的广泛关注。基于关键点的算法，尤其是基于尺度不变特征变换的算法，已取得了显著成果。然而，当篡改区域出现在平滑区域时，多数关键点检测算法无法生成足够的匹配对，导致匹配不足。为此，本文引入熵图像，基于尺度不变特征变换检测器确定关键点的坐标与尺度，使得预处理更适合解决上述问题。此外，我们开发了一种重叠熵层级聚类算法，以缓解关键点灰度值非理想分布所导致的匹配复杂度增加问题。实验结果表明，本算法在性能与时间效率之间实现了良好平衡。