Copy-move (or copy-paste or cloning) is one of the most common image forgeries, wherein one or more region are copied and pasted within the same image. The motivations of such forgery include hiding an element in the image or emphasizing a particular object. Copy-move image forgery is more challenging to detect than other types, such as splicing and retouching. In recent years, keypoint based copy-move forgery detection, which extracts image keypoints and uses local visual features to identify duplicated regions, exhibits remarkable performance with respect to memory requirement and robustness against various attacks. However, these approaches usually have poor detection ability when copy-move forgeries only involve small or smooth regions. Moreover, they cannot always effectively deal with multiple copy-move forgeries. To tackle these challenges, we propose a robust and effective multiple copy-move forgeries detection and localization method through adaptive keypoint extraction, robust local feature representation, and offsets clustering based post-processing. Firstly, we develop a new image keypoint detector, named generic features from accelerated segment test, and extract adaptively the uniform distribution keypoints from the forged image by employing the adaptive-thresholding and non-maximum suppression. Then, we introduce fast quaternion polar complex exponential transform to describe the image keypoints compactly and distinctively, and utilize the KD tree based K-nearest neighbor matching to find possible correspondences. Finally, the falsely matched pairs are removed by employing the offsets information based candidate clustering, and the duplicated regions are localized using RANSAC and ZNCC algorithm. We conduct extensive experiments to evaluate the performance of the proposed approach, in which encouraging results validate the effectiveness of the proposed technique, especially for plain/multiple copy-move forgeries, in comparison with the state-of-the-art approaches recently proposed in the literature.

复制移动（或复制粘贴或克隆）是最常见的图像伪造之一，其中一个或多个区域被复制并粘贴在同一图像内。这种伪造的动机包括在图像中隐藏元素或强调特定对象。复制移动图像伪造比其他类型（例如剪接和修饰）更难检测。近年来，基于关键点的复制移动伪造检测可提取图像关键点并使用局部视觉特征来识别重复区域，在内存需求和针对各种攻击的鲁棒性方面表现出卓越的性能。但是，当复制移动伪造仅涉及较小或平滑的区域时，这些方法通常检测能力较差。而且，它们不能总是有效地处理多个复制移动伪造品。为了应对这些挑战，我们通过自适应关键点提取，鲁棒的局部特征表示和基于偏移聚类的后处理，提出了一种鲁棒且有效的多重复制移动伪造检测和定位方法。首先，我们开发了一种新的图像关键点检测器，从加速段测试中命名为通用特征，并通过采用自适应阈值和非最大抑制来自适应地从伪造图像中提取均匀分布关键点。然后，我们引入快速四元数极复数指数变换来紧凑而独特地描述图像关键点，并利用基于KD树的K最近邻匹配找到可能的对应关系。最后，通过采用基于偏移量信息的候选聚类来去除错误匹配的对，使用RANSAC和ZNCC算法定位重复区域。我们进行了广泛的实验，以评估所提出方法的性能，其中令人鼓舞的结果证实了所提出技术的有效性，特别是对于普通/多次复制-移动伪造而言，与最近在2000年提出的最新方法相比文献。