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Fast and effective Keypoint-based image copy-move forgery detection using complex-valued moment invariants
Journal of Visual Communication and Image Representation ( IF 2.6 ) Pub Date : 2021-03-11 , DOI: 10.1016/j.jvcir.2021.103068
P. Niu , C. Wang , W. Chen , H. Yang , X. Wang

Copy-move forgery is one of the most common image tampering schemes, with the potential use for misleading the opinion of the general public. Keypoint-based detection methods exhibit remarkable performance in terms of computational cost and robustness. However, these methods are difficult to effectively deal with the cases when 1) forgery only involves small or smooth regions, 2) multiple clones are conducted or 3) duplicated regions undergo geometric transformations or signal corruptions. To overcome such limitations, we propose a fast and accurate copy-move forgery detection algorithm, based on complex-valued invariant features. First, dense and uniform keypoints are extracted from the whole image, even in small and smooth regions. Then, these keypoints are represented by robust and discriminative moment invariants, where a novel fast algorithm is designed especially for the computation of dense keypoint features. Next, an effective magnitude-phase hierarchical matching strategy is proposed for fast matching a massive number of keypoints while maintaining the accuracy. Finally, a reliable post-processing algorithm is developed, which can simultaneously reduce false negative rate and false positive rate. Extensive experimental results demonstrate the superior performance of our proposed scheme compared with existing state-of-the-art algorithms, with average pixel-level F-measure of 94.54% and average CPU-time of 36.25 s on four publicly available datasets.



中文翻译:

使用复值矩不变量快速有效地进行基于关键点的图像复制移动伪造检测

拷贝移动伪造是最常见的图像篡改方案之一,可能会误导公众意见。基于关键点的检测方法在计算成本和鲁棒性方面表现出卓越的性能。但是,这些方法很难有效地处理以下情况:1)伪造仅涉及较小或平滑的区域,2)进行了多个克隆,或者3)复制的区域经历了几何变换或信号损坏。为了克服这些限制,我们提出了一种基于复值不变特征的快速,准确的复制移动伪造检测算法。首先,即使在小而光滑的区域中,也要从整个图像中提取密集且统一的关键点。然后,这些关键点由鲁棒且具有判别力的矩不变量表示,其中专门设计了一种新颖的快速算法来计算密集的关键点特征。接下来,提出了一种有效的幅度相位分层匹配策略,用于在保持精度的同时快速匹配大量关键点。最后,开发了一种可靠的后处理算法,可以同时降低假阴性率和假阳性率。大量的实验结果表明,与现有的最新算法相比,我们提出的方案具有更好的性能,在四个可公开获得的数据集上,平均像素级F度量为94.54%,平均CPU时间为36.25 s。提出了一种有效的量级相分级匹配策略,用于在保持精度的同时快速匹配大量关键点。最后,开发了一种可靠的后处理算法,可以同时降低假阴性率和假阳性率。大量的实验结果表明,与现有的最新算法相比,我们提出的方案具有更好的性能,在四个可公开获得的数据集上,平均像素级F度量为94.54%,平均CPU时间为36.25 s。提出了一种有效的量级相分级匹配策略,用于在保持精度的同时快速匹配大量关键点。最后,开发了一种可靠的后处理算法,可以同时降低假阴性率和假阳性率。大量的实验结果表明,与现有的最新算法相比,我们提出的方案具有更好的性能,在四个可公开获得的数据集上,平均像素级F度量为94.54%,平均CPU时间为36.25 s。

更新日期:2021-03-19
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