Going deeper into copy-move forgery detection: exploring image telltales via multi-scale analysis and voting processes

This work presents a new approach toward copy-move forgery detection based on multi-scale analysis and voting processes of a digital image. Given a suspicious image, we extract interest points robust to scale and rotation finding possible correspondences among them. We cluster correspondent points into regions based on geometric constraints. Thereafter, we construct a multi-scale image representation and for each scale, we examine the generated groups using a descriptor strongly robust to rotation, scaling and partially robust to compression, which decreases search space of duplicated regions and yields a detection map. The final decision is based on a voting process among all detection maps. We validate the method using various datasets comprising original and realistic image clonings. We compare the proposed method to 13 others from the literature and report promising results.

本研究提出了一种基于多尺度分析与投票流程的数字图像拷贝移动伪造检测（copy-move forgery detection）新方法。针对待检测的可疑图像，我们提取对尺度与旋转鲁棒的兴趣点，并从中查找可能存在的匹配对应点；随后基于几何约束将匹配对应点聚类为区域。此后，我们构建多尺度图像表征，针对每个尺度，使用对旋转、缩放具有强鲁棒性，且对压缩具备部分鲁棒性的图像描述子（descriptor）对生成的分组进行校验，此举可缩减重复区域的搜索空间并生成检测图。最终决策基于所有检测图间的投票流程。我们采用包含原始图像与真实图像克隆伪造样本的多类数据集对所提方法进行验证，并将其与现有文献中的13种同类方法进行对比，实验结果颇具前景。