Copy-move forgery is a manipulation of copying and pasting specific patches from and to an image, with potentially illegal or unethical uses. Recent advances in the forensic methods for copy-move forgery have shown increasing success in detection accuracy and robustness. However, for images with high self-similarity or strong signal corruption, the existing algorithms often exhibit inefficient processes and unreliable results. This is mainly due to the inherent semantic gap between low-level visual representation and high-level semantic concept. In this paper, we present a very first study of trying to mitigate the semantic gap problem in copy-move forgery detection, with spatial pooling of local moment invariants for midlevel image representation. Our detection method expands the traditional works on two aspects: 1) we introduce the bag-of-visual-words model into this field for the first time, may meaning a new perspective of forensic study; 2) we propose a word-to-phrase feature description and matching pipeline, covering the spatial structure and visual saliency information of digital images. Extensive experimental results show the superior performance of our framework over state-of-the-art algorithms in overcoming the related problems caused by the semantic gap.

中文翻译：

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缩小语义差距：用于复制移动伪造检测的局部矩不变量的空间池化

复制移动伪造是一种从图像复制和粘贴特定补丁的操作，具有潜在的非法或不道德用途。复制移动伪造取证方法的最新进展表明检测准确性和鲁棒性越来越成功。然而，对于具有高自相似性或强信号损坏的图像，现有算法往往表现出低效的过程和不可靠的结果。这主要是由于低级视觉表示和高级语义概念之间固有的语义鸿沟。在本文中，我们提出了第一项研究，试图通过局部矩不变量的空间池化来缓解复制移动伪造检测中的语义差距问题，以实现中级图像表示。我们的检测方法在两个方面扩展了传统的工作：1）我们首次将视觉词袋模型引入该领域，可能意味着取证研究的新视角；2）我们提出了一个词到词的特征描述和匹配管道，涵盖了数字图像的空间结构和视觉显着性信息。广泛的实验结果表明，我们的框架在克服由语义差距引起的相关问题方面优于最先进的算法。