The increasing popularity of the internet suggests that digital multimedia has become easier to transmit and acquire more rapidly. This also means that this multimedia has become more susceptible to tampering through forgery. One type of forgery, known as copy-move duplication, is a specified type that usually involves image tampering. In this study, a keypoint-based image forensics approach based on a superpixel segmentation algorithm and Helmert transformation has been proposed. The purpose of this approach is to detect copy-move forgery images and to obtain forensic information. The procedure of the proposed approach consists of the following phases. First, we extract the keypoints and their descriptors by using a scale-invariant feature transform (SIFT) algorithm. Then, based on the descriptor, matching pairs will be obtained by calculating the similarity between keypoints. Next, we will group these matching pairs based on spatial distance and geometric constraints via Helmert transformation to obtain the coarse forgery regions. Then, we refine these coarse forgery regions and remove mistakes or isolated areas. Finally, the forgery regions can be localized more precisely. Our proposed approach is a more robust solution for scaling, rotation, and compression forgeries. The experimental results obtained from testing different datasets demonstrate that the proposed method can obtain impressive precision/recall rates in comparison to state-of-the-art methods.

中文翻译：

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使用超像素分割和Helmert变换对图像取证进行复制移动伪造检测

互联网的日益普及表明数字多媒体已变得更容易传输和获取。这也意味着该多媒体变得更容易受到伪造的篡改。一种伪造类型，称为复制移动复制，是一种通常涉及图像篡改的指定类型。在这项研究中，提出了一种基于点的图像取证方法，该方法基于超像素分割算法和Helmert变换。这种方法的目的是检测复制移动的伪造图像并获取法医信息。提议的方法的过程包括以下几个阶段。首先，我们使用尺度不变特征变换（SIFT）算法提取关键点及其描述符。然后，基于描述符，将通过计算关键点之间的相似度来获得匹配对。接下来，我们将通过Helmert变换基于空间距离和几何约束对这些匹配对进行分组，以获取粗糙的伪造区域。然后，我们完善这些粗略的伪造区域，并消除错误或孤立区域。最后，可以更精确地定位伪造区域。我们提出的方法是针对缩放，旋转和压缩伪造的更可靠的解决方案。通过测试不同的数据集获得的实验结果表明，与最先进的方法相比，该方法可以实现令人印象深刻的精度/召回率。我们将通过Helmert变换根据空间距离和几何约束对这些匹配对进行分组，以获取粗糙的伪造区域。然后，我们完善这些粗略的伪造区域，并消除错误或孤立的区域。最后，可以更精确地定位伪造区域。我们提出的方法是针对缩放，旋转和压缩伪造的更可靠的解决方案。通过测试不同的数据集获得的实验结果表明，与最先进的方法相比，该方法可以实现令人印象深刻的精度/召回率。我们将通过Helmert变换根据空间距离和几何约束对这些匹配对进行分组，以获取粗糙的伪造区域。然后，我们完善这些粗略的伪造区域，并消除错误或孤立的区域。最后，可以更精确地定位伪造区域。我们提出的方法是针对缩放，旋转和压缩伪造的更可靠的解决方案。通过测试不同的数据集获得的实验结果表明，与最先进的方法相比，该方法可以实现令人印象深刻的精度/召回率。