Image forgery poses a serious threat in electric power, medicine and other fields. Relevant departments need to pay a great price to identify the authenticity of the image. For traditional copy-move forgery image detection, the existing methods have at least two problems: low robustness and poor matching caused by a low number of feature points. Here, a novel similarity metric combining cosine and Jaccard is proposed to improve feature matching, which combines with oriented features from accelerated segment test and rotated binary robust independent elementary features (ORB) feature extraction to realise effective and fast image forgery detection. First, the image is divided into overlapping blocks, and ORB is used to extract the feature points of each image block to obtain the text information. Second, the novel similarity metric is used to calculate similarity and match the text. Finally, two image blocks with the highest similarity are located. The experimental results show that, on the one hand, ORB can greatly lessen detection time. On the other hand, the novel similarity metric can improve the poor matching caused by the small number of feature points. Combining the two methods can exhibit high robustness to translation, rotation, noise, illumination and JPEG compression.

中文翻译：

---

基于ORB和新颖相似度量的图像复制移动伪造检测算法

图像伪造在电力，医学和其他领域构成严重威胁。有关部门需要付出巨大的代价来识别图像的真实性。对于传统的复制移动伪造图像检测，现有方法至少存在两个问题：鲁棒性低和特征点数量少导致匹配性差。这里，提出了一种新的将余弦和Jaccard相结合的相似度度量来改进特征匹配，该特征度量与来自加速段测试和旋转二进制鲁棒独立基本特征（ORB）特征提取的定向特征相结合，以实现有效且快速的图像伪造检测。首先，将图像划分为重叠的块，然后使用ORB提取每个图像块的特征点以获得文本信息。第二，新颖的相似度指标用于计算相似度并匹配文本。最后，找到两个具有最高相似度的图像块。实验结果表明，一方面，ORB可以大大减少检测时间。另一方面，新颖的相似性度量可以改善由少量特征点引起的不良匹配。结合这两种方法可以表现出对平移，旋转，噪声，照明和JPEG压缩的高鲁棒性。