Accurate and robust image copy-move forgery detection using adaptive keypoints and FQGPCET-GLCM feature

With the rapid development of image editing software, forged images have become a serious social problem because of their great destructiveness. Copy-move is one of the most commonly used types of forgery. The keypoint-based copy-move forgery detection (CMFD) techniques identify forged regions by extracting image keypoints and using local visual features. These methods show remarkable detection capability in some areas, such as memory requirements and computational cost. After years of research, the challenges of the current keypoint-based methods are as follows: 1) The number or distribution of extracted keypoints is not satisfactory, especially in smoothed and textured areas. 2) Many local visual features have poor robustness to geometric attacks and post-processing disturbances, which fundamentally limits the performance of algorithms. 3) The existing post-processing algorithms can’t effectively filter out false matched pairs and accurately locate the tampered areas. To overcome these problems, an accurate and robust image copy-move forgery detection method using adaptive keypoints and hybrid features is proposed. Firstly, an adaptive keypoint extraction method based on the simple linear iterative clustering (SLIC) and the K-multiple-means (KMM) is proposed, which can extract the dense and uniform keypoints in the whole image. Then, we combine the transform domain features based on the Fast Quaternion Generic Polar Complex Exponential Transform (FQGPCET) and the texture features based on the Gray-level co-occurrence matrix (GLCM) to obtain the robust hybrid features. The hybrid features have outstanding descriptive power. Then the feature matching is performed by the double-bit quantized locally sensitive hash (DBQ-LSH). Finally, a high-precision post-processing algorithm includes two-step filtering and two-step clustering is proposed. The experimental results demonstrate that the overall performance of the proposed algorithm is superior to that of other solutions for detecting copy-move forgery images.