A copy-move forgery detection technique using DBSCAN-based keypoint similarity matching

In an era marked by the contrast between information and disinformation, the ability to differentiate between authentic and manipulated images holds immense importance for both security professionals and the scientific community. Copy-move forgery is widely practiced thus, sprang up as a prevalent form of image manipulation among different types of forgeries. In this counterfeiting process, a region of an image is copied and pasted into different parts of the same image to hide or replicate the same objects. As copy-move forgery is hard to detect and localize, a swift and efficacious detection scheme based on keypoint detection is introduced. Especially the localization of forged areas becomes more difficult when the forged image is subjected to different post-processing attacks and geometrical attacks. In this paper, a robust, translation-invariant, and efficient copy-move forgery detection technique has been introduced. To achieve this goal, we developed an AKAZE-driven keypoint-based forgery detection technique. AKAZE is applied to the LL sub-band of the SWT-transformed image to extract translation invariant features, rather than extracting them directly from the original image. We then use the DBSCAN clustering algorithm and a uniform quantizer on each cluster to form group pairs based on their feature descriptor values. To mitigate false positives, keypoint pairs are separated by a distance greater than a predefined shift vector distance. This process forms a collection of keypoints within each cluster by leveraging their similarities in feature descriptors. Our clustering-based similarity-matching algorithm effectively locates the forged region. To assess the proposed scheme we deploy it on different datasets with post-processing attacks ranging from blurring, color reduction, contrast adjustment, brightness change, and noise addition. Even our method successfully withstands geometrical manipulations like rotation, skewing, and different affine transform attacks. Visual outcomes, numerical results, and comparative analysis show that the proposed model accurately detects the forged area with fewer false positives and is more computationally efficient than other methods.