Digital image forgery is a huge challenge in the present digital era. Hence there is a need for continuous development of robust techniques for image forgery detection. The proposed approach provides an advanced forgery detection method for copy-move attacks in digital images. It uses the Oriented FAST and Rotated BRIEF (ORB) as the principal method. The research algorithm first extracts key points through the FAST algorithm, then selects N best points. These key points are then enriched with directional information in the intensity centroid. Then the binary descriptors are generated using the BRIEF algorithm, and a greedy algorithm, which identifies low-correlation pixel blocks. The combination of all these steps results in a robust 256-bit descriptor, which holds the unique features of the digital image. To acknowledge the challenges of multi-resolution copy-move attacks, the proposed technique dynamically helps to identify in variant image sizes. In the overall experiment, the parameters of the algorithm, number of key points, and threshold value are dynamically changed for optimal performance across diverse resolutions for different images. The proposed approach is demonstrated through an experiment with one hundred images. The results of this experiment show that this technique can detect and locate copy-move forgery (CMF) attacks with high accuracy. This study could be an aid to digital forensics with a robust and adaptive solution for copy-move forgeries over multiple resolutions. The integration of ORB is going to help digital forensics for forgery detection with a high level of accuracy.