The authentication of digital images poses a significant challenge due to the wide range of image forgery techniques employed, with one notable example being a copy-move forgery. This form of forgery involves duplicating and relocating segments of an image within the same image, often accompanied by geometric transformations to deceive viewers into perceiving the forged image as authentic. Furthermore, additional processing techniques like scaling, rotation, JPEG compression, and the application of Additive White Gaussian Noise (AWGN) are frequently employed to further obscure any traces of forgery, making the detection and verification process even more complex. This paper presents a novel approach for detecting copy-move forgery in digital images using the self-supervised image keypoint detector, SuperPoint. Our approach leverages the advanced capabilities of SuperPoint, which combines keypoint detection and descriptor extraction, to identify and localize copy-move forgery accurately. One important aspect of our approach is its ability to handle images with different textures, including smooth and self-similar structural images. The proposed approach is able to produce stable results in images with various attacks, making it a functional and reliable tool for detecting copy-move forgery in a diverse range of forged images. Comparative analysis with existing forgery detection methods shows the superior performance of our proposed approach. Furthermore, the computational efficiency of our algorithm enables real-time forgery detection. Our approach using SuperPoint offers an effective solution for detecting copy-move forgery in digital images, making it valuable for image forensics and authenticity.