  Copy move forgery detection in digital images has become a very popular
research topic in the area of image forensics. Due to the availability of
sophisticated image editing tools and ever increasing hardware capabilities, it
has become an easy task to manipulate the digital images. Passive forgery
detection techniques are more relevant as they can be applied without the prior
information about the image in question. Block based techniques are used to
detect copy move forgery, but have limitations of large time complexity and
sensitivity against affine operations like rotation and scaling. Keypoint based
approaches are used to detect forgery in large images where the possibility of
significant post processing operations like rotation and scaling is more. A
hybrid approach is proposed using different methods for keypoint detection and
description. Speeded Up Robust Features (SURF) are used to detect the keypoints
in the image and Binary Robust Invariant Scalable Keypoints (BRISK) features
are used to describe features at these keypoints. The proposed method has
performed better than the existing forgery detection method using SURF
significantly in terms of detection speed and is invariant to post processing
operations like rotation and scaling. The proposed method is also invariant to
other commonly applied post processing operations like adding Gaussian noise
and JPEG compression
