Image forgeries are applied to give the digital images other
meanings or to deceive the viewers. Image forgeries appear in
many cases such as judges in courts, cybercrimes, military and
intelligence deception, or defamation of important characters.
There are many different types of image forgeries such as copy
move forgery, image retouching, image splicing, image morphing,
and image resampling. Copy move forgery is the widest type and
easy to apply between all digital image forgeries. Scale Invariant
Features Transform (SIFT) algorithm is used strongly to detect
copy move forgeries due to its efficiency in digital image analysis.
SIFT algorithm is extracting image features, which are invariant to
geometrical transformations such as scaling, translation, and
rotation. These features are used in performing the matching
between different views of a scene or an object. This paper
enhances the efficiency of using SIFT algorithm in detecting copy
move forgery by two ways. Firstly, it enhances the image itself by
applying different types of digital filters to reinforce the image
features giving the ability to detect forgeries. Butterworth low-pass
filter, a high-pass filter, and the combination of them are applied
to this task. Secondly, the matching strategy is adapted based on
a new thresholding approach to increase the true positive rate
and decrease the false positive rate. Experimental results show
that the proposed approach gives better results compared with
traditional copy-move detection approaches. In addition, it gives better stability and reliability to different copy-move forgery conditions.