  With the increase in interchange of data, there is a growing necessity of
security. Considering the volumes of digital data that is transmitted, they are
in need to be secure. Among the many forms of tampering possible, one
widespread technique is Copy Move Forgery CMF. This forgery occurs when parts
of the image are copied and duplicated elsewhere in the same image. There exist
a number of algorithms to detect such a forgery in which the primary step
involved is feature extraction. The feature extraction techniques employed must
have lesser time and space complexity involved for an efficient and faster
processing of media. Also, majority of the existing state of art techniques
often tend to falsely match similar genuine objects as copy move forged during
the detection process. To tackle these problems, the paper proposes a novel
algorithm that recognizes a unique approach of using Hus Invariant Moments and
Log polar Transformations to reduce feature vector dimension to one feature per
block simultaneously detecting CMF among genuine similar objects in an image.
The qualitative and quantitative results obtained demonstrate the effectiveness
of this algorithm.
