Copy Move Forgery using Hus Invariant Moments and Log Polar Transformations
This addresses security issues in digital image transmission by improving detection accuracy for copy move forgeries, though it appears incremental as it builds on existing feature extraction methods.
The paper tackled the problem of copy move forgery detection in images by proposing a novel algorithm using Hus Invariant Moments and Log Polar Transformations to reduce feature vector dimensions and avoid false matches of similar genuine objects, with results demonstrating its effectiveness.
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.