Intensity and Rescale Invariant Copy Move Forgery Detection Techniques

In this contemporary world digital media such as videos and images behave as an active medium to carry valuable information across the globe on all fronts. However there are several techniques evolved to tamper the image which has made their authenticity untrustworthy. CopyMove Forgery CMF is one of the most common forgeries present in an image where a cluster of pixels are duplicated in the same image with potential postprocessing techniques. Various state-of-art techniques are developed in the recent years which are effective in detecting passive image forgery. However most methods do fail when the copied image is rescaled or added with certain intensity before being pasted due to de-synchronization of pixels in the searching process. To tackle this problem the paper proposes distinct novel algorithms which recognize a unique approach of using Hus invariant moments and Discreet Cosine Transformations DCT to attain the desired rescale invariant and intensity invariant CMF detection techniques respectively. The experiments conducted quantitatively and qualitatively demonstrate the effectiveness of the algorithm.

Copy Move Forgery using Hus Invariant Moments and Log Polar Transformations

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.