Graphics processing unit based parallel copy move image forgery detection scheme

Abstract

In digital image forensics, an important area of research is forgery detection. Copy-move forgery is a specific type of image tampering where a part of the image is copied and pasted on some other part of the same image. Currently, robust copy move image forgery detection techniques are complex and face the problem of high computation time. CPU based and partial GPU based versions of copy move image forgery detection schemes currently exist, but parallelization can be improved to further reducing computation time. In this project, a fully GPU based detection scheme was designed and developed to achieve improved performance. In addition, this project uses counting bloom filters instead of radix sort for detecting duplicated image regions. To compare counting bloom filters with radix sort for duplicate detection, a detection scheme which supports both techniques is developed. The effectiveness of counting bloom filter is tested for robustness against copy move image forgeries with added post-processing and geometric transformations. The developed GPU based scheme is five times faster than multi-threaded CPU implementations for the feature extraction process while counting bloom filters performed 18 times faster than radix sort in duplicate detection. The scheme also achieves 84% detection rate. No false positives were detected by the scheme.