A High Performance Copy-Move Image Forgery Detection Scheme on GPU

This paper presents an accelerated version of copy-move image forgery detection scheme on the Graphics Processing Units or GPUs. With the replacement of analog cameras with their digital counterparts and availability of powerful image processing software packages, authentication of digital images has gained importance in the recent past. This paper focuses on improving the performance of a copy-move forgery detection scheme based on radix sort by porting it onto the CPUs. This scheme has enhanced performance and is much more efficient compared to other methods without degradation of detection results. The CPU version of the radix-sort based detection scheme was developed in Mat lab and critical sections of the CPU version were coded in C-language using Mat lab's Mex interface to get the maximum performance. The GPU version was developed using Jacket GPU Engine for Mat lab and performs over twelve times faster than its optimized CPU variant. The contribution this paper makes towards blind image forensics is the use of integral images for computing feature vectors of overlapping blocks in block-matching technique and acceleration of the entire copy-move forgery detection scheme on the GPUs, not found in literature.