Fast and Robust Copy-Move Forgery Detection Using Wavelet Transforms and SURF

Most of the images today are stored in digital format. With the advent of digital imagery, tampering of images became easy. The problem has become altogether intensified due to the availability of image tampering softwares. Moreover there exist cameras with different resolutions and encoding techniques. Detecting forgery in such cases becomes a challenging task. Furthermore, the forged image may be compressed or resized which further complicates the problem. This article focuses on blind detection of copy-move forgery using a combination of an invariant feature transform and a wavelet transform. The feature transform employed is Speeded Up Robust Features (SURF) and the wavelet transforms employed are Discrete Wavelet Transform (DWT) and Dyadic Wavelet Transform (DyWT). A comparison between the performances of the two wavelet transforms is presented. The proposed algorithms are different from the previously proposed methods in a way that they are applied on the whole image, rather than after dividing the image in to blocks. A comparative study between the proposed algorithm and the previous block-based methods is presented. From the results obtained, we conclude that these algorithms perform better than their counterparts in terms of accuracy, computational complexity and robustness to various attacks.