Improved Image Re-sampling Detection Technique

In this modern age in which we are living, digital images play a vital role in many application areas. But at the same time the image retouching techniques has also increased. To cope with this problem, the field of digital forensics and investigation emerged and provided some trust in digital images. In this paper we proposed a technique for image authentication that detects the manipulations that are done in the digital images. In most of the image forgeries such as copy-and-paste forgery, region duplication forgery, image splicing forgery etc basic image operations or manipulations are often involved. Thus if there exists the evidence for basic image alterations in digital images we can say that the image has been altered. Re-sampling (rotation, rescaling) operation is often done in forged images. The existing interpolation-related spectral signature method detects re-sampling and also estimates parameters such as rotated angle and rescaled factor effectively. But the re-sampling detection method detects some unaltered images as altered one when the images are JPEG compressed. We have overcome that problem by adding noise in the input images thus improving its performance. This work is tested in USC-SIPI database which consists of general unaltered images and we compared our results with the available re-sampling detection method.