title,doi,url,abstract,journal,publication_year,pmid,arxiv
Feature Based Analysis of Copy-Paste Image Tampering Detection, https://doi.org/10.32628/IJSRCSEIT, https://ijsrcseit.com/CSEIT1726166, Authentication of a digital image is a challenging task. A tampered image is created by altering some of its contents using standard image processing tools. Copy-paste tampering is created by copying some part of an image and pasting it within the same image for covering unwanted information or an object is the most used technique in digital image manipulation. The motive of copy-paste tampering detection technique is to locate regions that have been copied and pasted within the same image. A number of techniques are employed to detect copy-paste tampering; using image features / parameters is also one of them. In the present research work a parametric non-overlapping block-based tampering detection model has been applied to ensure the presence of copy-paste tampering in a given digital image. The behaviour of different parameters has been analysed after their implementation onto a wide variety of digital images having different types formats and dimensions. Statistical parameters of the input images of three different formats are computed analysed and compared with those of their tampered images using specific threshold values. The model is tested for three different formats and for seven different selected block sizes. The results show that the proposed model identifies the tampered area for all the given images and works well with low to moderate copy-paste tampering. The results obtained can be used as the initial verification of the images for tampering and to enhance the tampering detection process by identifying most likely cases of possible image tampering. The proposed model is tested with larger domain of images having different types formats and dimensions and for tampering within an image. However the model has limitations with certain geometrical transformations. , International Journal of Scientific Research in Computer Science Engineering and Information Technology, 2017, CSEIT1726166