An Approach for Detection of Copy-Move Forgery of Images Using Histogram of Orientated Gradients and Prewitt Filter

With the emergence of power image editing tools, it is now simply possible to manipulate the image, change the content, and edit or remove important features of an image. The operation of identifying manipulated images is of high importance since several millions of images are sent to the internet every day. The most common method of tampering with a digital image is copy-move forgery, in which a part of an image is duplicated and used to substitute another part of the same image at a different location. In this research, The input image is segmented into blocks, then a histogram of orientated gradients is applied to each block. HOG provides a grouping of gradient information and histograms related to gradient orientations. Statistical features are extracted and reduced to facilitate the measurement of similarity. Finally, duplicated image blocks are detected by identifying similar block pairs. The results of the experiment show that the proposed method is an effective and useful scheme with a degree of detection accuracy of 77% for images obtained from the comofod database and the ability to detect forged images using copy-move method, even when the image is distorted via transformations such as changes in brightness, color reduction , blurring or JPEG compression.

An Approach for Detection of Copy-Move Forgery of Images Using Histogram of Orientated Gradients and Prewitt Filter Keywords:

Digital image forgery detection , Histogram of orientated gradients , Copy-move image forgery , Feature extraction