Improved DSIFT Descriptor Based Copy-Rotate-Move Forgery Detection

Abstract

Abstract. In recent years, there has been a dramatic increase in the number of images captured by users. This is due to the wide availability of digital cameras and mobile phones which are able to capture and transmit images. Simultaneously, image-editing applications have become more usable, and a casual user can easily improve the quality of an image or change its content. The most common type of image modication is cloning, or copy-move forgery (CMF), which is easy to implement and dicult to detect. In most cases, it is hard to detect CMF with the naked eye and many possible manipulations (attacks) can be used to make the doctored image more realistic. In CMF, the forger copies part(s) of the image and pastes them back into the same image. One possible transformation is rotation, where an object is copied, rotated and pasted. Rotation-invariant features need to be used to detect Copy-Rotate-Move (CRM) forgery. In this paper we presented three contributions. First, a new technique to detect CMF is developed, using Dense Scale-Invariant Feature Transform (DSIFT). Second, a new improved DSIFT descriptor is implemented which is more robust to rotation than Zernike moments. Third, a new method to remove false matching is proposed. Extensive experiments have been conducted to train, evaluate and test the algorithms, the new feature vector and the suggested method to remove false matching. We show that the proposed method can detect forgery in images with blurring, brightness change, colour reduction, JPEG compression, variations in contrast and added noise.

Copy-Move Forgery Detection Using the Segment Gradient Orientation Histogram

“…In the next step, dilate each region using a disk with a one-pixel radius size. For each of the newly added pixels, compute the improved DSIFT [6]. Build a K-d tree and find the 2ANN for each new feature vector.…”

“…This type of forgery is more challenging to detect than other types, such as splicing and retouching. This is because the usual methods of detecting incompatibilities, using statistical measurements to compare different parts of the image, will be useless for CMF detection [6].…”