Hybridized Technique for Copy-Move Forgery Detection Using Discrete Cosine Transform and Speeded-Up Robust Feature Techniques

Abstract

As the world has greatly experienced a serious advancement in the area of technological advancement over the years, the availability of lots of sophisticated and powerful image editing tools has been on the rise. These image editing tools have become easily available on the internet, which has made people who are a novice in the field of image editing, to be capable of tampering with an image easily without leaving any visible clue or trace behind, which has led to increase in digital images losing authenticity. This has led to developing various techniques for tackling authenticity and integrity of forged images. In this paper, a robust and enhanced algorithm is been developed in detecting copy-move forgery, which is done by hybridizing block-based DCT (Discrete Cosine Transform) technique and a keypointbased SURF (Speeded-Up Robust Feature)technique using the MATLAB platform. The performance of the above technique has been compared with DCT and SURF techniques as well as other hybridized techniques in terms of precision, recall, FPR and accuracy metrics using MICC-F220 dataset. This technique works by applying DCT to the forged image, with the main goal of enhancing the detection rate of such image and then SURF is applied to the resulting image with the main goal of detecting those areas that are been tampered with on the image. It has been observed that this paper's technique named HDS has an effective detection rate on the MICC-F220 dataset with multiple cloning attacks and other various attacks such as rotation, scaling, a combination of scaling plus rotation, blur, compression, and noise.

A Novel Machine Learning Approach for Forgery Detection and Verification in Digital Image

“…This magnitude was utilized by Wang et al (13) and from QDCT, the Markov features were extracted in three phases. DOC (Discrete Octonion Cosine) Transform was used in (14) for extracting the Markov features and Ojeniyi ei al. (15) used a system based on Keypoint and a block DCT which Speeded Up SURF (Robust Feature).…”

Copy-Move Forgeries Detection and Localization Using Two Levels of Keypoints Extraction

“…Joseph A. Ojeniyi [13] proposed a technique to detect copy move by hybridizing block-based DCT technique and, a keypoint-based SURF technique. In this technique, DCT is applied to the forged image with the main objective of enhancing the detection rate accuracy of the forged image, and then SURF is applied to the generating image with the core objective of detecting forged regions that have been tampered in the image.…”