Research Information System
PAMUKKALE UNIVERSITY JOURNAL OF ENGINEERING SCIENCES-PAMUKKALE UNIVERSITESI MUHENDISLIK BILIMLERI DERGISI, vol.28, no.5, pp.748-760, 2022 (ESCI)
Copy-move forgery, in which copied a region of the image and pasted onto another region on the same image, is the most encountered image forgery technique recently. Many frameworks have been presented to detect such forgeries. The main drawback with these approaches is their performance can be degraded when the duplicated image has undergone to some attacks. In this work, it is aimed to propose a hybrid approach, which uses deep features and DCT-based block features in a combined manner, to achieve higher detection performance even if under various attack scenarios. The proposed method uses a global contrast correction technique called LDR during the preprocessing phase and then extracts deep features from the image patches using a deep neural network. The method also obtains block features from the image to robustness against JPEG compression attacks. Hybrid features (deep and block-based features) are matched using Patch Match and then the proposed post-processing operation is realized on the matching results to minimize false matches. According to empirical studies performed on available databases, the proposed scheme gives better results when compared to both keypoint-based and block-based references even under attacks with challenging parameters.