Social network platforms such as Twitter, Instagram and Facebook are one of the fastest and most convenient means for sharing digital images. Digital images are generally accepted as credible news but, it may undergo ...Social network platforms such as Twitter, Instagram and Facebook are one of the fastest and most convenient means for sharing digital images. Digital images are generally accepted as credible news but, it may undergo some manipulations before being shared without leaving any obvious traces of tampering; due to existence of the powerful image editing softwares. Copy-move forgery technique is a very simple and common type of image forgery, where a part of the image is copied and then pasted in the same image to replicate or hide some parts from the image. In this paper, we proposed a copy-scale-move forgery detection method based on Scale Invariant Feature Operator (SFOP) detector. The keypoints are then described using MROGH descriptor. Experimental results show that the proposed method is able to locate and detect the forgery even if under some geometric transformations such as scaling.展开更多
Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus.In copy-move forgery,the assailant intends to hide a portion of an image by pasting other portio...Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus.In copy-move forgery,the assailant intends to hide a portion of an image by pasting other portions of the same image.The detection of such manipulations in images has great demand in legal evidence,forensic investigation,and many other fields.The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors,such as local ternary pattern,local phase quantization,local Gabor binary pattern histogram sequence,Weber local descriptor,and local monotonic pattern,and classifiers such as optimized support vector machine and optimized NBC.The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated,even if the test image is subjected to attacks such as JPEG compression,scaling,rotation,and brightness variation.CoMoFoD,CASIA,and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms.The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.展开更多
基金The authors would like to thank all anonymous reviewers for their insightful comments. Additionally, This work is supported by the National Natural Science Foundation of China (Grant Number: 61471141, 61301099, 61361166006), the Fundamental Research Funds for the Central Universities (Grant Number: HIT. KISTP. 201416, HIT. KISTP. 201414).
文摘Social network platforms such as Twitter, Instagram and Facebook are one of the fastest and most convenient means for sharing digital images. Digital images are generally accepted as credible news but, it may undergo some manipulations before being shared without leaving any obvious traces of tampering; due to existence of the powerful image editing softwares. Copy-move forgery technique is a very simple and common type of image forgery, where a part of the image is copied and then pasted in the same image to replicate or hide some parts from the image. In this paper, we proposed a copy-scale-move forgery detection method based on Scale Invariant Feature Operator (SFOP) detector. The keypoints are then described using MROGH descriptor. Experimental results show that the proposed method is able to locate and detect the forgery even if under some geometric transformations such as scaling.
文摘Passive image forgery detection methods that identify forgeries without prior knowledge have become a key research focus.In copy-move forgery,the assailant intends to hide a portion of an image by pasting other portions of the same image.The detection of such manipulations in images has great demand in legal evidence,forensic investigation,and many other fields.The paper aims to present copy-move forgery detection algorithms with the help of advanced feature descriptors,such as local ternary pattern,local phase quantization,local Gabor binary pattern histogram sequence,Weber local descriptor,and local monotonic pattern,and classifiers such as optimized support vector machine and optimized NBC.The proposed algorithms can classify an image efficiently as either copy-move forged or authenticated,even if the test image is subjected to attacks such as JPEG compression,scaling,rotation,and brightness variation.CoMoFoD,CASIA,and MICC datasets and a combination of CoMoFoD and CASIA datasets images are used to quantify the performance of the proposed algorithms.The proposed algorithms are more efficient than state-of-the-art algorithms even though the suspected image is post-processed.
