Deep Learning on Digital Image Splicing Detection Using CFA Artifacts

2020 ◽  
Vol 12 (2) ◽  
pp. 31-44 ◽  
Author(s):  
Nadheer Younus Hussien ◽  
Rasha O. Mahmoud ◽  
Hala Helmi Zayed
Keyword(s):  
Digital Image ◽  
Feature Reduction ◽  
Color Filter ◽  
Forgery Detection ◽  
Image Splicing ◽  
Detection Scheme ◽  
Image Forgery ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Digital Image Forgery

Digital image forgery is a serious problem of an increasing attention from the research society. Image splicing is a well-known type of digital image forgery in which the forged image is synthesized from two or more images. Splicing forgery detection is more challenging when compared with other forgery types because the forged image does not contain any duplicated regions. In addition, unavailability of source images introduces no evidence about the forgery process. In this study, an automated image splicing forgery detection scheme is presented. It depends on extracting the feature of images based on the analysis of color filter array (CFA). A feature reduction process is performed using principal component analysis (PCA) to reduce the dimensionality of the resulting feature vectors. A deep belief network-based classifier is built and trained to classify the tested images as authentic or spliced images. The proposed scheme is evaluated through a set of experiments on Columbia Image Splicing Detection Evaluation Dataset (CISDED) under different scenarios including adding postprocessing on the spliced images such JPEG compression and Gaussian Noise. The obtained results reveal that the proposed scheme exhibits a promising performance with 95.05% precision, 94.05% recall, 94.05% true positive rate, and 98.197% accuracy. Moreover, the obtained results show the superiority of the proposed scheme compared to other recent splicing detection method.

scholarly journals Image Splicing Forgery Detection Scheme Using New Local Binary Pattern Varient

2020 ◽  
Vol 9 (3) ◽  
pp. 208
Author(s):  
Araz R. Abrahim ◽  
Mohd Sh. Mohd Rahim ◽  
Ahmed S. Sami
Keyword(s):  
Local Binary Pattern ◽  
Detection Method ◽  
Forgery Detection ◽  
Image Splicing ◽  
Detection Scheme ◽  
Local Ternary Pattern ◽  
Image Splicing Detection ◽  
Color Features ◽  
Splicing Detection ◽  
Robust To Noise

In this research develop passive image splicing detection method based on a new descriptor called Adaptive Threshold Mean Ternary Pattern (ATMTP). It was developed based on strength and weaknesses of both Local Binary Pattern (LBP) and Local Ternary Pattern (LTP). ATMTP extraction feature is normally achieved by using proposed mean based thresholding and adaptive ternary thresholding, the former is robust to noise while the latter is robust to noise and other photometric attacks. It is designed to withstand against photometric manipulations, be it single or double attacks. In this research the ATMTP color features extracted from R, G, and B channels have revealed that the present method achieved higher accuracy on standard datasets CASIA V2.0 out of 99.03%, Sensitivity 99.6%, and specificity 98.1%. Finally, in terms of accuracy, the proposed SFD scheme outperformed the best recent works in this area.

scholarly journals Image Forgery Detection and Localization using Modified JPEG Ghost

2019 ◽  
Vol 8 (12) ◽  
pp. 5433-5436
Keyword(s):  
Digital Image ◽  
Original Structure ◽  
Forgery Detection ◽  
Image Forgery ◽  
Digital Devices ◽  
Image Forgery Detection ◽  
Storage Format ◽  
Digital Image Forgery ◽  
Detection And Localization ◽  
Multiple Compression

Authenticity of an image taken digitally suffers severe threats as a result of increase in various powerful digital image editing tools. These tools modifies the image contents without leaving footprint of such modifications. We come up with a technique that analyzes digital image forgery detection in JPEG images which goes through multiple compression. Nearly all digital devices uses JPEG as a standard storage format to maintain the storage space. JPEG is a lossy compression standard. By using any image processing tools, when assailant changes any part of a JPEG image and save it, the alter part of the image has different compression artifacts. JPEG ghost algorithm is used to detect disparity in JPEG blocks that rise from improper alignments of JPEG blocks respect to original structure and detect local footprint of JPEG compression. In our work, our proposed technique will modify JPEG ghost detection to detect and localize digital image forgery.

Passive Aproaches for Detecting Image Tampering: A Review

2015 ◽  
Vol 73 (2) ◽  
Author(s):  
Fatma Salman Hashem ◽  
Ghazali Sulong
Keyword(s):  
Digital Image ◽  
Active Area ◽  
Forgery Detection ◽  
Image Forgery ◽  
Detection Techniques ◽  
Image Tampering ◽  
Highly Active ◽  
Image Forgery Detection ◽  
Hidden Data ◽  
Digital Image Forgery

This paper defines the presently used methods and approaches in the domain of digital image forgery detection.  A survey of a recent study is explored including an examination of the current techniques and passive approaches in detecting image tampering. This area of research is relatively new and only a few sources exist that directly relate to the detection of image forgeries. Passive, or blind, approaches for detecting image tampering are regarded as a new direction of research. In recent years, there has been significant work performed in this highly active area of research. Passive approaches do not depend on hidden data to detect image forgeries, but only utilize the statistics and/or content of the image in question to verify its genuineness. The specific types of forgery detection techniques are discussed below. 

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