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.

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 Improved Image Splicing Forgery Detection by Combination of Conformable Focus Measures and Focus Measure Operators Applied on Obtained Redundant Discrete Wavelet Transform Coefficients

Symmetry ◽  
2019 ◽  
Vol 11 (11) ◽  
pp. 1392
Author(s):  
Subramaniam ◽  
Jalab ◽  
Ibrahim ◽  
Mohd Noor
Keyword(s):  
Wavelet Transform ◽  
Fractional Calculus ◽  
Discrete Wavelet Transform ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Information Carrier ◽  
Image Splicing Detection ◽  
Splicing Detection ◽  
Redundant Discrete Wavelet Transform

The image is the best information carrier in the current digital era and the easiest to manipulate. Image manipulation causes the integrity of this information carrier to be ambiguous. The image splicing technique is commonly used to manipulate images by fusing different regions in one image. Over the last decade, it has been confirmed that various structures in science and engineering can be demonstrated more precisely by fractional calculus using integrals or derivative operators. Many fractional-order-based techniques have been used in the image-processing field. Recently, a new specific fractional calculus, called conformable calculus, was delivered. Herein, we employ the combination of conformable focus measures (CFMs), and focus measure operators (FMOs) in obtaining redundant discrete wavelet transform (RDWT) coefficients for improving the image splicing forgery detection. The process of image splicing disorders the content of tampered image and causes abnormality in the image features. The spliced region’s boundaries are usually blurring to avoid detection. To make use of the blurred information, both CFMs and FMOs are used to calculate the degree of blurring of the tampered region’s boundaries for image splicing detection. The two public image datasets IFS-TC and CASIA TIDE V2 are used for evaluation of the proposed method. The obtained results of the proposed method achieved accuracy rate 98.30% for Cb channel on IFS-TC image dataset and 98.60% of the Cb channel on CASIA TIDE V2 with 24-D feature vector. The proposed method exhibited superior results compared with other image splicing detection methods.

Digital image splicing detection technique using optimal threshold based local ternary pattern

2020 ◽  
Vol 79 (19-20) ◽  
pp. 12829-12846
Author(s):  
Navdeep Kanwal ◽  
Akshay Girdhar ◽  
Lakhwinder Kaur ◽  
Jaskaran S. Bhullar
Keyword(s):  
Digital Image ◽  
Detection Technique ◽  
Optimal Threshold ◽  
Image Splicing ◽  
Local Ternary Pattern ◽  
Image Splicing Detection ◽  
Splicing Detection

scholarly journals Image splicing detection based on noise level approach

2020 ◽  
Vol 31 (4) ◽  
pp. 55
Author(s):  
Mohammed Kassem Alshwely ◽  
Saad N. AlSaad
Keyword(s):  
Digital Media ◽  
Noise Level ◽  
Detection Method ◽  
Promising Result ◽  
Rapid Development ◽  
Principal Component ◽  
Detection Methods ◽  
Image Splicing ◽  
Image Splicing Detection ◽  
Splicing Detection

The rapid development in technology and the spread of editing image software has led to spread forgery in digital media. It is now not easy by just looking at an image to know whether the image is original or has been tampered. This article describes a new image splicing detection method based on noise level as a major feature to detect the tempered region. Principal Component Analysis (PCA) is exploited to estimate the noise of image and the K-means clustering for authentic and forged region classification. The proposed method adopts Columbia Uncompressed Image Splicing Dataset for evaluation and effectiveness. The experimental results for 360 images demonstrate that the method achieved an 83.33% for detecting tampered region this percentage represent a promising result competed with Stat-of-art splicing detection methods.

Effective Deep Features for Image Splicing Detection

2021 ◽  
Author(s):  
Ismail Taha Ahmed ◽  
Baraa Tareq Hammad ◽  
Norziana Jamil
Keyword(s):  
Image Splicing ◽  
Image Splicing Detection ◽  
Splicing Detection
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