A novel image splicing detection method based on the inconsistency of image noise

2016 ◽  
Author(s):  
Jing Dong ◽  
Li Chen ◽  
Jing Tian ◽  
Xin Xu
Keyword(s):  
Detection Method ◽  
Image Noise ◽  
Image Splicing ◽  
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.

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.

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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