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 Improving RLRN Image Splicing Detection with the Use of PCA and Kernel PCA

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Zahra Moghaddasi ◽  
Hamid A. Jalab ◽  
Rafidah Md Noor ◽  
Saeed Aghabozorgi
Keyword(s):  
Dimension Reduction ◽  
Rapid Development ◽  
Principal Component ◽  
Computational Time ◽  
Support Vector ◽  
Kernel Pca ◽  
Image Splicing ◽  
Image Splicing Detection ◽  
Reduction Methods ◽  
Splicing Detection

Digital image forgery is becoming easier to perform because of the rapid development of various manipulation tools. Image splicing is one of the most prevalent techniques. Digital images had lost their trustability, and researches have exerted considerable effort to regain such trustability by focusing mostly on algorithms. However, most of the proposed algorithms are incapable of handling high dimensionality and redundancy in the extracted features. Moreover, existing algorithms are limited by high computational time. This study focuses on improving one of the image splicing detection algorithms, that is, the run length run number algorithm (RLRN), by applying two dimension reduction methods, namely, principal component analysis (PCA) and kernel PCA. Support vector machine is used to distinguish between authentic and spliced images. Results show that kernel PCA is a nonlinear dimension reduction method that has the best effect on R, G, B, and Y channels and gray-scale images.

BSFCoS: Block and Sparse Principal Component Analysis-Based Fast Co-Saliency Detection Method

2015 ◽  
Vol 30 (01) ◽  
pp. 1655003 ◽  
Author(s):  
Ning-Min Shen ◽  
Jing Li ◽  
Pei-Yun Zhou ◽  
Ying Huo ◽  
Yi Zhuang
Keyword(s):  
Detection Method ◽  
Feature Fusion ◽  
Rapid Development ◽  
Saliency Detection ◽  
Principal Component ◽  
Saliency Map ◽  
Detection Methods ◽  
Image Block ◽  
Feature Extraction Method ◽  
Sparse Features

Co-saliency detection, an emerging research area in saliency detection, aims to extract the common saliency from the multi images. The extracted co-saliency map has been utilized in various applications, such as in co-segmentation, co-recognition and so on. With the rapid development of image acquisition technology, the original digital images are becoming more and more clearly. The existing co-saliency detection methods processing these images need enormous computer memory along with high computational complexity. These limitations made it hard to satisfy the demand of real-time user interaction. This paper proposes a fast co-saliency detection method based on the image block partition and sparse feature extraction method (BSFCoS). Firstly, the images are divided into several uniform blocks, and the low-level features are extracted from Lab and RGB color spaces. In order to maintain the characteristics of the original images and reduce the number of feature points as well as possible, Truncated Power for sparse principal components method are employed to extract sparse features. Furthermore, K-Means method is adopted to cluster the extracted sparse features, and calculate the three salient feature weights. Finally, the co-saliency map was acquired from the feature fusion of the saliency map for single image and multi images. The proposed method has been tested and simulated on two benchmark datasets: Co-saliency Pairs and CMU Cornell iCoseg datasets. Compared with the existing co-saliency methods, BSFCoS has a significant running time improvement in multi images processing while ensuring detection results. Lastly, the co-segmentation method based on BSFCoS is also given and has a better co-segmentation performance.

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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