scholarly journals Random Forest based Copy-Move Forgery Detection

2020 ◽  
Author(s):  
Jawad Khan
Keyword(s):  
Random Forest ◽  
Cross Correlation ◽  
Feature Matching ◽  
Random Forest Classifier ◽  
Forgery Detection ◽  
Feature Vectors ◽  
Color Correlogram ◽  
Copy Move Forgery Detection ◽  
Texture Attributes ◽  
Blind Image

Proving the authenticity of images is animportant part of image forensics. Copy-move forgery is amethod of forgery commonly followed in blind image forensics.We propose the use of a modified Auto Color Correlogram toobtain feature vectors from the forged image. The featuresextracted are sent as input to a RBF-SVM that gives a score forthe possibility of a copy-move situation. We then use anormalized cross correlation for feature matching with thesame feature vectors and then produce texture attributes assmoothness and Entropy. Based on the entropy andsmoothness we use a linear regression model to classify thisand obtain a predicted score. The two outputs obtained arepassed as input to a Random forest classifier which classifiesthe image as either forged or not forged.

scholarly journals Perceptual Hashing-Based Image Copy-Move Forgery Detection

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Huan Wang ◽  
Hongxia Wang
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Contrast Ratio ◽  
Lexicographic Order ◽  
Forgery Detection ◽  
Feature Vectors ◽  
Perceptual Hash ◽  
Perceptual Hashing ◽  
Gaussian Blurring ◽  
Copy Move Forgery Detection

This paper proposes a blind authentication scheme to identify duplicated regions for copy-move forgery based on perceptual hashing and package clustering algorithms. For all fixed-size image blocks in suspicious images, discrete cosine transform (DCT) is used to obtain their DCT coefficient matrixes. Their perceptual hash matrixes and perceptual hash feature vectors are orderly addressed. Moreover, a package clustering algorithm is proposed to replace traditional lexicographic order algorithms for improving the detection precision. Similar blocks can be identified by matching the perceptual hash feature vectors in each package and its adjacent package. The experimental results show that the proposed scheme can locate irregular tampered regions and multiple duplicated regions in suspicious images although they are distorted by some hybrid trace hiding operations, such as adding white Gaussian noise and Gaussian blurring, adjusting contrast ratio, luminance, and hue, and their hybrid operations.

scholarly journals Arabic tweeps dialect prediction based on machine learning approach

2021 ◽  
Vol 11 (2) ◽  
pp. 1627
Author(s):  
Khaled Alrifai ◽  
Ghaida Rebdawi ◽  
Nada Ghneim
Keyword(s):  
Machine Learning ◽  
Random Forest ◽  
Prediction Model ◽  
Feature Vector ◽  
Random Forest Classifier ◽  
Learning Approach ◽  
Feature Vectors ◽  
Machine Learning Approach ◽  
Important Trait

In this paper, we present our approach for profiling Arabic authors on twitter, based on their tweets. We consider here the dialect of an Arabic author as an important trait to be predicted. For this purpose, many indicators, feature vectors and machine learning-based classifiers were implemented. The results of these classifiers were compared to find out the best dialect prediction model. The best dialect prediction model was obtained using random forest classifier with full forms and their stems as feature vector.

Copy-Move Forgery Detection Based on Euclidean Distance and Texture Feature Analysis

2021 ◽  
Vol 2 (2) ◽  
pp. 25-32
Author(s):  
Ashutosh Kumara ◽  
Neha Janu
Keyword(s):  
Euclidean Distance ◽  
Feature Detection ◽  
Feature Matching ◽  
Texture Feature ◽  
Input Image ◽  
Feature Analysis ◽  
Forgery Detection ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Pca Algorithm

Digital images are important part of our life. Copy and Move forgery detection techniques are designed to detect edited part of the image. The copy and move forgery techniques are based on the feature detection and matching. The techniques which are designed so far use the Euclidean distance concept for feature matching. The feature detection techniques which are much popular like Haar transformation are used for feature extraction. In this research, the PCA algorithm is used for the simplification of features which are extracted with Haar transformation. The GLCM algorithm is used for texture feature analysis of input image. In the end, Euclidean distance is used for feature matching and mismatched features are marked as forgery. The proposed approach is implemented in MALTAB and results are analyzed in terms of accuracy.

scholarly journals Copy-Move Forgery Detection Technique for Forensic Analysis in Digital Images

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Toqeer Mahmood ◽  
Tabassam Nawaz ◽  
Aun Irtaza ◽  
Rehan Ashraf ◽  
Mohsin Shah ◽  
...  
Keyword(s):  
Feature Matching ◽  
Feature Space ◽  
Forensic Analysis ◽  
High Tech ◽  
Forgery Detection ◽  
Computationally Efficient ◽  
Forgery Attack ◽  
News Reports ◽  
Rbf Kernel ◽  
Copy Move Forgery Detection

Due to the powerful image editing tools images are open to several manipulations; therefore, their authenticity is becoming questionable especially when images have influential power, for example, in a court of law, news reports, and insurance claims. Image forensic techniques determine the integrity of images by applying various high-tech mechanisms developed in the literature. In this paper, the images are analyzed for a particular type of forgery where a region of an image is copied and pasted onto the same image to create a duplication or to conceal some existing objects. To detect the copy-move forgery attack, images are first divided into overlapping square blocks and DCT components are adopted as the block representations. Due to the high dimensional nature of the feature space, Gaussian RBF kernel PCA is applied to achieve the reduced dimensional feature vector representation that also improved the efficiency during the feature matching. Extensive experiments are performed to evaluate the proposed method in comparison to state of the art. The experimental results reveal that the proposed technique precisely determines the copy-move forgery even when the images are contaminated with blurring, noise, and compression and can effectively detect multiple copy-move forgeries. Hence, the proposed technique provides a computationally efficient and reliable way of copy-move forgery detection that increases the credibility of images in evidence centered applications.

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