scholarly journals New Texture Descriptor Based on Modified Fractional Entropy for Digital Image Splicing Forgery Detection

Entropy ◽  
2019 ◽  
Vol 21 (4) ◽  
pp. 371 ◽  
Author(s):  
Hamid Jalab ◽  
Thamarai Subramaniam ◽  
Rabha Ibrahim ◽  
Hasan Kahtan ◽  
Nurul Noor
Keyword(s):  
Input Image ◽  
Image Manipulation ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Texture Descriptor ◽  
Image Splicing ◽  
Feature Vectors ◽  
Low Dimension ◽  
Fractional Entropy ◽  
Tampered Image

Forgery in digital images is immensely affected by the improvement of image manipulation tools. Image forgery can be classified as image splicing or copy-move on the basis of the image manipulation type. Image splicing involves creating a new tampered image by merging the components of one or more images. Moreover, image splicing disrupts the content and causes abnormality in the features of a tampered image. Most of the proposed algorithms are incapable of accurately classifying high-dimension feature vectors. Thus, the current study focuses on improving the accuracy of image splicing detection with low-dimension feature vectors. This study also proposes an approximated Machado fractional entropy (AMFE) of the discrete wavelet transform (DWT) to effectively capture splicing artifacts inside an image. AMFE is used as a new fractional texture descriptor, while DWT is applied to decompose the input image into a number of sub-images with different frequency bands. The standard image dataset CASIA v2 was used to evaluate the proposed approach. Superior detection accuracy and positive and false positive rates were achieved compared with other state-of-the-art approaches with a low-dimension of feature vectors.

scholarly journals Frequency based edge-texture feature using Otsu’s based enhanced local ternary pattern technique for digital image splicing detection

2021 ◽  
Vol 10 (6) ◽  
pp. 3147-3155
Author(s):  
Vikas Srivastava ◽  
Sanjay Kumar Yadav
Keyword(s):  
Digital Image ◽  
Texture Feature ◽  
Detection Technique ◽  
Support Vector ◽  
Image Editing ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Image Splicing ◽  
Local Ternary Pattern ◽  
Micro Pattern

Sharing information through images is a trend nowadays. Advancements in the technology and user-friendly image editing tool make easy to edit the image and spread fake news through different social networking platforms. Forged image has been generated through an advanced image editing tool, so it is very challenging for image forensics to detect the micro discrepancy which distorted the micro pattern. This paper proposes an image forensic detection technique, which implies multi-level discrete wavelet transform to implement digital image filtering. Canny edge detection technique is implemented to detect the edge of the image to implement Otsu’s based enhanced local ternary pattern (OELTP), which can detect forgery-related artifact. DWT is implemented over Cb and Cr components of the image and using edge texture to improve the Otsu global threshold, which is used to extract features using ELTP technique. Support vector machine (SVM) is used for classification to find the image is forged or not. The performance of the work evaluated on three different open available data sets CASIA v1, CASIA v2, and Columbia. Our proposed work gives better results with some of the previous states of the work in terms of detection accuracy.

Improving the Computational Cost for Copied Region Detection in Forensic Images

2016 ◽  
Vol 2 (1) ◽  
pp. 55
Author(s):  
Tu Huynh-Kha ◽  
Thuong Le-Tien ◽  
Synh Ha ◽  
Khoa Huynh-Van
Keyword(s):  
Wavelet Transform ◽  
Euclidean Distance ◽  
Research Work ◽  
Computational Cost ◽  
Correlation Coefficients ◽  
Zernike Moments ◽  
Computational Time ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
Region Detection

This research work develops a new method to detect the forgery in image by combining the Wavelet transform and modified Zernike Moments (MZMs) in which the features are defined from more pixels than in traditional Zernike Moments. The tested image is firstly converted to grayscale and applied one level Discrete Wavelet Transform (DWT) to reduce the size of image by a half in both sides. The approximation sub-band (LL), which is used for processing, is then divided into overlapping blocks and modified Zernike moments are calculated in each block as feature vectors. More pixels are considered, more sufficient features are extracted. Lexicographical sorting and correlation coefficients computation on feature vectors are next steps to find the similar blocks. The purpose of applying DWT to reduce the dimension of the image before using Zernike moments with updated coefficients is to improve the computational time and increase exactness in detection. Copied or duplicated parts will be detected as traces of copy-move forgery manipulation based on a threshold of correlation coefficients and confirmed exactly from the constraint of Euclidean distance. Comparisons results between proposed method and related ones prove the feasibility and efficiency of the proposed algorithm.

A Robust Blind Image Forensic-Detection Method based on Fuzzy Technique

2019 ◽  
Vol 8 (3) ◽  
pp. 5926-5929
Keyword(s):  
Research Direction ◽  
Geometric Transformation ◽  
Detection Accuracy ◽  
Forensic Investigation ◽  
Image Forensic ◽  
Fuzzy Logic Approach ◽  
Logic Approach ◽  
Tampered Image ◽  
New Research ◽  
Blind Image

Blind forensic-investigation in a digital image is a new research direction in image security. It aims to discover the altered image content without any embedded security scheme. Block and key point based methods are the two dispensation options in blind image forensic investigation. Both the techniques exhibit the best performance to reveal the tampered image. The success of these methods is limited due to computational complexity and detection accuracy against various image distortions and geometric transformation operations. This article introduces different blind image tampering methods and introduces a robust image forensic investigation method to determine the copy-move tampered image by means of fuzzy logic approach. Empirical outcomes facilitate that the projected scheme effectively classifies copy-move type of forensic images as well as blurred tampered image. Overall detection accuracy of this method is high over the existing methods.

scholarly journals Improved YOLO Network for Free-Angle Remote Sensing Target Detection

2021 ◽  
Vol 13 (11) ◽  
pp. 2171
Author(s):  
Yuhao Qing ◽  
Wenyi Liu ◽  
Liuyan Feng ◽  
Wanjia Gao
Keyword(s):  
Remote Sensing ◽  
Feature Extraction ◽  
Target Detection ◽  
Multiple Scales ◽  
Classification Problem ◽  
Input Image ◽  
Detection Accuracy ◽  
Feature Maps ◽  
Regression Problem ◽  
Public Datasets

Despite significant progress in object detection tasks, remote sensing image target detection is still challenging owing to complex backgrounds, large differences in target sizes, and uneven distribution of rotating objects. In this study, we consider model accuracy, inference speed, and detection of objects at any angle. We also propose a RepVGG-YOLO network using an improved RepVGG model as the backbone feature extraction network, which performs the initial feature extraction from the input image and considers network training accuracy and inference speed. We use an improved feature pyramid network (FPN) and path aggregation network (PANet) to reprocess feature output by the backbone network. The FPN and PANet module integrates feature maps of different layers, combines context information on multiple scales, accumulates multiple features, and strengthens feature information extraction. Finally, to maximize the detection accuracy of objects of all sizes, we use four target detection scales at the network output to enhance feature extraction from small remote sensing target pixels. To solve the angle problem of any object, we improved the loss function for classification using circular smooth label technology, turning the angle regression problem into a classification problem, and increasing the detection accuracy of objects at any angle. We conducted experiments on two public datasets, DOTA and HRSC2016. Our results show the proposed method performs better than previous methods.

scholarly journals Universal statistical steganalytic method

2017 ◽  
Vol 68 (2) ◽  
pp. 117-124
Author(s):  
Martin Broda ◽  
Vladimír Hajduk ◽  
Dušan Levický
Keyword(s):  
Wavelet Transformation ◽  
Ensemble Classifier ◽  
Secret Message ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Discrete Cosine Transformation ◽  
Static Images ◽  
Novel Method ◽  
Statistical Steganalysis ◽  
Popular Image

Abstract Novel image steganalytic method used to detection of secret message in static images is introduced in this paper. This method is based on statistical steganalysis (SS), where statistical vector is composed by 285 statistical features (parameters) extracted from DCT (Discrete Cosine Transformation) domain and 46 features extracted mainly from DWT (Discrete Wavelet Transformation) domain. Classification process was realized by Ensemble classifier that was helpful in reduction of computational and time complexity. Proposed steganalytic method was verified by detection of popular image steganographic methods. Novel method was also compared with existing steganalytic methods by overall detection accuracy of a secret message.

scholarly journals Copy Move Forgery Detection Through Differential Excitation Component-Based Texture Features

2020 ◽  
Vol 12 (3) ◽  
pp. 27-44
Author(s):  
Gulivindala Suresh ◽  
Chanamallu Srinivasa Rao
Keyword(s):  
Feature Vector ◽  
Texture Feature ◽  
Texture Features ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Copy Move Forgery Detection ◽  
Occurrence Matrix ◽  
Differential Excitation

Copy-move forgery (CMF) is an established process to copy an image segment and pastes it within the same image to hide or duplicate a portion of the image. Several CMF detection techniques are available; however, better detection accuracy with low feature vector is always substantial. For this, differential excitation component (DEC) of Weber Law descriptor in combination with the gray level co-occurrence matrix (GLCM) approach of texture feature extraction for CMFD is proposed. GLCM Texture features are computed in four directions on DEC and this acts as a feature vector for support vector machine classifier. These texture features are more distinguishable and it is validated through other two proposed methods based on discrete wavelet transform-GLCM (DWT-GLCM) and GLCM. Experimentation is carried out on CoMoFoD and CASIA databases to validate the efficacy of proposed methods. Proposed methods exhibit resilience against many post-processing attacks. Comparative analysis with existing methods shows the superiority of the proposed method (DEC-GLCM) with regard to detection accuracy.

Statistical Methods and Artificial Neural Networks Techniques in Electromyography

2012 ◽  
Vol 1 (1) ◽  
pp. 39-47 ◽  
Author(s):  
Ahmad Taher Azar ◽  
Valentina E. Balas
Keyword(s):  
Neural Network ◽  
Statistical Methods ◽  
Muscle Activity ◽  
Masseter Muscle ◽  
Discrete Wavelet ◽  
Neural Network Classifier ◽  
Denture Base ◽  
Feature Vectors ◽  
Significant Difference ◽  
Before And After

This work represents a comparative study for the activity of the masseter muscle for patients before trial base denture insertion and the activity of the same muscle after trial denture base insertion for both right and left masseter muscles. The study tried to find if there were significant differences in the activity of the masseter muscle before and after patients wearing their trial denture base using two approaches: parametric statistical methods and a Neural Network Classifier. Statistical analysis was performed on three feature vectors extracted from autoregressive (AR) modeling, Discrete Wavelet Transform (WT), and from Wavelet Packet Transform (WP). The least significant difference test and the student t-test have not proved significant differences in the masseter muscle activity before and after wearing denture. However, using the same feature vectors, a neural network classifier has proved that there are significant differences in the masseter muscle activity before and after patients wearing trial denture base.

Real-Time Human Detection Based on the Improved Local Binary Pattern

2014 ◽  
Vol 1044-1045 ◽  
pp. 1246-1250
Author(s):  
Dong Mei Wu ◽  
Xing Ma ◽  
Jing Wang ◽  
Hao Zhang
Keyword(s):  
Real Time ◽  
Local Binary Pattern ◽  
Human Detection ◽  
Computational Method ◽  
Detection Accuracy ◽  
Real Time System ◽  
Feature Vectors ◽  
Linear Classifier ◽  
Detection Window ◽  
Testing Speed

By analyzing the detection accuracy and the testing speed of the Local Binary Pattern. we propose an improved LBP algorithm and apply it in human detection. Through the signs of the comparisons among neighboring pixels, it will get the histogram of the detection window. Then we can encode the global contour by the distribution coefficient of the histogram. when the Linear classifier is used, we propose a fast computational method that does not need to explicitly generate feature vectors and not require feature vectors normalization. experiment shows that this method has higher efficiency and can’t reduce the accuracy, it achieves 19 fps speed and can be used in a real-time system.

Solving Time Complexity Issue in Copy-Move Forgery Detection Thru Pre-processing Techniques

2018 ◽  
Vol 8 (3) ◽  
Author(s):  
Siti Fadzlun Md Salleh ◽  
Mohd Foad Rohani ◽  
Mohd Aizaini Maarof Maarof
Keyword(s):  
Time Complexity ◽  
Processing Technique ◽  
Experimental Result ◽  
Discrete Wavelet ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Complexity Issue ◽  
Processing Step ◽  
Copy Move Forgery Detection ◽  
Processing Techniques

Copy-move forgery detection (CMFD) has become a popular an important research focus in digital image forensic. Copy-move forgery happens when a region in an image is copied and paste into the same image. Apart from the main problem of detection robustness and accuracy, CMFD is struggle with time complexity issue. One of the options to resolve this problem was by including pre-processing step in CMFD pipeline. This paper reviews on the importance of pre-processing step, and available techniques in reducing time complexity of copy-move forgery detection. An experiment using discrete wavelet transform (DWT) as a pre-processing technique was carried out to evaluate the performance of adopting pre-processing technique in CMFD pipeline. The experimental result has shown a significant reduction in processing time with some trade off to detection accuracy.

A Method of Extracting Feature of Phonic Signal Based on the Matrix Analysis

2013 ◽  
Vol 457-458 ◽  
pp. 1200-1203
Author(s):  
Yang Xu ◽  
Fang Chao Hu
Keyword(s):  
Embedded System ◽  
Feature Vector ◽  
Recognition Rate ◽  
Audio Signal ◽  
Coefficient Matrix ◽  
Matrix Analysis ◽  
Discrete Wavelet ◽  
Feature Vectors ◽  
The Matrix ◽  
The Embedded System

In the speech recognition technology, feature extraction is essential for the system recognition rate, taking amount of strategies to find the better feature vectors are most researchers target. This paper presents a method of extracting feature of audio signal based on the discrete wavelet transform, then decomposed the coefficient matrix by the matrix analysis way, through this method to find a new thinking on the way of extracting feature vector. The method can be achieved in the procedure. The main purpose is to reduce the dimension of feature vector, make the vector briefer, and then reduce the computing complexity in the embedded system. This method can reduce the feature vectors dimension, accelerated the computing velocity.

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