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.

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.

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

Real-time Mental Stress Detection Technique Using Neural Networks towards Wearable Health Monitor

2021 ◽  
Author(s):  
Nilava Mukherjee ◽  
Sumitra Mukhopadhyay ◽  
Rajarshi Gupta
Keyword(s):  
Feature Extraction ◽  
Real Time ◽  
Mental Stress ◽  
Detection Technique ◽  
Physiological Signals ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Optimum Number ◽  
Stress Detection ◽  
Detection Techniques

Abstract Motivation: In recent times, mental stress detection using physiological signals have received widespread attention from the technology research community. Although many motivating research works have already been reported in this area, the evidence of hardware implementation is occasional. The main challenge in stress detection research is using optimum number of physiological signals, and real-time detection with low complexity algorithm. Objective: In this work, a real-time stress detection technique is presented which utilises only photoplethysmogram (PPG) signal to achieve improved accuracy over multi-signal-based mental stress detection techniques. Methodology: A short segment of 5s PPG signal was used for feature extraction using an autoencoder (AE), and features were minimized using recursive feature elimination (RFE) integrated with a multi-class support vector machine (SVM) classifier. Results: The proposed AE-RFE-SVM based mental stress detection technique was tested with WeSAD dataset to detect four-levels of mental state, viz., baseline, amusement, meditation and stress and to achieve an overall accuracy, F1 score and sensitivity of 99%, 0.99 and 98% respectively for 5s PPG data. The technique provided improved performance over discrete wavelet transformation (DWT) based feature extraction followed by classification with either of the five types of classifiers, viz., SVM, random forest (RF), k-nearest neighbour (k-NN), linear regression (LR) and decision tree (DT). The technique was translated into a quad-core-based standalone hardware (1.2 GHz, and 1 GB RAM). The resultant hardware prototype achieves a low latency (~0.4 s) and low memory requirement (~1.7 MB). Conclusion: The present technique can be extended to develop remote healthcare system using wearable sensors.

scholarly journals Color Channel Characteristics (CCC) for Efficient Digital Image Forensics

2018 ◽  
Vol 8 (1) ◽  
pp. 2555-2561
Author(s):  
S. Gupta ◽  
N. Mohan
Keyword(s):  
Digital Image ◽  
Low Cost ◽  
Texture Features ◽  
Image Forensics ◽  
Digital Image Forensics ◽  
Support Vector ◽  
Image Splicing ◽  
Color Channel ◽  
Channel Characteristics ◽  
Edge Detectors

Digital image forgery has become extremely easy as low-cost image processing programs are readily available. Digital image forensics is a science of classifying images as authentic or manipulated. This paper aims at implementing a novel digital image forensics technique by exploiting an image’s Color Channel Characteristics (CCC). The CCCs considered are the noise and edge characteristics of the image. Averaging, median, Gaussian and Wiener filters along with Sobel, Canny, Prewitt and Laplacian of Gaussian (LoG) edge detectors are applied to get the noise and texture features. A complete, no reference, blind classifier for image tamper detection has been proposed and implemented. The proposed CCC classifier can detect copy-move as well as image splicing accurately with lower dimensionality. Support Vector Machine is used for classification of images as authentic or tampered. Experimental results have shown that the proposed technique outperforms the existing ones and may serve as a complete tool for digital image forensics.

scholarly journals Image Splicing-Based Forgery Detection Using Discrete Wavelet Transform and Edge Weighted Local Binary Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Muhammad Hameed Siddiqi ◽  
Khurshed Asghar ◽  
Umar Draz ◽  
Amjad Ali ◽  
Madallah Alruwaili ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Discrete Wavelet Transform ◽  
Digital Image ◽  
Digital Images ◽  
Local Binary Patterns ◽  
Discrete Wavelet ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Image Forgery Detection

With the advancement of the multimedia technology, the extensive accessibility of image editing applications makes it easier to tamper the contents of digital images. Furthermore, the distribution of digital images over the open channel using information and communication technology (ICT) makes it more vulnerable to forgery. The vulnerabilities in telecommunication infrastructure open the doors for intruders to introduce deceiving changes in image data, which is hard to detect. The forged images can create severe social and legal troubles if altered with malicious purpose. Image forgery detection necessitates the development of sophisticated techniques that can efficiently detect the alterations in the digital image. Splicing forgery is commonly used to conceal the reality in images. Splicing introduces high contrast in the corners, smooth regions, and edges. We proposed a novel image forgery detection technique based on image splicing using Discrete Wavelet Transform and histograms of discriminative robust local binary patterns. First, a given color image is transformed in YCbCr color space and then Discrete Wavelet Transform (DWT) is applied on Cb and Cr components of the digital image. Texture variation in each subband of DWT is described using the dominant rotated local binary patterns (DRLBP). The DRLBP from each subband are concatenated to produce the final feature vector. Finally, a support vector machine is used to develop image forgery detection model. The performance and generalization of the proposed technique were evaluated on publicly available benchmark datasets. The proposed technique outperformed the state-of-the-art forgery detection techniques with 98.95% detection accuracy.

Visual Saliency and Extended Hidden Markov Model Based Approach for Image Splicing Detection

2013 ◽  
Vol 385-386 ◽  
pp. 1466-1469
Author(s):  
Xiang Li ◽  
Xuan Jing Shen ◽  
Ying Da Lv ◽  
Hai Peng Chen
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Hidden Markov ◽  
Visual Saliency ◽  
Support Vector ◽  
Detection Accuracy ◽  
Blind Detection ◽  
Image Splicing ◽  
Image Splicing Detection ◽  
Splicing Detection

In order to improve the detection accuracy of spliced images, a new blind detection based on visual saliency was proposed in this paper. Firstly, create the edge conspicuous map by an improved OSF-based method, and extract fixations by visual attention model. Then locate those fixations on conspicuous edges by conspicuous edge positioning method. Accordingly, key feature fragments can be captured. Secondly, extract Extended Hidden Markov Model features, and reduce their dimension by SVM-RFE. Finally, support vector machine was exploited to classify the authentic and spliced images. The experimental results showed that, when testing on the Columbia image splicing detection dataset, the detection accuracy of the proposed method was 96.68%.

scholarly journals Digital Images Authentication Technique Based on DWT, DCT and Local Binary Patterns

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3372 ◽  
Author(s):  
Esteban Armas Vega ◽  
Ana Sandoval Orozco ◽  
Luis García Villalba ◽  
Julio Hernandez-Castro
Keyword(s):  
Digital Image ◽  
Digital Images ◽  
Modern Society ◽  
Image Authentication ◽  
Local Binary Patterns ◽  
Image Editing ◽  
Discrete Wavelet ◽  
Local Texture ◽  
The World ◽  
Authentication Technique

In the last few years, the world has witnessed a ground-breaking growth in the use of digital images and their applications in the modern society. In addition, image editing applications have downplayed the modification of digital photos and this compromises the authenticity and veracity of a digital image. These applications allow for tampering the content of the image without leaving visible traces. In addition to this, the easiness of distributing information through the Internet has caused society to accept everything it sees as true without questioning its integrity. This paper proposes a digital image authentication technique that combines the analysis of local texture patterns with the discrete wavelet transform and the discrete cosine transform to extract features from each of the blocks of an image. Subsequently, it uses a vector support machine to create a model that allows verification of the authenticity of the image. Experiments were performed with falsified images from public databases widely used in the literature that demonstrate the efficiency of the proposed method.

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.

Texture Classification Research Based on the Logarithmic Polar Coordinates Transform and Support Vector Machine

2010 ◽  
Vol 39 ◽  
pp. 226-231
Author(s):  
De Cheng Gao ◽  
Bin Liu ◽  
Jian Guo Wang
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector Machines ◽  
Texture Classification ◽  
Texture Feature ◽  
Polar Coordinates ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Translation Invariant ◽  
Vector Machines

In this paper, we have investigated an approach based on log-polar transform and support vector machine (SVM) for texture classification. Firstly, we convert rotation into translation, which reduced the effect of rotation and scale changes using the characteristics of logarithm Polar transformation. Then we extract texture feature after eliminating translation using smooth discrete wavelet transform (SWT) with translation invariant. Finally, support vector machines(SVM) are adopted to the texture classification. The experiment results show the proposed approach can get improved results for texture classification.

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