scholarly journals A Robust Forgery Detection Method for Copy–Move and Splicing Attacks in Images

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1500
Author(s):  
Mohammad Manzurul Islam ◽  
Gour Karmakar ◽  
Joarder Kamruzzaman ◽  
Manzur Murshed
Keyword(s):  
Model Building ◽  
Performance Metrics ◽  
Detection Method ◽  
Color Image ◽  
Computational Time ◽  
Support Vector ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Forgery ◽  
The Mean

Internet of Things (IoT) image sensors, social media, and smartphones generate huge volumes of digital images every day. Easy availability and usability of photo editing tools have made forgery attacks, primarily splicing and copy–move attacks, effortless, causing cybercrimes to be on the rise. While several models have been proposed in the literature for detecting these attacks, the robustness of those models has not been investigated when (i) a low number of tampered images are available for model building or (ii) images from IoT sensors are distorted due to image rotation or scaling caused by unwanted or unexpected changes in sensors’ physical set-up. Moreover, further improvement in detection accuracy is needed for real-word security management systems. To address these limitations, in this paper, an innovative image forgery detection method has been proposed based on Discrete Cosine Transformation (DCT) and Local Binary Pattern (LBP) and a new feature extraction method using the mean operator. First, images are divided into non-overlapping fixed size blocks and 2D block DCT is applied to capture changes due to image forgery. Then LBP is applied to the magnitude of the DCT array to enhance forgery artifacts. Finally, the mean value of a particular cell across all LBP blocks is computed, which yields a fixed number of features and presents a more computationally efficient method. Using Support Vector Machine (SVM), the proposed method has been extensively tested on four well known publicly available gray scale and color image forgery datasets, and additionally on an IoT based image forgery dataset that we built. Experimental results reveal the superiority of our proposed method over recent state-of-the-art methods in terms of widely used performance metrics and computational time and demonstrate robustness against low availability of forged training samples.

scholarly journals Examination of Abnormal Behavior Detection Based on Improved YOLOv3

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 197
Author(s):  
Meng-ting Fang ◽  
Zhong-ju Chen ◽  
Krzysztof Przystupa ◽  
Tao Li ◽  
Michal Majka ◽  
...  
Keyword(s):  
Detection Method ◽  
Abnormal Behavior ◽  
Detection Accuracy ◽  
Average Precision ◽  
Research Results ◽  
Behavior Detection ◽  
Abnormal Behavior Detection ◽  
The Mean ◽  
Examination Room ◽  
Detection Speed

Examination is a way to select talents, and a perfect invigilation strategy can improve the fairness of the examination. To realize the automatic detection of abnormal behavior in the examination room, the method based on the improved YOLOv3 (The third version of the You Only Look Once algorithm) algorithm is proposed. The YOLOv3 algorithm is improved by using the K-Means algorithm, GIoUloss, focal loss, and Darknet32. In addition, the frame-alternate dual-thread method is used to optimize the detection process. The research results show that the improved YOLOv3 algorithm can improve both the detection accuracy and detection speed. The frame-alternate dual-thread method can greatly increase the detection speed. The mean Average Precision (mAP) of the improved YOLOv3 algorithm on the test set reached 88.53%, and the detection speed reached 42 Frames Per Second (FPS) in the frame-alternate dual-thread detection method. The research results provide a certain reference for automated invigilation.

scholarly journals A Novel Approach for Efficient Forgery Image Detection Using Hybrid Feature Extraction and Classification

2018 ◽  
Vol 7 (3.27) ◽  
pp. 215
Author(s):  
G Clara Shanthi ◽  
V Cyril Raj
Keyword(s):  
Feature Extraction ◽  
Support Vector ◽  
Forgery Detection ◽  
Image Splicing ◽  
Image Forgery ◽  
Second Stage ◽  
Image Forgery Detection ◽  
Novel Approach ◽  
Different Types ◽  
Hybrid Feature Extraction

Image forgery detection is developing as one of the major research topic among researchers in the area of image forensics. These image forgery detection is addressed by two different types: (i) Active, (ii) Passive. Further consist of some different methods, such as Copy-Move, Image Splicing, and Retouching. Development of the image forgery is very necessary to detect as the image is true or it is forgery. In this paper, an efficient forgery detection and classification technique is proposed by three different stages. At first stage, preprocessing is carried out using bilateral filtering to remove noise. At second stage, extract unique features from forged image by using efficient feature extraction technique namely Gray Level Co-occurance Matrices (GLCM). Here, the GLCM improves the feature extraction accuracy. Finally, forged image is detected by classifying the type of image forgery using Multi Class- Support Vector Machine (SVM). Also, the performance of the proposed method is analyzed using the following metrics: accuracy, sensitivity and specificity.  

scholarly journals Fuzzy integral-based multi-sensor fusion for arc detection in the pantograph-catenary system

2016 ◽  
Vol 232 (1) ◽  
pp. 159-170 ◽  
Author(s):  
Ilhan Aydin ◽  
Selahattin B Celebi ◽  
Sami Barmada ◽  
Mauro Tucci
Keyword(s):  
Detection Method ◽  
Electrical Power ◽  
Classifier Fusion ◽  
Support Vector ◽  
Fuzzy Integral ◽  
Detection Accuracy ◽  
Correct Operation ◽  
Railway Traffic ◽  
Individual Classifier ◽  
The Individual

The pantograph-catenary subsystem is a fundamental component of a railway train since it provides the traction electrical power. A bad operating condition or, even worse, a failure can disrupt the railway traffic creating economic damages and, in some cases, serious accidents. Therefore, the correct operation of such subsystems should be ensured in order to have an economically efficient, reliable and safe transportation system. In this study, a new arc detection method was proposed and is based on features from the current and voltage signals collected by the pantograph. A tool named mathematical morphology is applied to voltage and current signals to emphasize the effect of the arc, before applying the fast Fourier transform to obtain the power spectrum. Afterwards, three support vector machine-based classifiers are trained separately to detect the arcs, and a fuzzy integral technique is used to synthesize the results obtained by the individual classifiers, therefore implementing a classifier fusion technique. The experimental results show that the proposed approach is effective for the detection of arcs, and the fusion of classifier has a higher detection accuracy than any individual classifier.

scholarly journals An Improved Discrete Cosine Transformation Block Based Scheme for Copy-move Image Forgery Detection

2019 ◽  
Vol 9 (2) ◽  
Author(s):  
Abdulraheem Hassanat Oyiza ◽  
Mohd Aizaini Maarof
Keyword(s):  
Detection Algorithm ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Image Block ◽  
Discrete Cosine Transformation ◽  
Image Forgery ◽  
Robust Detection ◽  
Image Forgery Detection ◽  
Dct Coefficients ◽  
Block Based

Copy-moved forgery is a common method to manipulate images. Several attempts of image forgery have been discovered and involves a region been duplicated and copied and pasted on another region of the same image in other to achieve selfish gain. Generally, there are two classification of copy-move forgery detection technique such as the block-based and key point-based. The block-based division is mostly used and divides image into blocks during the stage of image pre-processing before features are extracted, whereas key-point based technique skips the division of image into blocks and directly extracts different local feature from the image. In this paper, we review various block based and key point approach which has been proposed by various researchers. There is a problem of achieving a balance between improving the detection accuracy and having minimal computational complexity. The proposed technique is based on an improved DCT based copy-move image forgery detection (IDB-CFD), which involves using an octagonal block to reduce the number of features for matching, thereby improving detection accuracy while having minimal complexity. The analysis of this work as compared to previous proposed works which is based on a robust detection algorithm for copy-move image forgery (RDA-CF) and involves using circle block to reduce the number of features, results show that previous work represents about 79% of the quantized DCT coefficients on each image block and this proposed work represents about 85% of quantized DCT coefficients, therefore, recovery of about 6% more features using the IDB-CFD technique was observed as the improvement over the previously proposed RDA-CF.

Eye Detection Using Color, Haar Features, and Efficient Support Vector Machine

2012 ◽  
pp. 286-309
Author(s):  
Shuo Chen ◽  
Chengjun Liu
Keyword(s):  
Support Vector Machine ◽  
Face Recognition ◽  
Detection Method ◽  
Support Vector ◽  
Grand Challenge ◽  
Detection Accuracy ◽  
Eye Detection ◽  
Color Information ◽  
Haar Features ◽  
Improved Performance

Eye detection is an important initial step in an automatic face recognition system. Though numerous eye detection methods have been proposed, many problems still exist, especially in the detection accuracy and efficiency under challenging image conditions. The authors present a novel eye detection method using color information, Haar features, and a new efficient Support Vector Machine (eSVM) in this chapter . In particular, this eye detection method consists of two stages: the eye candidate selection and validation. The selection stage picks up eye candidates over an image through color information, while the validation stage applies 2D Haar wavelet and the eSVM to detect the center of the eye among these candidates. The eSVM is defined on fewer support vectors than the standard SVM, which can achieve faster detection speed and higher or comparable detection accuracy. Experiments on Face Recognition Grand Challenge (FRGC) database show the improved performance over existing methods on both efficiency and accuracy.

Research on the Detection Method of the Malicious Attacks on Campus Network

2014 ◽  
Vol 644-650 ◽  
pp. 3291-3294
Author(s):  
Jing Lei Wang
Keyword(s):  
Support Vector Machine ◽  
Detection Method ◽  
Attack Detection ◽  
Support Vector ◽  
Detection Accuracy ◽  
Campus Network ◽  
Malicious Attacks ◽  
Accuracy Rate ◽  
Malicious Attack ◽  
Conventional Manner

The problem of malicious attacks detection on campus network is studied to improve the accuracy of detection. When detecting malicious attacks on campus network, a conventional manner is usually conducted in malicious attack detection of campus network. If a malicious signature is mutated into a new feature, the conventional detection method cannot recognize the new malicious signature, resulting in a relative low detection accuracy rate of malicious attacks. To avoid these problems, in this paper, the malicious attacks detection method for campus network based on support vector machine algorithm is proposed. The plane of support vector machine classification is constructed, to complete the malicious attacks detection of campus network. Experiments show that this approach can improve the accuracy rate of the malicious attack detection, and achieve satisfactory results.

scholarly journals Image Forgery Detection and Localization via a Reliability Fusion Map

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6668
Author(s):  
Hongwei Yao ◽  
Ming Xu ◽  
Tong Qiao ◽  
Yiming Wu ◽  
Ning Zheng
Keyword(s):  
Data Driven ◽  
Detection Accuracy ◽  
Forgery Detection ◽  
Multimedia Forensics ◽  
Pass Filter ◽  
Image Forgery ◽  
Use Of Data ◽  
Image Forgery Detection ◽  
Detection And Localization ◽  
High Pass

Moving away from hand-crafted feature extraction, the use of data-driven convolution neural network (CNN)-based algorithms facilitates the realization of end-to-end automated forgery detection in multimedia forensics. On the basis of fingerprints acquired by images from different camera models, the goal of this paper is to design an effective detector capable of completing image forgery detection and localization. Specifically, relying on the designed constant high-pass filter, we first establish a well-performing CNN architecture to adaptively and automatically extract characteristics, and design a reliability fusion map (RFM) to improve localization resolution, and tamper detection accuracy. The extensive results from our empirical experiments demonstrate the effectiveness of our proposed RFM-based detector, and its better performance than other competing approaches.

Sign in / Sign up

Export Citation Format

Share Document