scholarly journals Multiple Image Splicing Dataset (MISD): A Dataset for Multiple Splicing

Data ◽  
2021 ◽  
Vol 6 (10) ◽  
pp. 102
Author(s):  
Kalyani Dhananjay Kadam ◽  
Swati Ahirrao ◽  
Ketan Kotecha
Keyword(s):  
Research Work ◽  
Ground Truth ◽  
Easy Access ◽  
Image Editing ◽  
Image Splicing ◽  
Misleading Information ◽  
Detection Techniques ◽  
Multiple Image ◽  
Significant Area ◽  
Social Media Platforms

Image forgery has grown in popularity due to easy access to abundant image editing software. These forged images are so devious that it is impossible to predict with the naked eye. Such images are used to spread misleading information in society with the help of various social media platforms such as Facebook, Twitter, etc. Hence, there is an urgent need for effective forgery detection techniques. In order to validate the credibility of these techniques, publically available and more credible standard datasets are required. A few datasets are available for image splicing, such as Columbia, Carvalho, and CASIA V1.0. However, these datasets are employed for the detection of image splicing. There are also a few custom datasets available such as Modified CASIA, AbhAS, which are also employed for the detection of image splicing forgeries. A study of existing datasets used for the detection of image splicing reveals that they are limited to only image splicing and do not contain multiple spliced images. This research work presents a Multiple Image Splicing Dataset, which consists of a total of 300 multiple spliced images. We are the pioneer in developing the first publicly available Multiple Image Splicing Dataset containing high-quality, annotated, realistic multiple spliced images. In addition, we are providing a ground truth mask for these images. This dataset will open up opportunities for researchers working in this significant area.

scholarly journals Evaluating effects of focal length and viewing angle in a comparison of recent face landmark and alignment methods

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiang Li ◽  
Jianzheng Liu ◽  
Jessica Baron ◽  
Khoa Luu ◽  
Eric Patterson
Keyword(s):  
Focal Length ◽  
Ground Truth ◽  
Detection Methods ◽  
Viewing Angle ◽  
Deep Convolutional Neural Networks ◽  
Landmark Detection ◽  
High Performing ◽  
Detection Techniques ◽  
And Performance ◽  
Lens Focal Length

AbstractRecent attention to facial alignment and landmark detection methods, particularly with application of deep convolutional neural networks, have yielded notable improvements. Neither these neural-network nor more traditional methods, though, have been tested directly regarding performance differences due to camera-lens focal length nor camera viewing angle of subjects systematically across the viewing hemisphere. This work uses photo-realistic, synthesized facial images with varying parameters and corresponding ground-truth landmarks to enable comparison of alignment and landmark detection techniques relative to general performance, performance across focal length, and performance across viewing angle. Recently published high-performing methods along with traditional techniques are compared in regards to these aspects.

scholarly journals A Hybrid Deep Learning Framework for Unsupervised Anomaly Detection in Multivariate Spatio-Temporal Data

2020 ◽  
Vol 10 (15) ◽  
pp. 5191
Author(s):  
Yıldız Karadayı ◽  
Mehmet N. Aydin ◽  
A. Selçuk Öğrenci
Keyword(s):  
Deep Learning ◽  
Anomaly Detection ◽  
Hurricane Katrina ◽  
Time Series Data ◽  
Hybrid Approach ◽  
Ground Truth ◽  
Outbreak Detection ◽  
Series Data ◽  
Detection Techniques ◽  
Spatio Temporal

Multivariate time-series data with a contextual spatial attribute have extensive use for finding anomalous patterns in a wide variety of application domains such as earth science, hurricane tracking, fraud, and disease outbreak detection. In most settings, spatial context is often expressed in terms of ZIP code or region coordinates such as latitude and longitude. However, traditional anomaly detection techniques cannot handle more than one contextual attribute in a unified way. In this paper, a new hybrid approach based on deep learning is proposed to solve the anomaly detection problem in multivariate spatio-temporal dataset. It works under the assumption that no prior knowledge about the dataset and anomalies are available. The architecture of the proposed hybrid framework is based on an autoencoder scheme, and it is more efficient in extracting features from the spatio-temporal multivariate datasets compared to the traditional spatio-temporal anomaly detection techniques. We conducted extensive experiments using buoy data of 2005 from National Data Buoy Center and Hurricane Katrina as ground truth. Experiments demonstrate that the proposed model achieves more than 10% improvement in accuracy over the methods used in the comparison where our model jointly processes the spatial and temporal dimensions of the contextual data to extract features for anomaly detection.

scholarly journals Salient Object Detection Techniques in Computer Vision—A Survey

Entropy ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. 1174
Author(s):  
Ashish Kumar Gupta ◽  
Ayan Seal ◽  
Mukesh Prasad ◽  
Pritee Khanna
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Object Detection ◽  
Large Scale ◽  
Research Work ◽  
Salient Object Detection ◽  
Future Research ◽  
Automatic Identification ◽  
Salient Object ◽  
Detection Techniques

Detection and localization of regions of images that attract immediate human visual attention is currently an intensive area of research in computer vision. The capability of automatic identification and segmentation of such salient image regions has immediate consequences for applications in the field of computer vision, computer graphics, and multimedia. A large number of salient object detection (SOD) methods have been devised to effectively mimic the capability of the human visual system to detect the salient regions in images. These methods can be broadly categorized into two categories based on their feature engineering mechanism: conventional or deep learning-based. In this survey, most of the influential advances in image-based SOD from both conventional as well as deep learning-based categories have been reviewed in detail. Relevant saliency modeling trends with key issues, core techniques, and the scope for future research work have been discussed in the context of difficulties often faced in salient object detection. Results are presented for various challenging cases for some large-scale public datasets. Different metrics considered for assessment of the performance of state-of-the-art salient object detection models are also covered. Some future directions for SOD are presented towards end.

scholarly journals An Enhanced Hidden Semi-Markov model for Outlier Detection in Multivariate Datasets

2021 ◽  
Author(s):  
G Manoharan ◽  
K Sivakumar
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Outlier Detection ◽  
Hidden Markov ◽  
Research Work ◽  
Detection Accuracy ◽  
Detection Techniques ◽  
Detection Model ◽  
Huge Data ◽  
Proposed Model

Outlier detection in data mining is an important arena where detection models are developed to discover the objects that do not confirm the expected behavior. The generation of huge data in real time applications makes the outlier detection process into more crucial and challenging. Traditional detection techniques based on mean and covariance are not suitable to handle large amount of data and the results are affected by outliers. So it is essential to develop an efficient outlier detection model to detect outliers in the large dataset. The objective of this research work is to develop an efficient outlier detection model for multivariate data employing the enhanced Hidden Semi-Markov Model (HSMM). It is an extension of conventional Hidden Markov Model (HMM) where the proposed model allows arbitrary time distribution in its states to detect outliers. Experimental results demonstrate the better performance of proposed model in terms of detection accuracy, detection rate. Compared to conventional Hidden Markov Model based outlier detection the detection accuracy of proposed model is obtained as 98.62% which is significantly better for large multivariate datasets.

scholarly journals Sentiment Analysis on Twitter Data: A Comparative Approach

2021 ◽  
Vol 9 (10) ◽  
pp. 1-12
Author(s):  
Subhadip Chandra ◽  
Randrita Sarkar ◽  
Sayon Islam ◽  
Soham Nandi ◽  
Avishto Banerjee ◽  
...  
Keyword(s):  
Machine Learning ◽  
Sentiment Analysis ◽  
Computational Linguistics ◽  
Language Processing ◽  
Research Work ◽  
Comparative Approach ◽  
Support Vector ◽  
Affective States ◽  
Twitter Data ◽  
Social Media Platforms

Sentiment analysis is the methodical recognition, extraction, quantification, and learning of affective states and subjective information using natural language processing, text analysis, computational linguistics, and biometrics. People frequently use Twitter, one of numerous popular social media platforms, to convey their thoughts and opinions about a business, a product, or a service. Analysis of tweet sentiments is particularly useful in detecting if people have a good, negative, or neutral opinion. This study assesses public opinion about an individual, activity, commodity, or organization. The Twitter API is utilised in this article to directly get tweets from Twitter and develop a sentiment categorization for the tweets. This paper has used Twitter data for two separate approaches, viz., Lexicon & Machine Learning. Lexicon based approach further categorized in Corpus-based and Dictionary-based. And various Machine learning-based approaches like Support Vector Machine (SVM), Naïve Bayes, Maximum entropy are used to analyse Twitter data. Neural Network (NN), Decision tree-based sentiment analysis is also covered in this research work, to find out better accuracy of the approaches in the various data range. Graphs and confusion matrices are used to visualise the results of the analysis for positive, negative, and neutral remarks regarding their opinions.

scholarly journals Edge Detection Using Distinct Particle Swarm Optimization

2019 ◽  
Vol 8 (9) ◽  
pp. 1050-1057
Keyword(s):  
Particle Swarm Optimization ◽  
Edge Detection ◽  
Shape Matching ◽  
Particle Swarm ◽  
Ground Truth ◽  
Edge Image ◽  
Swarm Optimization ◽  
Detection Techniques ◽  
Edge Detectors ◽  
Distinct Particle

Edge detection is long-established in computer perception approach such as object detection, shape matching, medical image classification etc. For this reason many edge detectors like, Sobel, Robert, Prewitt, Canny etc. has been progressed to increase the effectiveness of the edge pixels. All these approaches work fine on images having minimum variation in intensity. Therefore, a new objective function based distinct particle swarm optimization (DPSO) is proposed in this paper to identify unbroken edges in an image. The conventional edge detectors such as “Canny” & computational intelligent techniques like ACO, GA and PSO are compared with proposed algorithm. Precision, Recall & F-Score is used as performance parameters for these edge detection techniques. The ground truth images are taken as reference edge images and all the edge images acquired by different edge detection systems are contrasted with reference edge image with ascertain the Precision, Recall and F-Score. The techniques are tested on 500 test images from the “BSD500” datasets. The empirical results presented by the proposed algorithm performance better than other edge detection techniques in the images. The proposed method observes edges more accurately and smoothly than other edge detection techniques such as “Canny, ACO, GA and PSO” in different images

scholarly journals A Review : Video Tampering Attacks and Detection Techniques

2019 ◽  
pp. 158-168
Author(s):  
Ruksana Habeeb ◽  
L. C. Manikandan
Keyword(s):  
Digital Video ◽  
Review Paper ◽  
Image Editing ◽  
Forgery Detection ◽  
Tampering Detection ◽  
Detection Techniques ◽  
Visual Evidence ◽  
Different Types ◽  
Video Forgery

Technological advancements of various video and image editing tools has reached such a level that the tampering of digital video or image can be performed easily without degrading their quality or leaving any visual evidence. This review paper presents an overview of various types of video forgery and the different types of techniques that are employed for its detection. Passive and active forgery detection techniques are commonly used methods for detecting the tampering in a digital video. Passive and active tampering detection techniques are utilized for detecting the integrity as well as the authenticity of a given video. The aim of this review is to provide some productive information about video tampering attacks for upcoming researchers.

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 IS FAKE NEWS SPREADING MORE RAPIDLY THAN COVID-19 IN INDIA?

2020 ◽  
Vol 11 (10) ◽  
pp. 208-220 ◽  
Keyword(s):  
Social Media ◽  
High Speed ◽  
Self Regulation ◽  
National Policy ◽  
Easy Access ◽  
Regulatory Body ◽  
Fake News ◽  
Red Tape ◽  
Social Media Platforms ◽  
The Government

Fake news is not a new phenomenon. With the mushrooming of smartphone users and the easy access to the internet, fake news is spreading at very high speed from people to people. Why do people flagrantly believe fake news? Why don‟t people fact-check before sharing information with others? etc., are still some of the questions unanswered. The panic created by fake news during the time of the Covid-19 pandemic outbreak is also not less. Hence, this study aims to focus on understanding people‟s perspectives on controlling the spread of fake news on social media. The respective study is based on quantitative data analysis of the responses of 300 social media users across India, collected online in April 2020 during a nationwide lockdown. The study arrived at a conclusion stating that self-regulation may not deliver a proper effect on controlling the spread of fake news. But, organising effective campaigns to build social media literacy can be one of the potential measures that can be implemented in order to control the spread of fake news over social media platforms. The respondents of the study also agree to bring-in a national policy and a national regulatory body control the spread of fake news over social media platforms. Hence, this particular study can support the Government of India to make decisions to roll out regulatory measures to control the spread of fake news through social media, which is still on the red tape.

Social Media Integration in Educational Administration as Information and Smart Systems

2020 ◽  
pp. 203-223
Author(s):  
Sadrag Panduleni Shihomeka ◽  
Helena N. Amadhila
Keyword(s):  
Social Media ◽  
Educational Administration ◽  
Political Development ◽  
Easy Access ◽  
Development Education ◽  
Smart Systems ◽  
Social Media Platforms ◽  
The Many ◽  
Education Administrators ◽  
Media Applications

The proliferation and access to social media platforms that allow easy access to information systems and services, content creation, and sharing, in a convenient form, has taken education administration and management by storm. Facebook is one of the many online media that can let education administrators and managers interact with each other or their subordinates by sharing information about themselves or any topical community issue via personal profiles or institutional profiles. Furthermore, it is noted that social media applications by their nature have the capabilities of educating, informing, entertaining (leisure), and socializing the audience. The research revealed that there are various groups on Facebook where youthful education administrators can use to post educational information and discuss pertinent issues concerning their institutions. Indeed, social media are being used as channels to foster economic, social, and political development education among Namibian educational administrators.

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