scholarly journals Overcome the Brightness and Jitter Noises in Video Inter-Frame Tampering Detection

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 3953
Author(s):  
Han Pu ◽  
Tianqiang Huang ◽  
Bin Weng ◽  
Feng Ye ◽  
Chenbin Zhao
Keyword(s):  
Detection Method ◽  
Real Life ◽  
Vital Role ◽  
Video Forensics ◽  
Flow Algorithm ◽  
Benchmark Datasets ◽  
Media Reports ◽  
Intensity Normalization ◽  
Inter Frame ◽  
Stable Feature

Digital video forensics plays a vital role in judicial forensics, media reports, e-commerce, finance, and public security. Although many methods have been developed, there is currently no efficient solution to real-life videos with illumination noises and jitter noises. To solve this issue, we propose a detection method that adapts to brightness and jitter for video inter-frame forgery. For videos with severe brightness changes, we relax the brightness constancy constraint and adopt intensity normalization to propose a new optical flow algorithm. For videos with large jitter noises, we introduce motion entropy to detect the jitter and extract the stable feature of texture changes fraction for double-checking. Experimental results show that, compared with previous algorithms, the proposed method is more accurate and robust for videos with significant brightness variance or videos with heavy jitter on public benchmark datasets.

Deep Learning based Thermal Crack Detection on Structural Concrete Exposed to Elevated Temperature

2021 ◽  
pp. 136943322098663
Author(s):  
Diana Andrushia A ◽  
Anand N ◽  
Eva Lubloy ◽  
Prince Arulraj G
Keyword(s):  
Deep Learning ◽  
Crack Detection ◽  
Detection Method ◽  
Concrete Structures ◽  
Vital Role ◽  
Subjective Knowledge ◽  
Thermal Cracks ◽  
Reinforced Cement ◽  
Novel Method ◽  
Human Inspection

Health monitoring of concrete including, detecting defects such as cracking, spalling on fire affected concrete structures plays a vital role in the maintenance of reinforced cement concrete structures. However, this process mostly uses human inspection and relies on subjective knowledge of the inspectors. To overcome this limitation, a deep learning based automatic crack detection method is proposed. Deep learning is a vibrant strategy under computer vision field. The proposed method consists of U-Net architecture with an encoder and decoder framework. It performs pixel wise classification to detect the thermal cracks accurately. Binary Cross Entropy (BCA) based loss function is selected as the evaluation function. Trained U-Net is capable of detecting major thermal cracks and minor thermal cracks under various heating durations. The proposed, U-Net crack detection is a novel method which can be used to detect the thermal cracks developed on fire exposed concrete structures. The proposed method is compared with the other state-of-the-art methods and found to be accurate with 78.12% Intersection over Union (IoU).

Towards Faster Mining of Disjunction-Based Concise Representations of Frequent Patterns

2014 ◽  
Vol 23 (02) ◽  
pp. 1450001
Author(s):  
T. Hamrouni ◽  
S. Ben Yahia ◽  
E. Mephu Nguifo
Keyword(s):  
Empirical Study ◽  
Real Life ◽  
Search Space ◽  
Frequent Patterns ◽  
Memory Consumption ◽  
Efficient Tool ◽  
Condensed Representation ◽  
Benchmark Datasets ◽  
Condensed Representations ◽  
Amount Of Knowledge

In many real-life datasets, the number of extracted frequent patterns was shown to be huge, hampering the effective exploitation of such amount of knowledge by human experts. To overcome this limitation, exact condensed representations were introduced in order to offer a small-sized set of elements from which the faithful retrieval of all frequent patterns is possible. In this paper, we introduce a new exact condensed representation only based on particular elements from the disjunctive search space. In this space, a pattern is characterized by its disjunctive support, i.e., the frequency of complementary occurrences – instead of the ubiquitous co-occurrence link – of its items. For several benchmark datasets, this representation has been shown interesting in compactness terms compared to the pioneering approaches of the literature. In this respect, we mainly focus here on proposing an efficient tool for mining this representation. For this purpose, we introduce an algorithm, called DSSRM, dedicated to this task. We also propose several techniques to optimize its mining time as well as its memory consumption. The carried out empirical study on benchmark datasets shows that DSSRM is faster by several orders of magnitude than the MEP algorithm.

scholarly journals Facial Expression Recognition using SVM with CNN and Handcrafted Features

2019 ◽  
Vol 8 (4) ◽  
pp. 3570-3574
Keyword(s):  
Facial Expression ◽  
Facial Expression Recognition ◽  
Recognition System ◽  
Vital Role ◽  
Working Environment ◽  
Support Vector ◽  
Expression Recognition ◽  
Shopping Malls ◽  
Benchmark Datasets ◽  
Mind Set

The facial expression recognition system is playing vital role in many organizations, institutes, shopping malls to know about their stakeholders’ need and mind set. It comes under the broad category of computer vision. Facial expression can easily explain the true intention of a person without any kind of conversation. The main objective of this work is to improve the performance of facial expression recognition in the benchmark datasets like CK+, JAFFE. In order to achieve the needed accuracy metrics, the convolution neural network was constructed to extract the facial expression features automatically and combined with the handcrafted features extracted using Histogram of Gradients (HoG) and Local Binary Pattern (LBP) methods. Linear Support Vector Machine (SVM) is built to predict the emotions using the combined features. The proposed method produces promising results as compared to the recent work in [1].This is mainly needed in the working environment, shopping malls and other public places to effectively understand the likeliness of the stakeholders at that moment.

A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

2018 ◽  
pp. 1307-1321
Author(s):  
Vinh-Tiep Nguyen ◽  
Thanh Duc Ngo ◽  
Minh-Triet Tran ◽  
Duy-Dinh Le ◽  
Duc Anh Duong
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Spatial Information ◽  
Real Life ◽  
Inverted Index ◽  
Bag Of Words ◽  
Visual Words ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

A Combination of Spatial Pyramid and Inverted Index for Large-Scale Image Retrieval

2015 ◽  
Vol 6 (2) ◽  
pp. 37-51 ◽  
Author(s):  
Vinh-Tiep Nguyen ◽  
Thanh Duc Ngo ◽  
Minh-Triet Tran ◽  
Duy-Dinh Le ◽  
Duc Anh Duong
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Spatial Information ◽  
Real Life ◽  
Inverted Index ◽  
Bag Of Words ◽  
Visual Words ◽  
Benchmark Datasets ◽  
Large Scale Image Retrieval ◽  
Inverted Indexing

Large-scale image retrieval has been shown remarkable potential in real-life applications. The standard approach is based on Inverted Indexing, given images are represented using Bag-of-Words model. However, one major limitation of both Inverted Index and Bag-of-Words presentation is that they ignore spatial information of visual words in image presentation and comparison. As a result, retrieval accuracy is decreased. In this paper, the authors investigate an approach to integrate spatial information into Inverted Index to improve accuracy while maintaining short retrieval time. Experiments conducted on several benchmark datasets (Oxford Building 5K, Oxford Building 5K+100K and Paris 6K) demonstrate the effectiveness of our proposed approach.

scholarly journals Inferring Emotion Tags from Object Images Using Convolutional Neural Network

2020 ◽  
Vol 10 (15) ◽  
pp. 5333
Author(s):  
Anam Manzoor ◽  
Waqar Ahmad ◽  
Muhammad Ehatisham-ul-Haq ◽  
Abdul Hannan ◽  
Muhammad Asif Khan ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Human Behavior ◽  
Real Life ◽  
Vital Role ◽  
Object Categories ◽  
Different Types ◽  
Gender Based ◽  
High Level ◽  
Human Behavior Analysis

Emotions are a fundamental part of human behavior and can be stimulated in numerous ways. In real-life, we come across different types of objects such as cake, crab, television, trees, etc., in our routine life, which may excite certain emotions. Likewise, object images that we see and share on different platforms are also capable of expressing or inducing human emotions. Inferring emotion tags from these object images has great significance as it can play a vital role in recommendation systems, image retrieval, human behavior analysis and, advertisement applications. The existing schemes for emotion tag perception are based on the visual features, like color and texture of an image, which are poorly affected by lightning conditions. The main objective of our proposed study is to address this problem by introducing a novel idea of inferring emotion tags from the images based on object-related features. In this aspect, we first created an emotion-tagged dataset from the publicly available object detection dataset (i.e., “Caltech-256”) using subject evaluation from 212 users. Next, we used a convolutional neural network-based model to automatically extract the high-level features from object images for recognizing nine (09) emotion categories, such as amusement, awe, anger, boredom, contentment, disgust, excitement, fear, and sadness. Experimental results on our emotion-tagged dataset endorse the success of our proposed idea in terms of accuracy, precision, recall, specificity, and F1-score. Overall, the proposed scheme achieved an accuracy rate of approximately 85% and 79% using top-level and bottom-level emotion tagging, respectively. We also performed a gender-based analysis for inferring emotion tags and observed that male and female subjects have discernment in emotions perception concerning different object categories.

scholarly journals Framing the Real: Lefèbvre and Neo-Realist Cinematic Space as Practice

2014 ◽  
Author(s):  
David Brancaleone
Keyword(s):  
Film Theory ◽  
Real Life ◽  
Vital Role ◽  
Theory And Practice ◽  
Multidimensional Space ◽  
Spatial Practices ◽  
The Real ◽  
Cinematic Space ◽  
The Difference ◽  
The One

In 1945 Roberto Rossellini’s Neo-realist Rome, Open City set in motion an approach to cinema and its representation of real life – and by extension real spaces – that was to have international significance in film theory and practice. However, the re-use of the real spaces of the city, and elsewhere, as film sets in Neo-realist film offered (and offers) more than an influential aesthetic and set of cinematic theories. Through Neo-realism, it can be argued that we gain access to a cinematic relational and multidimensional space that is not made from built sets, but by filming the built environment. On the one hand, this space allows us to “notice” the contradictions around us in our cities and, by extension, the societies that have produced those cities, while on the other, allows us to see the spatial practices operative in the production and maintenance of those contradictions. In setting out a template for understanding the spatial practices of Neo-realism through the work of Henri Lefèbvre, this paper opens its films, and those produced today in its wake, to a spatio-political reading of contemporary relevance. We will suggest that the rupturing of divisions between real spaces and the spaces of film locations, as well the blurring of the difference between real life and performed actions for the camera that underlies much of the central importance of Neo-realism, echoes the arguments of Lefèbvre with regard the social production of space. In doing so, we will suggest that film potentially had, and still has, a vital role to play in a critique of contemporary capitalist spatial practices.

scholarly journals Content Characteristics Based Robust Watermarking for Relational Database: A New Approach to Database Security

2018 ◽  
Vol 7 (1.9) ◽  
pp. 302
Author(s):  
Manoj Kumar ◽  
O P. Verma
Keyword(s):  
Relational Database ◽  
Relational Databases ◽  
Image Watermarking ◽  
Real Life ◽  
Database Security ◽  
Vital Role ◽  
Digital Data ◽  
Secret Key ◽  
Areas Of Interest ◽  
Audio Video

Digital data such as text, relational database, audio, video and software are intellectual property of creators/ writers/owners. The database services have become easily available and economical since the booming of internet. However, their outsourcing through the internet accompanies multiple threats like copying, modifying as well as unauthorized distribution. Relational Database has a wide-spread use in many real-life applications, hence, it is essential to maintain integrity and provide copyright protection. To counter the threats, watermarking techniques have been playing a vital role since the last decade. As a security measure, Relational Database Watermarking is becoming more popular and strengthened day-by-day. This is also one of the upcoming areas of interest among researchers of the Database Security. A technique earlier used for Image Watermarking is applied to watermark Relational Databases. In Image Watermarking technique, a pixel or a pair of pixels must satisfy certain characteristics. Usually, database watermarking techniques concentrate on hiding a watermark in database. Extraction and matching of hidden watermark with original watermark confirms ownership of database. This paper demonstrates the use of image watermarking technique for relational databases. Here we align some properties of attributes of database by changing some bit(s) in attribute value. Using secret key, we have ensured that values of two attributes of a tuples satisfy some bit-similarity property and to do so, we slightly alter values of attributes. Detection of such characteristic in a database using secret key can be done easily to verify the presence of a watermark.  

scholarly journals Federated Learning on Clinical Benchmark Data: Performance Assessment

10.2196/20891 ◽  
2020 ◽  
Vol 22 (10) ◽  
pp. e20891
Author(s):  
Geun Hyeong Lee ◽  
Soo-Yong Shin
Keyword(s):  
Hospital Mortality ◽  
High Performance ◽  
Medical Information ◽  
Characteristic Curve ◽  
Real Life ◽  
Personal Privacy ◽  
Comparative Performance ◽  
Client Server ◽  
Benchmark Datasets ◽  
Mimic Iii

Background Federated learning (FL) is a newly proposed machine-learning method that uses a decentralized dataset. Since data transfer is not necessary for the learning process in FL, there is a significant advantage in protecting personal privacy. Therefore, many studies are being actively conducted in the applications of FL for diverse areas. Objective The aim of this study was to evaluate the reliability and performance of FL using three benchmark datasets, including a clinical benchmark dataset. Methods To evaluate FL in a realistic setting, we implemented FL using a client-server architecture with Python. The implemented client-server version of the FL software was deployed to Amazon Web Services. Modified National Institute of Standards and Technology (MNIST), Medical Information Mart for Intensive Care-III (MIMIC-III), and electrocardiogram (ECG) datasets were used to evaluate the performance of FL. To test FL in a realistic setting, the MNIST dataset was split into 10 different clients, with one digit for each client. In addition, we conducted four different experiments according to basic, imbalanced, skewed, and a combination of imbalanced and skewed data distributions. We also compared the performance of FL to that of the state-of-the-art method with respect to in-hospital mortality using the MIMIC-III dataset. Likewise, we conducted experiments comparing basic and imbalanced data distributions using MIMIC-III and ECG data. Results FL on the basic MNIST dataset with 10 clients achieved an area under the receiver operating characteristic curve (AUROC) of 0.997 and an F1-score of 0.946. The experiment with the imbalanced MNIST dataset achieved an AUROC of 0.995 and an F1-score of 0.921. The experiment with the skewed MNIST dataset achieved an AUROC of 0.992 and an F1-score of 0.905. Finally, the combined imbalanced and skewed experiment achieved an AUROC of 0.990 and an F1-score of 0.891. The basic FL on in-hospital mortality using MIMIC-III data achieved an AUROC of 0.850 and an F1-score of 0.944, while the experiment with the imbalanced MIMIC-III dataset achieved an AUROC of 0.850 and an F1-score of 0.943. For ECG classification, the basic FL achieved an AUROC of 0.938 and an F1-score of 0.807, and the imbalanced ECG dataset achieved an AUROC of 0.943 and an F1-score of 0.807. Conclusions FL demonstrated comparative performance on different benchmark datasets. In addition, FL demonstrated reliable performance in cases where the distribution was imbalanced, skewed, and extreme, reflecting the real-life scenario in which data distributions from various hospitals are different. FL can achieve high performance while maintaining privacy protection because there is no requirement to centralize the data.

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