scholarly journals Composition of Visual Feature Vector Pattern for Deep Learning in Image Forensics

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 188970-188980
Author(s):  
Kang Hyeon Rhee
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Image Forensics ◽  
Visual Feature

scholarly journals Classifying Informatory Tweets during Disaster Using Deep Learning

2020 ◽  
Vol 32 ◽  
pp. 03025
Author(s):  
Pradip Bhere ◽  
Anand Upadhyay ◽  
Ketan Chaudhari ◽  
Tushar Ghorpade
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Natural Disaster ◽  
Feature Vector ◽  
Sound Image ◽  
Life Saving ◽  
Disaster Data ◽  
Educational Information

Micro blogging platforms like Twitter generate a wealth of information during a disaster. Data can be in the form of sound, image, text, video etc. by way of tweets. Tweets produced during a disaster are not always educational. Information tweets can provide useful information about affected people, infrastructure damage, civilized organizations etc. Studies show that when it comes to sharing emergency information during a natural disaster, time is everything. Research on Twitter use during hurricanes, floods and floods provide potentially life-saving data on how information is disseminated in emergencies. The proposed system outlines how to distinguish sensitive and non-useful tweets during a disaster. The proposed method is based on the use of Word2Vec and the Convolutional Neural Network (CNN). Word2vec provides a feature vector and CNN is used to classify tweets.

scholarly journals Security Consideration for Deep Learning-Based Image Forensics

2018 ◽  
Vol E101.D (12) ◽  
pp. 3263-3266 ◽  
Author(s):  
Wei ZHAO ◽  
Pengpeng YANG ◽  
Rongrong NI ◽  
Yao ZHAO ◽  
Haorui WU
Keyword(s):  
Deep Learning ◽  
Image Forensics

scholarly journals Distance Transform-Based Spectral-Spatial Feature Vector for Hyperspectral Image Classification with Stacked Autoencoder

2021 ◽  
Vol 13 (9) ◽  
pp. 1732
Author(s):  
Hadis Madani ◽  
Kenneth McIsaac
Keyword(s):  
Deep Learning ◽  
Spatial Information ◽  
Hyperspectral Image ◽  
Feature Vector ◽  
Remote Sensing Data ◽  
Distance Transform ◽  
Hyperspectral Image Classification ◽  
Spatial Feature ◽  
Learning Techniques

Pixel-wise classification of hyperspectral images (HSIs) from remote sensing data is a common approach for extracting information about scenes. In recent years, approaches based on deep learning techniques have gained wide applicability. An HSI dataset can be viewed either as a collection of images, each one captured at a different wavelength, or as a collection of spectra, each one associated with a specific point (pixel). Enhanced classification accuracy is enabled if the spectral and spatial information are combined in the input vector. This allows simultaneous classification according to spectral type but also according to geometric relationships. In this study, we proposed a novel spatial feature vector which improves accuracies in pixel-wise classification. Our proposed feature vector is based on the distance transform of the pixels with respect to the dominant edges in the input HSI. In other words, we allow the location of pixels within geometric subdivisions of the dataset to modify the contribution of each pixel to the spatial feature vector. Moreover, we used the extended multi attribute profile (EMAP) features to add more geometric features to the proposed spatial feature vector. We have performed experiments with three hyperspectral datasets. In addition to the Salinas and University of Pavia datasets, which are commonly used in HSI research, we include samples from our Surrey BC dataset. Our proposed method results compares favorably to traditional algorithms as well as to some recently published deep learning-based algorithms.

scholarly journals A Comprehensive Review of Deep-Learning-Based Methods for Image Forensics

2021 ◽  
Vol 7 (4) ◽  
pp. 69
Author(s):  
Ivan Castillo Castillo Camacho ◽  
Kai Wang
Keyword(s):  
Deep Learning ◽  
Image Forensics ◽  
Research Community ◽  
Image Databases ◽  
Special Focus ◽  
Specialized Knowledge ◽  
Naked Eye ◽  
Comprehensive Literature Review ◽  
Camera Identification

Seeing is not believing anymore. Different techniques have brought to our fingertips the ability to modify an image. As the difficulty of using such techniques decreases, lowering the necessity of specialized knowledge has been the focus for companies who create and sell these tools. Furthermore, image forgeries are presently so realistic that it becomes difficult for the naked eye to differentiate between fake and real media. This can bring different problems, from misleading public opinion to the usage of doctored proof in court. For these reasons, it is important to have tools that can help us discern the truth. This paper presents a comprehensive literature review of the image forensics techniques with a special focus on deep-learning-based methods. In this review, we cover a broad range of image forensics problems including the detection of routine image manipulations, detection of intentional image falsifications, camera identification, classification of computer graphics images and detection of emerging Deepfake images. With this review it can be observed that even if image forgeries are becoming easy to create, there are several options to detect each kind of them. A review of different image databases and an overview of anti-forensic methods are also presented. Finally, we suggest some future working directions that the research community could consider to tackle in a more effective way the spread of doctored images.

scholarly journals Intelligent Fault Diagnosis of Gears Based on Deep Learning Feature Extraction and Particle Swarm Support Vector Machine State Recognition

2021 ◽  
pp. 26-40
Author(s):  
Ahmed N. Al Al-Masri ◽  
◽  
◽  
Hamam Mokayed
Keyword(s):  
Feature Extraction ◽  
Deep Learning ◽  
Fault Diagnosis ◽  
Time Domain ◽  
Feature Vector ◽  
Particle Swarm ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Spectral Features ◽  
State Recognition

Gear faults have always been a problem encountered in mechanical processing. For gear fault diagnosis, using mathematical-statistical feature extraction methods, deep learning neural networks (DLNN), particle swarm algorithm (PSA), and support vector machines (SVM), etc. According to the feature extraction of deep learning and particle swarm SVM state recognition, the intelligent diagnosis model is established, and the reliability of the model is verified by experiments. The model uses the combination of spectral features extracted by deep learning adaptively and the time domain features extracted by mathematical statistics methods to form a joint feature vector and then uses particle swarm SVM to diagnose the joint feature vector. After research, this paper draws a classification fitness curve combining the fault spectrum features extracted by DLNN and traditional time-domain statistical features. The classification result obtained by using this method is 95.3%. The reliability of the model is verified, and satisfactory diagnosis results are obtained. In addition, the application results also verify the effectiveness of adaptively extracting spectral features based on deep learning.

scholarly journals Deep Learning Algorithms for Detection and Classification of Gastrointestinal Diseases

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Mosleh Hmoud Al-Adhaileh ◽  
Ebrahim Mohammed Senan ◽  
Waselallah Alsaade ◽  
Theyazn H. H Aldhyani ◽  
Nizar Alsharif ◽  
...  
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Gastrointestinal Diseases ◽  
Activation Function ◽  
Learning Networks ◽  
Video Endoscopy ◽  
Deep Feature ◽  
Short Period

Currently, nearly two million patients die of gastrointestinal diseases worldwide. Video endoscopy is one of the latest technologies in the medical imaging field for the diagnosis of gastrointestinal diseases, such as stomach ulcers, bleeding, and polyps. Medical video endoscopy generates many images, so doctors need considerable time to follow up all the images. This creates a challenge for manual diagnosis and has encouraged investigations into computer-aided techniques to diagnose all the generated images in a short period and with high accuracy. The novelty of the proposed methodology lies in developing a system for diagnosis of gastrointestinal diseases. This paper introduces three networks, GoogleNet, ResNet-50, and AlexNet, which are based on deep learning and evaluates them for their potential in diagnosing a dataset of lower gastrointestinal diseases. All images are enhanced, and the noise is removed before they are inputted into the deep learning networks. The Kvasir dataset contains 5,000 images divided equally into five types of lower gastrointestinal diseases (dyed-lifted polyps, normal cecum, normal pylorus, polyps, and ulcerative colitis). In the classification stage, pretrained convolutional neural network (CNN) models are tuned by transferring learning to perform new tasks. The softmax activation function receives the deep feature vector and classifies the input images into five classes. All CNN models achieved superior results. AlexNet achieved an accuracy of 97%, sensitivity of 96.8%, specificity of 99.20%, and AUC of 99.98%.

scholarly journals Wireless Image Transmission Interference Signal Recognition System Based on Deep Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Zhijun Guo ◽  
Shuai Liu
Keyword(s):  
Deep Learning ◽  
Feature Vector ◽  
Recognition Performance ◽  
Image Transmission ◽  
Recognition System ◽  
Interference Signal ◽  
Signal Recognition ◽  
Interference Signals ◽  
The Time Domain ◽  
Wireless Image Transmission

In the process of wireless image transmission, there are a large number of interference signals, but the traditional interference signal recognition system is limited by various modulation modes, it is difficult to accurately identify the target signal, and the reliability of the system needs to be further improved. In order to solve this problem, a wireless image transmission interference signal recognition system based on deep learning is designed in this paper. In the hardware part, STM32F107VT and SI4463 are used to form a wireless controller to control the execution of each instruction. In the software part, aiming at the time-domain characteristics of the interference signal, the feature vector of the interference signal is extracted. With the support of GAP-CNN model, the interference signal is recognized through the training and learning of feature vector. The experimental results show that the packet loss rate of the designed system is less than 0.5%, the recognition performance is good, and the reliability of the system is improved.

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