scholarly journals Methods of Detecting Image forgery using convolutional neural network

2021 ◽  
Vol 1831 (1) ◽  
pp. 012026
Author(s):  
Alka Leekha ◽  
Arpan Gupta ◽  
Amit Kumar ◽  
Tarun Chaudhary
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Forgery

Convolutional Neural Network Considering the Effects of Noise for Bearing Fault Diagnosis

2020 ◽  
Author(s):  
Ilyoung Han ◽  
Jangbom Chai ◽  
Chanwoo Lim ◽  
Taeyun Kim
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
High Accuracy ◽  
Training Data ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
System Noise ◽  
Novel Method ◽  
Maintenance Activities

Abstract Convolutional Neural Network (CNN) is, in general, good at finding principal components of data. However, the characteristic components of the signals could often be obscured by system noise. Therefore, even though the CNN model is well-trained and predict with high accuracy, it may detect only the primary patterns of data which could be formed by system noise. They are, in fact, highly vulnerable to maintenance activities such as reassembly. In other words, CNN models could misdiagnose even with excellent performances. In this study, a novel method that combines the classification using CNN with the data preprocessing is proposed for bearing fault diagnosis. The proposed method is demonstrated by the following steps. First, training data is preprocessed so that the noise and the fault signature of the bearings are separated. Then, CNN models are developed and trained to learn significant features containing information of defects. Lastly, the CNN models are examined and validated whether they learn and extract the meaningful features or not.

scholarly journals A Deep Convolutional Neural Network Model for Intelligent Discrimination between Coal and Rocks in Coal Mining Face

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Lei Si ◽  
Xiangxiang Xiong ◽  
Zhongbin Wang ◽  
Chao Tan
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Data Augmentation ◽  
Deep Convolutional Neural Network ◽  
Accurate Identification ◽  
Experiment System ◽  
Speed Up ◽  
Novel Method ◽  
Coal Rock ◽  
Mining Face

Accurate identification of the distribution of coal seam is a prerequisite for realizing intelligent mining of shearer. This paper presents a novel method for identifying coal and rock based on a deep convolutional neural network (CNN). Three regularization methods are introduced in this paper to solve the overfitting problem of CNN and speed up the convergence: dropout, weight regularization, and batch normalization. Then the coal-rock image information is enriched by means of data augmentation, which significantly improves the performance. The shearer cutting coal-rock experiment system is designed to collect more real coal-rock images, and some experiments are provided. The experiment results indicate that the network we designed has better performance in identifying the coal-rock images.

scholarly journals Visual Interpretation of Convolutional Neural Network Predictions in Classifying Medical Image Modalities

Diagnostics ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 38 ◽  
Author(s):  
Incheol Kim ◽  
Sivaramakrishnan Rajaraman ◽  
Sameer Antani
Keyword(s):  
Neural Network ◽  
Information Retrieval ◽  
Convolutional Neural Network ◽  
Visual Information ◽  
Medical Image ◽  
Input Image ◽  
Visual Interpretation ◽  
Feature Maps ◽  
Novel Method ◽  
Series Of Experiments

Deep learning (DL) methods are increasingly being applied for developing reliable computer-aided detection (CADe), diagnosis (CADx), and information retrieval algorithms. However, challenges in interpreting and explaining the learned behavior of the DL models hinders their adoption and use in real-world systems. In this study, we propose a novel method called “Class-selective Relevance Mapping” (CRM) for localizing and visualizing discriminative regions of interest (ROI) within a medical image. Such visualizations offer improved explanation of the convolutional neural network (CNN)-based DL model predictions. We demonstrate CRM effectiveness in classifying medical imaging modalities toward automatically labeling them for visual information retrieval applications. The CRM is based on linear sum of incremental mean squared errors (MSE) calculated at the output layer of the CNN model. It measures both positive and negative contributions of each spatial element in the feature maps produced from the last convolution layer leading to correct classification of an input image. A series of experiments on a “multi-modality” CNN model designed for classifying seven different types of image modalities shows that the proposed method is significantly better in detecting and localizing the discriminative ROIs than other state of the art class-activation methods. Further, to visualize its effectiveness we generate “class-specific” ROI maps by averaging the CRM scores of images in each modality class, and characterize the visual explanation through their different size, shape, and location for our multi-modality CNN model that achieved over 98% performance on a dataset constructed from publicly available images.

scholarly journals Weighted Channel Dropout for Regularization of Deep Convolutional Neural Network

2019 ◽  
Vol 33 ◽  
pp. 8425-8432 ◽  
Author(s):  
Saihui Hou ◽  
Zilei Wang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Random Number ◽  
Random Number Generator ◽  
Test Phase ◽  
Deep Convolutional Neural Network ◽  
Multiple Datasets ◽  
Novel Method ◽  
Fully Connected ◽  
Activation Status

In this work, we propose a novel method named Weighted Channel Dropout (WCD) for the regularization of deep Convolutional Neural Network (CNN). Different from Dropout which randomly selects the neurons to set to zero in the fully-connected layers, WCD operates on the channels in the stack of convolutional layers. Specifically, WCD consists of two steps, i.e., Rating Channels and Selecting Channels, and three modules, i.e., Global Average Pooling, Weighted Random Selection and Random Number Generator. It filters the channels according to their activation status and can be plugged into any two consecutive layers, which unifies the original Dropout and Channel-Wise Dropout. WCD is totally parameter-free and deployed only in training phase with very slight computation cost. The network in test phase remains unchanged and thus the inference cost is not added at all. Besides, when combining with the existing networks, it requires no re-pretraining on ImageNet and thus is well-suited for the application on small datasets. Finally, WCD with VGGNet-16, ResNet-101, Inception-V3 are experimentally evaluated on multiple datasets. The extensive results demonstrate that WCD can bring consistent improvements over the baselines.

scholarly journals Segmenting the Semi-Conductive Shielding Layer of Cable Slice Images Using the Convolutional Neural Network

Polymers ◽  
2020 ◽  
Vol 12 (9) ◽  
pp. 2085
Author(s):  
Wen Zhu ◽  
Fei Dong ◽  
Beiping Hou ◽  
Wesley Kenniard Takudzwa Gwatidzo ◽  
Le Zhou ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Essential Role ◽  
Structural Parameters ◽  
Receptive Fields ◽  
Structural Parameter ◽  
Pixel Classification ◽  
Convolutional Network ◽  
Fully Convolutional Network ◽  
Novel Method

Being an important part of aerial insulated cable, the semiconductive shielding layer is made of a typical polymer material and can improve the cable transmission effects; the structural parameters will affect the cable quality directly. Then, the image processing of the semiconductive layer plays an essential role in the structural parameter measurements. However, the semiconductive layer images are often disturbed by the cutting marks, which affect the measurements seriously. In this paper, a novel method based on the convolutional neural network is proposed for image segmentation. In our proposed strategy, a deep fully convolutional network with a skip connection algorithm is defined as the main framework. The inception structure and residual connection are employed to fuse features extracted from the receptive fields with different sizes. Finally, an improved weighted loss function and refined algorithm are utilized for pixel classification. Experimental results show that our proposed algorithm achieves better performance than the current algorithms.

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