Feature extraction-based image steganalysis using deep learning

2021 ◽  
pp. 188-198
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Secure Communication ◽  
Information Technologies ◽  
Network Models ◽  
Error Rates ◽  
Multimedia Data ◽  
Secret Message ◽  
Neural Network Models

The innovations in advanced information technologies has led to rapid delivery and sharing of multimedia data like images and videos. The digital steganography offers ability to secure communication and imperative for internet. The image steganography is essential to preserve confidential information of security applications. The secret image is embedded within pixels. The embedding of secret message is done by applied with S-UNIWARD and WOW steganography. Hidden messages are reveled using steganalysis. The exploration of research interests focused on conventional fields and recent technological fields of steganalysis. This paper devises Convolutional neural network models for steganalysis. Convolutional neural network (CNN) is one of the most frequently used deep learning techniques. The Convolutional neural network is used to extract spatio-temporal information or features and classification. We have compared steganalysis outcome with AlexNet and SRNeT with same dataset. The stegnalytic error rates are compared with different payloads.

Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

2020 ◽  
pp. 274-294
Author(s):  
Yilin Yan ◽  
Min Chen ◽  
Saad Sadiq ◽  
Mei-Ling Shyu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Imbalanced Data ◽  
Network Models ◽  
Multimedia Data ◽  
Neural Network Models ◽  
Minority Class ◽  
Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

Efficient Imbalanced Multimedia Concept Retrieval by Deep Learning on Spark Clusters

2017 ◽  
Vol 8 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Yilin Yan ◽  
Min Chen ◽  
Saad Sadiq ◽  
Mei-Ling Shyu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Imbalanced Data ◽  
Network Models ◽  
Multimedia Data ◽  
Neural Network Models ◽  
Minority Class ◽  
Imbalanced Data Classification

The classification of imbalanced datasets has recently attracted significant attention due to its implications in several real-world use cases. The classifiers developed on datasets with skewed distributions tend to favor the majority classes and are biased against the minority class. Despite extensive research interests, imbalanced data classification remains a challenge in data mining research, especially for multimedia data. Our attempt to overcome this hurdle is to develop a convolutional neural network (CNN) based deep learning solution integrated with a bootstrapping technique. Considering that convolutional neural networks are very computationally expensive coupled with big training datasets, we propose to extract features from pre-trained convolutional neural network models and feed those features to another full connected neutral network. Spark implementation shows promising performance of our model in handling big datasets with respect to feasibility and scalability.

scholarly journals Predicting Success of Outbound Telemarketing in Insurance Policy Loans Using an Explainable Multiple-Filter Convolutional Neural Network

2021 ◽  
Vol 11 (15) ◽  
pp. 7147
Author(s):  
Jinmo Gu ◽  
Jinhyuk Na ◽  
Jeongeun Park ◽  
Hayoung Kim
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
False Positive Rate ◽  
Direct Marketing ◽  
Network Models ◽  
Learning Models ◽  
Insurance Policy ◽  
Neural Network Models ◽  
Potential Customers

Outbound telemarketing is an efficient direct marketing method wherein telemarketers solicit potential customers by phone to purchase or subscribe to products or services. However, those who are not interested in the information or offers provided by outbound telemarketing generally experience such interactions negatively because they perceive telemarketing as spam. In this study, therefore, we investigate the use of deep learning models to predict the success of outbound telemarketing for insurance policy loans. We propose an explainable multiple-filter convolutional neural network model called XmCNN that can alleviate overfitting and extract various high-level features using hundreds of input variables. To enable the practical application of the proposed method, we also examine ensemble models to further improve its performance. We experimentally demonstrate that the proposed XmCNN significantly outperformed conventional deep neural network models and machine learning models. Furthermore, a deep learning ensemble model constructed using the XmCNN architecture achieved the lowest false positive rate (4.92%) and the highest F1-score (87.47%). We identified important variables influencing insurance policy loan prediction through the proposed model, suggesting that these factors should be considered in practice. The proposed method may increase the efficiency of outbound telemarketing and reduce the spam problems caused by calling non-potential customers.

scholarly journals Four-Dimension Deep Learning Method for Flower Quality Grading with Depth Information

Electronics ◽  
2021 ◽  
Vol 10 (19) ◽  
pp. 2353
Author(s):  
Xinyan Sun ◽  
Zhenye Li ◽  
Tingting Zhu ◽  
Chao Ni
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Classification Accuracy ◽  
Network Models ◽  
Depth Image ◽  
Depth Information ◽  
Neural Network Models ◽  
Flower Bud ◽  
Flower Quality

Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstly, the RGB image and the depth image of a flower bud were collected and transformed into fused RGBD information. Then, the RGBD information of a flower was set as inputs of a convolutional neural network to determine the flower bud maturing status. Four convolutional neural network models (VGG16, ResNet18, MobileNetV2, and InceptionV3) were adjusted for a four-dimensional (4D) RGBD input to classify flowers, and their classification performances were compared with and without depth information. The experimental results show that the classification accuracy was improved with depth information, and the improved InceptionV3 network with RGBD achieved the highest classification accuracy (up to 98%), which means that the depth information can effectively reflect the characteristics of the flower bud and is helpful for the classification of the maturing status. These results have a certain significance for the intelligent classification and sorting of fresh flowers.

scholarly journals Subclass Separation of White Blood Cell Images Using Convolutional Neural Network Models

2019 ◽  
Vol 25 (5) ◽  
pp. 63-68 ◽  
Author(s):  
Mesut Togacar ◽  
Burhan Ergen ◽  
Mehmet Emre Sertkaya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Immune System ◽  
Blood Cell ◽  
Convolutional Neural Network ◽  
White Blood Cell ◽  
Blood Cells ◽  
White Blood Cells ◽  
Network Models ◽  
Neural Network Models

The white blood cells produced in the bone marrow and lymphoid tissue known as leucocytes are an important part of the immune system to protect the body against foreign invaders and infectious disease. These cells, which do not have color, have a few days or several weeks of life. A lot of clinic experience is required for a doctor to detect the amount of white blood cells in human blood and classify it. Thus, early and accurate diagnosis can be made in the formation of various disease types, including infection on the immune system, such as anemia and leukemia, while evaluating and determining the disease of a patient. The white blood cells can be separated into four subclasses, such as Eosinophil, Lymphocyte, Monocyte, and Neutrophil. This study focuses on the separation of the white blood cell images by the classification process using convolutional neural network models, which is a deep learning model. A deep learning network, which is slow in the training step due to the complex architecture, but fast in the test step, is used for the feature extraction instead of intricate methods. For the subclass separation of white blood cells, the experimental results show that the AlexNet architecture gives the correct recognition rate among the convolutional neural network architectures tested in the study. Various classifiers are performed on the features derived from the AlexNet architecture to evaluate the classification performance. The best performance in the classification of white blood cells is given by the quadratic discriminant analysis classifier with the accuracy of 97.78 %.

Deep Learning With TensorFlow: A Review

2019 ◽  
Vol 45 (2) ◽  
pp. 227-248 ◽  
Author(s):  
Bo Pang ◽  
Erik Nijkamp ◽  
Ying Nian Wu
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Optimization Method ◽  
Stochastic Gradient Descent ◽  
Processing Unit ◽  
Neural Network Models ◽  
Core Concepts ◽  
High Level

This review covers the core concepts and design decisions of TensorFlow. TensorFlow, originally created by researchers at Google, is the most popular one among the plethora of deep learning libraries. In the field of deep learning, neural networks have achieved tremendous success and gained wide popularity in various areas. This family of models also has tremendous potential to promote data analysis and modeling for various problems in educational and behavioral sciences given its flexibility and scalability. We give the reader an overview of the basics of neural network models such as the multilayer perceptron, the convolutional neural network, and stochastic gradient descent, the most commonly used optimization method for neural network models. However, the implementation of these models and optimization algorithms is time-consuming and error-prone. Fortunately, TensorFlow greatly eases and accelerates the research and application of neural network models. We review several core concepts of TensorFlow such as graph construction functions, graph execution tools, and TensorFlow’s visualization tool, TensorBoard. Then, we apply these concepts to build and train a convolutional neural network model to classify handwritten digits. This review is concluded by a comparison of low- and high-level application programming interfaces and a discussion of graphical processing unit support, distributed training, and probabilistic modeling with TensorFlow Probability library.

scholarly journals Application of deep learning for division of petroleum reservoirs

2018 ◽  
Vol 246 ◽  
pp. 03004
Author(s):  
Yaqiong Qin ◽  
Zhaohui Ye ◽  
Conghui Zhang
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Bp Neural Network ◽  
Network Models ◽  
Petroleum Reservoirs ◽  
Division Problem ◽  
Learning Models ◽  
Neural Network Models ◽  
Hidden Layer

Traditional methods of dividing petroleum reservoirs are inefficient, and the accuracy of onehidden-layer BP neural network is not ideal when applied to dividing reservoirs. This paper proposes to use the deep learning models to solve the reservoir division problem. We apply multiple-hidden-layer BP neural network and convolutional neural network models, and adjust the network structures according to the characteristics of the reservoir problem. The results show that the deep learning models are better than onehidden- layer BP neural network, and the performance of the convolutional neural network is very close to the artificial work.

scholarly journals Recognition of Cursive Pashto Optical Digits and Characters with Trio Deep Learning Neural Network Models

Electronics ◽  
2021 ◽  
Vol 10 (20) ◽  
pp. 2508
Author(s):  
Muhammad Zubair Rehman ◽  
Nazri Mohd. Nawi ◽  
Mohammad Arshad ◽  
Abdullah Khan
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Models ◽  
The Other ◽  
Neural Network Models ◽  
The World ◽  
Deep Cnn ◽  
Deep Learning Neural Network ◽  
Percent Accuracy

Pashto is one of the most ancient and historical languages in the world and is spoken in Pakistan and Afghanistan. Various languages like Urdu, English, Chinese, and Japanese have OCR applications, but very little work has been conducted on the Pashto language in this perspective. It becomes more difficult for OCR applications to recognize handwritten characters and digits, because handwriting is influenced by the writer’s hand dynamics. Moreover, there was no publicly available dataset for handwritten Pashto digits before this study. Due to this, there was no work performed on the recognition of Pashto handwritten digits and characters combined. To achieve this objective, a dataset of Pashto handwritten digits consisting of 60,000 images was created. The trio deep learning Convolutional Neural Network, i.e., CNN, LeNet, and Deep CNN were trained and tested with both Pashto handwritten characters and digits datasets. From the simulations, the Deep CNN achieved 99.42 percent accuracy for Pashto handwritten digits, 99.17 percent accuracy for handwritten characters, and 70.65 percent accuracy for combined digits and characters. Similarly, LeNet and CNN models achieved slightly less accuracies (LeNet; 98.82, 99.15, and 69.82 percent and CNN; 98.30, 98.74, and 66.53 percent) for Pashto handwritten digits, Pashto characters, and the combined Pashto digits and characters recognition datasets, respectively. Based on these results, the Deep CNN model is the best model in terms of accuracy and loss as compared to the other two models.

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