ExypnoSteganos - A smarter approach to steganography

2021 ◽  
pp. 1-12
Author(s):  
Gaurav Sarraf ◽  
Anirudh Ramesh Srivatsa ◽  
MS Swetha
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Compression ◽  
Building Block ◽  
Communication Techniques ◽  
Systems Security ◽  
Learning Technique ◽  
Critical Problems ◽  
Sporadic Cases ◽  
Compressed Data

With the ever-rising threat to security, multiple industries are always in search of safer communication techniques both in rest and transit. Multiple security institutions agree that any systems security can be modeled around three major concepts: Confidentiality, Availability, and Integrity. We try to reduce the holes in these concepts by developing a Deep Learning based Steganography technique. In our study, we have seen, data compression has to be at the heart of any sound steganography system. In this paper, we have shown that it is possible to compress and encode data efficiently to solve critical problems of steganography. The deep learning technique, which comprises an auto-encoder with Convolutional Neural Network as its building block, not only compresses the secret file but also learns how to hide the compressed data in the cover file efficiently. The proposed techniques can encode secret files of the same size as of cover, or in some sporadic cases, even larger files can be encoded. We have also shown that the same model architecture can theoretically be applied to any file type. Finally, we show that our proposed technique surreptitiously evades all popular steganalysis techniques.

scholarly journals Detection of Driver’s Distractedness Using Convolutional Neural Networks

2021 ◽  
Vol 9 (VIII) ◽  
pp. 465-470
Author(s):  
Shailaja Pasupuleti
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Mobile Phones ◽  
Road Accidents ◽  
Image Dataset ◽  
Learning Technique ◽  
Critical Problems ◽  
Data Source

Deaths caused due to Road Accidents has always been an area of grave concern and happens to be one of the most critical problems in India as on date. The inattentiveness of the driver causes almost 80% of the casualties. Utilization of mobile phones, talking to passengers, reaching behind to grab something, and drinking while driving are some reasons drivers may lose attention. Distractions are considered of numerous types, out of which we focus on the manual distraction, which is based on the driver's posture. In particular, we consider nine distinct distracted states of the driver. Then, our goal is to detect whether a given image of the driver falls into one of these categories. Eventually, we will integrate an alarm that will alert the driver if she/he is detected to be in a distracted state. Accordingly, this paper presents a mechanism where we use convolutional neural networks; a deep learning technique, to classify driver images. The image dataset we use to train and test our neural network consists of the first dataset made available publicly at the Kaggle data source.

scholarly journals A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Jin-Woong Lee ◽  
Woon Bae Park ◽  
Jin Hee Lee ◽  
Satendra Pal Singh ◽  
Kee-Sun Sohn
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Inorganic Compounds ◽  
Phase Identification ◽  
X Ray Diffraction ◽  
X Ray ◽  
Learning Techniques ◽  
Learning Technique ◽  
Xrd Patterns

AbstractHere we report a facile, prompt protocol based on deep-learning techniques to sort out intricate phase identification and quantification problems in complex multiphase inorganic compounds. We simulate plausible powder X-ray diffraction (XRD) patterns for 170 inorganic compounds in the Sr-Li-Al-O quaternary compositional pool, wherein promising LED phosphors have been recently discovered. Finally, 1,785,405 synthetic XRD patterns are prepared by combinatorically mixing the simulated powder XRD patterns of 170 inorganic compounds. Convolutional neural network (CNN) models are built and eventually trained using this large prepared dataset. The fully trained CNN model promptly and accurately identifies the constituent phases in complex multiphase inorganic compounds. Although the CNN is trained using the simulated XRD data, a test with real experimental XRD data returns an accuracy of nearly 100% for phase identification and 86% for three-step-phase-fraction quantification.

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Deblending and classifying astronomical sources with Mask R-CNN deep learning

2019 ◽  
Vol 490 (3) ◽  
pp. 3952-3965 ◽  
Author(s):  
Colin J Burke ◽  
Patrick D Aleo ◽  
Yu-Ching Chen ◽  
Xin Liu ◽  
John R Peterson ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Ground Truth ◽  
Wide Field ◽  
Network Architectures ◽  
Deep Imaging ◽  
Learning Technique ◽  
Astronomical Images ◽  
Infrared Survey ◽  
Instance Segmentation

ABSTRACT We apply a new deep learning technique to detect, classify, and deblend sources in multiband astronomical images. We train and evaluate the performance of an artificial neural network built on the Mask Region-based Convolutional Neural Network image processing framework, a general code for efficient object detection, classification, and instance segmentation. After evaluating the performance of our network against simulated ground truth images for star and galaxy classes, we find a precision of 92 per cent at 80 per cent recall for stars and a precision of 98 per cent at 80 per cent recall for galaxies in a typical field with ∼30 galaxies arcmin−2. We investigate the deblending capability of our code, and find that clean deblends are handled robustly during object masking, even for significantly blended sources. This technique, or extensions using similar network architectures, may be applied to current and future deep imaging surveys such as Large Synoptic Survey Telescope and Wide-Field Infrared Survey Telescope. Our code, astro r-cnn, is publicly available at https://github.com/burke86/astro_rcnn.

Multi-modal infusion pump real-time monitoring technique for improvement in safety of intravenous-administration patients

2020 ◽  
Vol 235 (1) ◽  
pp. 73-81
Author(s):  
Young Jun Hwang ◽  
Gun Ho Kim ◽  
Eui Suk Sung ◽  
Kyoung Won Nam
Keyword(s):  
Neural Network ◽  
Performance Evaluation ◽  
Deep Learning ◽  
Real Time ◽  
Infusion Pump ◽  
Medication Administration ◽  
Thin Membrane ◽  
Monitoring Technique ◽  
Learning Technique ◽  
Deep Learning Model

Intravenous (IV) medication administration processes have been considered as high-risk steps, because accidents during IV administration can lead to serious adverse effects, which can deteriorate the therapeutic effect or threaten the patient’s life. In this study, we propose a multi-modal infusion pump (IP) monitoring technique, which can detect mismatches between the IP setting and actual infusion state and between the IP setting and doctor’s prescription in real time using a thin membrane potentiometer and convolutional-neural-network-based deep learning technique. During performance evaluation, the percentage errors between the reference infusion rate (IR) and average estimated IR were in the range of 0.50–2.55%, while those between the average actual IR and average estimated IR were in the range of 0.22–2.90%. In addition, the training, validation, and test accuracies of the implemented deep learning model after training were 98.3%, 97.7%, and 98.5%, respectively. The training and validation losses were 0.33 and 0.36, respectively. According to these experimental results, the proposed technique could provide improved protection functions to IV-administration patients.

scholarly journals Table Cleaning Task by Human Support Robot Using Deep Learning Technique

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1698 ◽  
Author(s):  
Jia Yin ◽  
Koppaka Ganesh Sai Apuroop ◽  
Yokhesh Krishnasamy Tamilselvam ◽  
Rajesh Elara Mohan ◽  
Balakrishnan Ramalingam ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Experimental Results ◽  
Detection Accuracy ◽  
Inspection Method ◽  
Learning Technique ◽  
Standard Quality ◽  
Typical Food ◽  
Human Support ◽  
Inspection Task

This work presents a table cleaning and inspection method using a Human Support Robot (HSR) which can operate in a typical food court setting. The HSR is able to perform a cleanliness inspection and also clean the food litter on the table by implementing a deep learning technique and planner framework. A lightweight Deep Convolutional Neural Network (DCNN) has been proposed to recognize the food litter on top of the table. In addition, the planner framework was proposed to HSR for accomplishing the table cleaning task which generates the cleaning path according to the detection of food litter and then the cleaning action is carried out. The effectiveness of the food litter detection module is verified with the cleanliness inspection task using Toyota HSR, and its detection results are verified with standard quality metrics. The experimental results show that the food litter detection module achieves an average of 96 % detection accuracy, which is more suitable for deploying the HSR robots for performing the cleanliness inspection and also helps to select the different cleaning modes. Further, the planner part has been tested through the table cleaning tasks. The experimental results show that the planner generated the cleaning path in real time and its generated path is optimal which reduces the cleaning time by grouping based cleaning action for removing the food litters from the table.

Effectiveness of a Hybrid Deep Learning Model Integrated with a Hybrid Parameterisation Model in Decision-Making Analysis

2020 ◽  
Author(s):  
Masurah Mohamad ◽  
Ali Selamat
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Missing Data ◽  
Machine Learning Technique ◽  
Processing Times ◽  
Learning Technique ◽  
Neural Network Algorithm ◽  
Deep Learning Model

Deep learning has recently gained the attention of many researchers in various fields. A new and emerging machine learning technique, it is derived from a neural network algorithm capable of analysing unstructured datasets without supervision. This study compared the effectiveness of the deep learning (DL) model vs. a hybrid deep learning (HDL) model integrated with a hybrid parameterisation model in handling complex and missing medical datasets as well as their performance in increasing classification. The results showed that 1) the DL model performed better on its own, 2) DL was able to analyse complex medical datasets even with missing data values, and 3) HDL performed well as well and had faster processing times since it was integrated with a hybrid parameterisation model.

Development of Radiographic Image Classification System for Weld Defect Identification using Deep Learning Technique

2021 ◽  
Vol 12 (5) ◽  
pp. 390-394
Author(s):  
Distun Stephen ◽  
Dr.Lalu P.P
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Neural Network Model ◽  
Classification System ◽  
Radiographic Image ◽  
Radiographic Images ◽  
Defect Identification ◽  
Weld Defect ◽  
Learning Technique ◽  
Welding Defect

Weld defect identification from radiographic images is a crucial task in the industry which requires trained human experts and enough specialists for performing timely inspections. This paper proposes a deep learning based approach to identify different weld defects automatically from radiographic images. To employ this a dataset containing 200 radiographic images labelled for four types of welding defect- gas pore, cluster porosity, crack and tungsten inclusion is developed. Then a Convolutional Neural Network model is designed and trained using this database.

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