scholarly journals A Novel Deep Learning Architecture for Image Hiding

2021 ◽  
Vol 16 ◽  
pp. 206-210
Author(s):  
S. Muni Rathnam ◽  
G. Siva Koteswara Rao
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Cover Image ◽  
Logical Relation ◽  
Deep Learning Algorithm ◽  
Image Hiding ◽  
Audio Recordings ◽  
Zero Watermarking ◽  
Processing Techniques ◽  
Electronic Content

Watermarking is a today's digital hiding technique within certain electronic content: for example, message, image, video, or audio recordings. Recent times, it was created as a modern copyright security tool. The pattern in zero watermarking technique isn't really inserted directly in the cover image, but has a logical relation with that cover image. In this article, we propose a powerful convolution neural Networks (CNN) and deep learning algorithm-based-watermarking technique in which the CNN produces robust inherent selected features and is merged with the XOR activity of host's watermark sequence. The outcomes of our proposed method present the courage of the watermark counter to many typical image processing techniques.

scholarly journals Image and Text Steganography Using Convolutional Neural Network

2021 ◽  
Vol 65 (1) ◽  
pp. 26-32
Author(s):  
Vijay Kumar ◽  
Saloni Laddha ◽  
Aniket ◽  
Nitin Dogra
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Cover Image ◽  
Secret Message ◽  
Main Aspect ◽  
Learning Modules ◽  
Deep Learning Algorithm ◽  
Minimal Distortion ◽  
Text Steganography

Steganography has been used since centuries for concealment of messages in a cover media where messages were physically hidden. The goal in our project is to hide digital messages using modern steganography techniques. An N * N RGB pixel secret message (either text or image) is to be transmitted in another N * N RGB container image with minimum changes in its contents. The cover image also called the carrier can be publicly visible. In this project, along with LSB encoding, deep learning modules using the Adam algorithm are used to train the model that comprises a hiding network and a reveal network. The encoder neural network determines where and how to place the message, dispersing it throughout the bits of cover image. The decoder network on the receiving side, which is simultaneously trained with the encoder, reveals the secret image. The main aspect of this work is it produces minimal distortion to the secret message. Thus, preserving its integrity. Also, other steganography softwares cannot be used to reveal the message since the model is trained using a deep learning algorithm which complicates its steganalysis. The network is only trained once, irrespective of the different container images and secret messages given as inputs. Thus, this work has wide and secure applications in many fields.

Detecting faults in VSC-HVDC systems by deep learning and K-means

2020 ◽  
Vol 13 ◽  
Author(s):  
Roohollah Sadeghi Goughari ◽  
Mehdi Jafari Shahbazzadeh ◽  
Mahdiyeh Eslami
Keyword(s):  
Signal Processing ◽  
Deep Learning ◽  
Wavelet Transform ◽  
Transmission Lines ◽  
Fault Location ◽  
Learning Algorithm ◽  
Hvdc Transmission ◽  
Deep Learning Algorithm ◽  
Processing Techniques ◽  
Signal Processing Techniques

Background: In this paper, two methods and their comparison used to determine the fault locaton in VSC-HVDC transmission lines. Fast and reliable control are features of these systems. Methods: Additionally, wavelet transform from advanced techniques of signal processing is employed for the purpose of extracting important characteristics of fault signal from both sides of the line by PMU. To do so, Deep learning is used to identify the relation between the extracted features from wavelet analysis of the fault current and variations under fault conditions. As such, wavelet transform and advanced signal processing techniques are used to extract important features of fault signal from both sides of the line by the PMU. Results: The results show the high accuracy of finding fault location by the deep learning algorithm method compared to the k-means algorithm with an error rate of <1%. Conclusion: Studies on the 50 kV VSC-HVDC transmission line with a length of 25 km in MATLAB have been simulated.

scholarly journals Kathakali face expression detection using deep learning techniques

2021 ◽  
Vol 2062 (1) ◽  
pp. 012018
Author(s):  
C Selvi ◽  
Y Anvitha ◽  
C H Asritha ◽  
P B Sayannah
Keyword(s):  
Deep Learning ◽  
Facial Expressions ◽  
Learning Algorithm ◽  
Paper Machine ◽  
Classical Dance ◽  
Face Expression ◽  
Deep Learning Algorithm ◽  
Art Form ◽  
Preprocessing Technique ◽  
Processing Techniques

Abstract To develop a Deep Learning algorithm that detects the Kathakali face expression (or Navarasas) from a given image of a person who performs Kathakali. One of India’s major classical dance forms is Kathakali. It is a “story play” genre of art, but one distinguished by the traditional male-actor-dancers costumes, face masks and makeup they wear. In the Southern region of India, Kathakali is a Hindu performance art in Malayalam speaking. Most of the plays are epic scenes of Mahabharata and Ramayana. A lot of foreigners visiting India are inspired by this art form and have been curious about the culture. It is still used for entertainment as a part of tourism and temple rituals. An understanding of facial expressions are essential so as to enjoy the play. The scope of the paper is to identify the facial expressions of Kathakali to have a better understanding of the art play. In this paper, Machine Learning and Image Processing techniques are used to decode the expressions. Kathakali face expressions are nine types namely-Adbhutam (wonder), Hasyam (comic), Sringaram(love), Bheebatsam(repulsion), Bhayanakam(fear), Roudram(anger), Veeram(pride), Karunam(sympathy) and Shantham (peace). These Expressions are mapped to real world human emotions for better classification through face detection and extraction to achieve the same. Similarly a lot of research in terms of Preprocessing and Classification is done to achieve the maximum accuracy. Using CNN algorithm 90% of the accuracy was achieved. In order to conserve the pixel distribution and as no preprocessing was used for better object recognition and analysis Fuzzy algorithm is taken into consideration. Using this preprocessing technique 93% accuracy was achieved.

scholarly journals Deep learning diagnostic and risk-stratification pattern detection for COVID-19 in digital lung auscultations: clinical protocol for a case–control and prospective cohort study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alban Glangetas ◽  
Mary-Anne Hartley ◽  
Aymeric Cantais ◽  
Delphine S. Courvoisier ◽  
David Rivollet ◽  
...  
Keyword(s):  
Deep Learning ◽  
Risk Stratification ◽  
Learning Algorithm ◽  
Signs And Symptoms ◽  
Support Vector ◽  
Nasopharyngeal Swab ◽  
University Hospitals ◽  
Deep Learning Algorithm ◽  
Audio Recordings ◽  
Lung Auscultation

Abstract Background Lung auscultation is fundamental to the clinical diagnosis of respiratory disease. However, auscultation is a subjective practice and interpretations vary widely between users. The digitization of auscultation acquisition and interpretation is a particularly promising strategy for diagnosing and monitoring infectious diseases such as Coronavirus-19 disease (COVID-19) where automated analyses could help decentralise care and better inform decision-making in telemedicine. This protocol describes the standardised collection of lung auscultations in COVID-19 triage sites and a deep learning approach to diagnostic and prognostic modelling for future incorporation into an intelligent autonomous stethoscope benchmarked against human expert interpretation. Methods A total of 1000 consecutive, patients aged ≥ 16 years and meeting COVID-19 testing criteria will be recruited at screening sites and amongst inpatients of the internal medicine department at the Geneva University Hospitals, starting from October 2020. COVID-19 is diagnosed by RT-PCR on a nasopharyngeal swab and COVID-positive patients are followed up until outcome (i.e., discharge, hospitalisation, intubation and/or death). At inclusion, demographic and clinical data are collected, such as age, sex, medical history, and signs and symptoms of the current episode. Additionally, lung auscultation will be recorded with a digital stethoscope at 6 thoracic sites in each patient. A deep learning algorithm (DeepBreath) using a Convolutional Neural Network (CNN) and Support Vector Machine classifier will be trained on these audio recordings to derive an automated prediction of diagnostic (COVID positive vs negative) and risk stratification categories (mild to severe). The performance of this model will be compared to a human prediction baseline on a random subset of lung sounds, where blinded physicians are asked to classify the audios into the same categories. Discussion This approach has broad potential to standardise the evaluation of lung auscultation in COVID-19 at various levels of healthcare, especially in the context of decentralised triage and monitoring. Trial registration: PB_2016-00500, SwissEthics. Registered on 6 April 2020.

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