Defending Against Adversarial Attacks in Deep Learning with Robust Auxiliary Classifiers Utilizing Bit Plane Slicing

2020 ◽  
Author(s):  
Yuan Liu ◽  
Pingqiang Zhou
Keyword(s):  
Deep Learning ◽  
Bit Plane ◽  
Bit Plane Slicing

scholarly journals Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Ching-Chun Chang ◽  
Xu Wang ◽  
Ji-Hwei Horng ◽  
Isao Echizen
Keyword(s):  
Deep Learning ◽  
Medical Images ◽  
Image Transmission ◽  
Compression Algorithm ◽  
Healthcare Sector ◽  
Generative Adversarial Networks ◽  
Progressive Image Transmission ◽  
Adversarial Networks ◽  
Bit Plane ◽  
Bit Planes

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

scholarly journals VCC-BPS: Vertical Collaborative Clustering using Bit Plane Slicing

PLoS ONE ◽  
2021 ◽  
Vol 16 (1) ◽  
pp. e0244691
Author(s):  
WAQAR ISHAQ ◽  
ELIYA BUYUKKAYA ◽  
MUSHTAQ ALI ◽  
ZAKIR KHAN
Keyword(s):  
Actual Data ◽  
Common Disease ◽  
Collaborative Clustering ◽  
Data Owner ◽  
Bit Plane ◽  
Class Labels ◽  
A Chain ◽  
Maximum Similarity ◽  
Improved Accuracy ◽  
Bit Plane Slicing

The vertical collaborative clustering aims to unravel the hidden structure of data (similarity) among different sites, which will help data owners to make a smart decision without sharing actual data. For example, various hospitals located in different regions want to investigate the structure of common disease among people of different populations to identify latent causes without sharing actual data with other hospitals. Similarly, a chain of regional educational institutions wants to evaluate their students’ performance belonging to different regions based on common latent constructs. The available methods used for finding hidden structures are complicated and biased to perform collaboration in measuring similarity among multiple sites. This study proposes vertical collaborative clustering using a bit plane slicing approach (VCC-BPS), which is simple and unique with improved accuracy, manages collaboration among various data sites. The VCC-BPS transforms data from input space to code space, capturing maximum similarity locally and collaboratively at a particular bit plane. The findings of this study highlight the significance of those particular bits which fit the model in correctly classifying class labels locally and collaboratively. Thenceforth, the data owner appraises local and collaborative results to reach a better decision. The VCC-BPS is validated by Geyser, Skin and Iris datasets and its results are compared with the composite dataset. It is found that the VCC-BPS outperforms existing solutions with improved accuracy in term of purity and Davies-Boulding index to manage collaboration among different data sites. It also performs data compression by representing a large number of observations with a small number of data symbols.

Automated glaucoma diagnosis using bit-plane slicing and local binary pattern techniques

2019 ◽  
Vol 105 ◽  
pp. 72-80 ◽  
Author(s):  
Shishir Maheshwari ◽  
Vivek Kanhangad ◽  
Ram Bilas Pachori ◽  
Sulatha V. Bhandary ◽  
U. Rajendra Acharya
Keyword(s):  
Local Binary Pattern ◽  
Glaucoma Diagnosis ◽  
Bit Plane ◽  
Bit Plane Slicing

scholarly journals High Quality - Low Computational Cost Technique for Automated Principal Object Segmentation Applied in Solar and Medical Imaging

2016 ◽  
Vol 9 (2) ◽  
pp. 23 ◽  
Author(s):  
Sofyan M. A. Hayajneh ◽  
AbdulRahman Rashad ◽  
Omar A. Saraereh ◽  
Obaida Al hazaimeh
Keyword(s):  
Real Time ◽  
Computational Cost ◽  
Real Life ◽  
Morphological Operation ◽  
Detection Process ◽  
Good Potential ◽  
Bit Plane ◽  
Noisy Background ◽  
Low Computational Cost ◽  
Bit Plane Slicing

The objective of this paper is to introduce a fully computerized, simple and low-computational cost technique that can be used in the preprocessing stages of digital images. This technique is specially designed to detect the principal (largest) closed shape object that embody the useful information in certain image types and neglect and avoid other noisy objects and artifacts. The detection process starts by calculating certain statistics of the image to estimate the amount of bit-plane slicing required to exclude the non-informative and noisy background. A simple closing morphological operation is then applied and followed by circular filter applied only on the outer coarse edge to finalize the detection process.  The proposed technique takes its importance from the huge explosion of images that need accurate processing in real time speedy manner. The proposed technique is implemented using MATLAB and tested on many solar and medical images; it was shown by the quantitative evaluation that the proposed technique can handle real-life (e.g. solar, medical fundus) images and shows very good potential even under noisy and artifacts conditions. Compared to the publicly available datasets, 97% and 99% of similarity detection is achieved in medical and solar images, respectively. Although it is well-know, the morphological bit-plane slicing technique is hoped to be used in the preprocessing stages of different applications to ease the subsequent image processing stages especially in real time applications where the proposed technique showed dramatic (~100 times) saving in processing time.

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