Adversarial Attack, Defense, and Applications with Deep Learning Frameworks

2019 ◽  
pp. 1-25
Author(s):  
Zhizhou Yin ◽  
Wei Liu ◽  
Sanjay Chawla
Keyword(s):  
Deep Learning ◽  
Adversarial Attack ◽  
Learning Frameworks

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

scholarly journals An exploratory study on the introduction and removal of different types of technical debt in deep learning frameworks

2021 ◽  
Vol 26 (2) ◽  
Author(s):  
Jiakun Liu ◽  
Qiao Huang ◽  
Xin Xia ◽  
Emad Shihab ◽  
David Lo ◽  
...  
Keyword(s):  
Deep Learning ◽  
Exploratory Study ◽  
Technical Debt ◽  
Different Types ◽  
Learning Frameworks

An Investigation of Deep-Learning Frameworks for Speaker Verification Antispoofing

2017 ◽  
Vol 11 (4) ◽  
pp. 684-694 ◽  
Author(s):  
Chunlei Zhang ◽  
Chengzhu Yu ◽  
John H. L. Hansen
Keyword(s):  
Deep Learning ◽  
Speaker Verification ◽  
Learning Frameworks

scholarly journals Securing IoT Devices: A Robust and Efficient Deep Learning with a Mixed Batch Adversarial Generation Process for CAPTCHA Security Verification

Electronics ◽  
2021 ◽  
Vol 10 (15) ◽  
pp. 1798
Author(s):  
Stephen Dankwa ◽  
Lu Yang
Keyword(s):  
Deep Learning ◽  
Internet Of Things ◽  
Process Model ◽  
Good Practice ◽  
Knowledge Exchange ◽  
Turing Test ◽  
The Internet ◽  
Generation Process ◽  
Adversarial Attack ◽  
Mixed Batch

The Internet of Things environment (e.g., smart phones, smart televisions, and smart watches) ensures that the end user experience is easy, by connecting lives on web services via the internet. Integrating Internet of Things devices poses ethical risks related to data security, privacy, reliability and management, data mining, and knowledge exchange. An adversarial machine learning attack is a good practice to adopt, to strengthen the security of text-based CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart), to withstand against malicious attacks from computer hackers, to protect Internet of Things devices and the end user’s privacy. The goal of this current study is to perform security vulnerability verification on adversarial text-based CAPTCHA, based on attacker–defender scenarios. Therefore, this study proposed computation-efficient deep learning with a mixed batch adversarial generation process model, which attempted to break the transferability attack, and mitigate the problem of catastrophic forgetting in the context of adversarial attack defense. After performing K-fold cross-validation, experimental results showed that the proposed defense model achieved mean accuracies in the range of 82–84% among three gradient-based adversarial attack datasets.

Algorithmically-consistent deep learning frameworks for structural topology optimization

2021 ◽  
Vol 106 ◽  
pp. 104483
Author(s):  
Jaydeep Rade ◽  
Aditya Balu ◽  
Ethan Herron ◽  
Jay Pathak ◽  
Rishikesh Ranade ◽  
...  
Keyword(s):  
Deep Learning ◽  
Topology Optimization ◽  
Structural Topology Optimization ◽  
Structural Topology ◽  
Learning Frameworks

Deep Learning Frameworks for COVID-19 Detection

2021 ◽  
Author(s):  
Ankit Vijayvargiya ◽  
Akshit Panchal ◽  
Abhijeet Parashar ◽  
Ayush Gautam ◽  
Jayesh Sharma ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Frameworks
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