High-quality face image generation using particle swarm optimization-based generative adversarial networks

2021 ◽  
Vol 122 ◽  
pp. 98-104
Author(s):  
Long Zhang ◽  
Lin Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Face Image ◽  
Generative Adversarial Networks ◽  
Image Generation ◽  
High Quality ◽  
Swarm Optimization ◽  
Adversarial Networks

XGRouter: high-quality global router in X-architecture with particle swarm optimization

2015 ◽  
Vol 9 (4) ◽  
pp. 576-594 ◽  
Author(s):  
Genggeng Liu ◽  
Wenzhong Guo ◽  
Rongrong Li ◽  
Yuzhen Niu ◽  
Guolong Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
High Quality ◽  
Swarm Optimization ◽  
X Architecture

Particle swarm optimization as alternative tool to sensory evaluation to produce high-quality low-sodium fish sauce via electrodialysis

2018 ◽  
Vol 228 ◽  
pp. 84-90
Author(s):  
San Ratanasanya ◽  
Nathamol Chindapan ◽  
Jumpol Polvichai ◽  
Booncharoen Sirinaovakul ◽  
Sakamon Devahastin
Keyword(s):  
Particle Swarm Optimization ◽  
Sensory Evaluation ◽  
Particle Swarm ◽  
Fish Sauce ◽  
High Quality ◽  
Swarm Optimization ◽  
Low Sodium

scholarly journals PSOFuzzer: A Target-Oriented Software Vulnerability Detection Technology Based on Particle Swarm Optimization

2021 ◽  
Vol 11 (3) ◽  
pp. 1095
Author(s):  
Chen Chen ◽  
Han Xu ◽  
Baojiang Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Target Point ◽  
Vulnerability Detection ◽  
High Quality ◽  
Swarm Optimization ◽  
Detection Technology ◽  
Test Process ◽  
Quality Features ◽  
Historical Samples

Coverage-oriented and target-oriented fuzzing are widely used in vulnerability detection. Compared with coverage-oriented fuzzing, target-oriented fuzzing concentrates more computing resources on suspected vulnerable points to improve the testing efficiency. However, the sample generation algorithm used in target-oriented vulnerability detection technology has some problems, such as weak guidance, weak sample penetration, and difficult sample generation. This paper proposes a new target-oriented fuzzer, PSOFuzzer, that uses particle swarm optimization to generate samples. PSOFuzzer can quickly learn high-quality features in historical samples and implant them into new samples that can be led to execute the suspected vulnerable point. The experimental results show that PSOFuzzer can generate more samples in the test process to reach the target point and can trigger vulnerabilities with 79% and 423% higher probability than AFLGo and Sidewinder, respectively, on tested software programs.

High-quality face image generated with conditional boundary equilibrium generative adversarial networks

2018 ◽  
Vol 111 ◽  
pp. 72-79 ◽  
Author(s):  
Bin Huang ◽  
Weihai Chen ◽  
Xingming Wu ◽  
Chun-Liang Lin ◽  
Ponnuthurai Nagaratnam Suganthan
Keyword(s):  
Face Image ◽  
Generative Adversarial Networks ◽  
High Quality ◽  
Adversarial Networks ◽  
Boundary Equilibrium
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