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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
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DualFlow: Generating imperceptible adversarial examples by flow field and normalize flow-based model.

Renyang Liu1,2, Xin Jin2,3, Dongting Hu4

  • 1School of Information Science and Engineering, Yunnan University, Kunming, China.

Frontiers in Neurorobotics
|February 27, 2023
PubMed
Summary

DualFlow crafts imperceptible adversarial examples for deep neural networks (DNNs) by manipulating latent image representations. This novel approach enhances adversarial attack effectiveness while evading detection by defense mechanisms and the human visual system (HVS).

Keywords:
adversarial attackadversarial exampledeep learningnormalize flowspatial transform

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Area of Science:

  • Computer Vision
  • Machine Learning Security

Background:

  • Deep learning models (DNNs) are vulnerable to adversarial attacks.
  • Existing attacks often generate perceptible perturbations, detectable by defenses and the human visual system (HVS).

Purpose of the Study:

  • To propose a novel framework, DualFlow, for generating human imperceptible adversarial examples.
  • To explore the fragility of DNNs by circumventing limitations of current attack methods.

Main Methods:

  • Utilizing a flow-based model and spatial transform techniques to disturb latent image representations.
  • Generating adversarial examples with imperceptible perturbations.

Main Results:

  • DualFlow achieves superior attack performance across CIFAR-10, CIFAR-100, and ImageNet datasets.
  • The method generates more imperceptible adversarial examples compared to existing imperceptible attack methods, validated by visualization and quantitative metrics.

Conclusions:

  • DualFlow effectively generates imperceptible adversarial examples, enhancing adversarial attack capabilities.
  • The framework offers a promising direction for exploring DNN vulnerabilities with improved stealth.