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Visualization of adversarial samples.

Visualization of adversarial samples.

<p>(a) represents the original image and (b)-(f) represent the adversarial examples generated by five methods.</p>

Shranjeno v:
Bibliografske podrobnosti
Glavni avtor: Chen Lin (95910) (author)
Drugi avtorji: Sheng Long (14795824) (author)
Izdano: 2025
Teme:
Neuroscience
Biological Sciences not elsewhere classified
Information Systems not elsewhere classified
two crucial metrics
nesterov &# 8217
natural data distribution
introduces nesterov ’
goals often conflict
generation process toward
extensive experiments demonstrate
deep neural networks
achieving stealthy attacks
imperceptible perturbations tend
evaluating adversarial attacks
div >< p
diffusion mechanism guides
imperceptible adversarial examples
imperceptible perturbations
adversarial attacks
box attacks
strong acceleration
size strategy
perform poorly
novel framework
mislead models
highly vulnerable
generalization capabilities
art methods
adding subtle
adaptive step
accelerated gradient
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  • Attack success rates (%) of adversarial attacks against six models. <sup>*</sup> indicates the white-box attacks.
    od: Chen Lin (95910)
    Izdano: (2025)
  • Attack success rates (%) of adversarial attacks against twelve models. <sup>*</sup> indicates the white-box attacks.
    od: Chen Lin (95910)
    Izdano: (2025)
  • Attack success rates (%) of adversarial attacks against twelve models. <sup>*</sup> indicates the white-box attacks.
    od: Chen Lin (95910)
    Izdano: (2025)
  • Attack success rates (%) of adversarial attacks against twelve models. <sup>*</sup> indicates the white-box attacks.
    od: Chen Lin (95910)
    Izdano: (2025)
  • Attack success rates (%) of adversarial attacks against twelve models. <sup>*</sup> indicates the white-box attacks.
    od: Chen Lin (95910)
    Izdano: (2025)

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