Deep learning vulnerability analysis against adversarial attacks

HIGHLIGHTS

• What: The study underlines the importance of developing AI systems that are not only intelligent but also robust against adversarial tactics aiming to enhance future deep learning models` resilience to such vulnerabilities. The ongoing quest for sophisticated methods to combat adversarial attacks highlights the dynamic nature of AI research, aims at understanding adversarial examples and devising robust defenses . While this approach has shown promise, it is not a panacea; adversarial training can sometimes lead to a reduction in accuracy on clean data, as the model may become overly conservative or biased towards the adversarial examples it has . . .
  

   

  

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