Comparison with state-of-the-art studies.

<div><p>Convolutional Neural Network (CNN)-based models are prone to adversarial attacks, which present a significant hurdle to their reliability and robustness. The vulnerability of CNN-based models may be exploited by attackers to launch cyber-attacks. An attacker typically adds small,...

وصف كامل

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Syeda Nazia Ashraf (17541222) (author)
مؤلفون آخرون:	Raheel Siddiqi (19923944) (author), Humera Farooq (19923947) (author)
منشور في:	2024
الموضوعات:	Biotechnology Immunology Science Policy Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified reducing overall accuracy projected gradient descent carefully crafted perturbations automating routine tasks also save time called stack model reliable defense framework multiple attack types based model receives autoencoder improves accuracy single defense mechanism reliable defense mechanism based models may based defense mechanism various adversarial attacks popular adversarial attacks detect adversarial attacks art attacks carried original medical images based pneumonia detection pgd attack using perturbed medical image pneumonia detection models vgg16 model shows defense mechanism based models adversarial attack adversarial attacks medical images hybrid model adversarial images robust detection defense strategies trained models convolutional autoencoder art studies pgd attacks ray images two state two pre treatment planning study shows significant hurdle one type often imperceptible launch cyber human eye five magnitudes facilitating radiologists even though earlier studies deep learning chest x auto encoder added perturbation 67 %.
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Comparison with state-of-the-art studies.

مواد مشابهة