Kazim Ali1,*, Adnan N. Qureshi1, Muhammad Shahid Bhatti2, Abid Sohail2, Mohammad Hijji3
Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2237-2256, 2023, DOI:10.32604/iasc.2023.028810
Abstract Deep learning-based systems have succeeded in many computer vision tasks. However, it is found that the latest study indicates that these systems are in danger in the presence of adversarial attacks. These attacks can quickly spoil deep learning models, e.g., different convolutional neural networks (CNNs), used in various computer vision tasks from image classification to object detection. The adversarial examples are carefully designed by injecting a slight perturbation into the clean images. The proposed CRU-Net defense model is inspired by state-of-the-art defense mechanisms such as MagNet defense, Generative Adversarial Network Defense, Deep Regret Analytic Generative Adversarial Networks Defense, Deep Denoising… More >
Login
Register
