AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
The obtained results exhibit the efficacy of the multi-teacher adversarial distillation and adaptive learning strategy, enhancing CNNs’ adversarial robustness against various adversarial attacks.
This work introduces Oscillatory Predictive Learning (OPL), a two-stage framework that combines Artificial Kuramoto Oscillatory Neurons (AKOrN) with predictive self-supervised pretraining using X-PhiNet and compares it with other randomized adversarial defense methods that provide precise, reproducible, and strong atta...
RL-FAT is proposed, a reinforcement-learning-inspired fair adversarial training framework that uses policy-gradient based feedback from adversarial predictions to improve adversarial robustness while promoting a more balanced robustness distribution across classes.
Neural networks, both convolution or transformer based, are essential for modern computer vision systems. However, they are vulnerable to small perturbations, almost imperceptible to humans, which significantly alter the model's prediction. These adversarial attacks are often considered to be a significant threat to th...
Adversarial training, one of the most effective methods for enhancing neural network robustness, is typically formulated as a min-max game between an attacker and a defender. Despite its success, most adversarial training methods suffer from robust overfitting, leading to a significant gap in robustness between the tra...
This work explores different neural network architectures, including fully connected networks, classical convolutional networks, and residual networks, under four types of adversarial attacks constrained by different L p norms, and investigates how adversarial examples affect the internal representations of networks...
Adversarial attacks misled deep neural networks by injecting perturbations into input images. Training networks with adversarial examples defended against adversarial attacks. However, training with specific adversarial examples only defended against the corresponding attacks. To generalize the defense effect from one...
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