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Introducing competition to boost the transferability of targeted adversarial examples through clean feature mixup
Deep neural networks are widely known to be susceptible to adversarial examples, which
can cause incorrect predictions through subtle input modifications. These adversarial …
can cause incorrect predictions through subtle input modifications. These adversarial …
Adversarial ranking attack and defense
Abstract Deep Neural Network (DNN) classifiers are vulnerable to adversarial attack, where
an imperceptible perturbation could result in misclassification. However, the vulnerability of …
an imperceptible perturbation could result in misclassification. However, the vulnerability of …
Robust design of deep neural networks against adversarial attacks based on lyapunov theory
Deep neural networks (DNNs) are vulnerable to subtle adversarial perturbations applied to
the input. These adversarial perturbations, though imperceptible, can easily mislead the …
the input. These adversarial perturbations, though imperceptible, can easily mislead the …
Adversarial examples for edge detection: They exist, and they transfer
Convolutional neural networks have recently advanced the state of the art in many tasks
including edge and object boundary detection. However, in this paper, we demonstrate that …
including edge and object boundary detection. However, in this paper, we demonstrate that …
System and method for training a neural network system
AR MOGHADDAM - US Patent 11,164,085, 2021 - Google Patents
(57) ABSTRACT A computer-implemented method for training a neural net work system. The
method includes receiving at least a first data vector at a first layer of the neural network …
method includes receiving at least a first data vector at a first layer of the neural network …