To explore the vulnerability of deep neural networks (DNNs), many attack paradigms have
been well studied, such as the poisoning-based backdoor attack in the training stage and …

Attackers can deceive neural networks by adding human imperceptive perturbations to their
input data; this reveals the vulnerability and weak robustness of current deep-learning …

Classifiers trained using conventional empirical risk minimization or maximum likelihood
methods are known to suffer dramatic performance degradations when tested over …

Deep neural networks (DNNs) are shown to be vulnerable to adversarial attacks—carefully
crafted additive noise that undermines DNNs integrity. Previously proposed defenses …

Recently, a new paradigm of the adversarial attack on the quantized neural network weights
has attracted great attention, namely, the Bit-Flip based adversarial weight attack, aka. Bit …

Neural networks are increasingly being moved to edge computing devices and smart
sensors, to reduce latency and save bandwidth. Neural network compression such as …

Deep neural networks (DNNs) have been shown to be powerful models and perform
extremely well on many complicated artificial intelligent tasks. However, recent research …

Deep Neural Networks (DNNs) have been shown to be vulnerable to adversarial attacks,
wherein, a model gets fooled by applying slight perturbations on the input. In this paper, we …

Data poisoning attacks on machine learning models have attracted much recent attention,
wherein poisoning samples are injected at the training phase to achieve adversarial goals at …

Deep neural networks are highly susceptible to imperceptible noise, even to the human eye.
While high attack success rate has been achieved in white-box setting, the attack …