Adversarial Attack News Today : Breaking News, Live Updates & Top Stories | Vimarsana

The rapid development of deep learning has demonstrated its potential for deployment in many intelligent service systems. However, some issues such as optimisation (e.g., how to reduce the deployment resources costs and further improve the detection speed), especially in scenarios where limited resources are available, remain challenging to address. In this paper, we aim to delve into the principles of deep neural networks, focusing on the importance of network neurons. The goal is to identify the neurons that exert minimal impact on model performances, thereby aiding in the process of model p...

While deep neural networks have excellent results in many fields, they are susceptible to interference from attacking samples resulting in erroneous judgments. Feature-level attacks are one of the effective attack types, which target the learned features in the hidden layers to improve their transferability across different models. Yet it is observed that the transferability has been largely impacted by the neuron importance estimation results. In this paper, a double adversarial neuron attribution attack method, termed ‘DANAA’, is proposed to obtain more accurate feature importance estima...

Deep neural networks excel at solving intuitive tasks that are hard to describe formally, such as classification, but are easily deceived by maliciously crafted samples, leading to misclassification. Recently, it has been observed that the attack-specific robustness of models obtained through adversarial training does not generalize well to novel or unseen attacks. While data augmentation through mixup in the input space has been shown to improve the generalization and robustness of models, there has been limited research progress on mixup in the latent space. Furthermore, almost no research o...

The development of connected and autonomous vehicles (CAV s) relies heavily on deep learning technology, which has been widely applied to perform a variety of tasks in CAYs. On the other hand, deep learning faces some security concerns. Data poisoning attack, as one of the security threats, can compromise the deep learning models by injecting poisoned training samples. The poisoned models may make more false predictions, and may cause fatal accidents of CA V s in the worst case. Therefore, the principles of poisoning attacks are worth studying in order to propose counter measures. In this work...

A joint research collaboration between US universities and IBM has formulated a proof-of-concept adversarial attack that's theoretically capable of causing stock market losses, simply by changing one word in a retweet of a Twitter post. The attack surface for an adversarial attack on automate