Abstract: Deep neural networks (DNNs) are easily fooled by adversarial examples. Most existing defense strategies defend against adversarial examples based on full information of whole images. In ...
Abstract: Current state-of-the-art plug-and-play countermeasures for mitigating adversarial examples (i.e., purification and detection) exhibit several fatal limitations, impeding their deployment in ...
Researchers around the world are racing to develop new quantum-based systems for sensing, communication, computing and control that have the promise of outperforming traditional systems. Creating ...
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