When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example. However, such adversarial attacks perturbing the …
Many recent studies demonstrate that state-of-the-art Deep neural networks (DNNs) might be easily fooled by adversarial examples, generated by adding carefully crafted and visually imperceptible distortions onto original legal inputs through …
Deep neural networks (DNNs) have been shown to be powerful models and perform extremely well on many complicated artificial intelligent tasks. However, recent research found that these powerful models are vulnerable to adversarial attacks, i.e., …
As deep learning penetrates into wide application domains, it is essential to evaluate the robustness of deep neural networks (DNNs) under adversarial attacks, especially for some security-critical applications. To better understand the security …
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. This work …
Deep neural networks (DNNs) are known vulnerable to adversarial attacks. That is, adversarial examples, obtained by adding delicately crafted distortions onto original legal inputs, can mislead a DNN to classify them as any target labels. In a …
Hybrid electric vehicles employ a hybrid propulsion system to combine the energy efficiency of electric motor and a long driving range of internal combustion engine, thereby achieving a higher fuel economy as well as convenience compared with …