Carbonized polyaniline bridging nanodiamond-graphene hybrids for enhanced microwave absorptions with ultrathin thickness.

For conventional design of the electromagnetic absorption materials, introduction of magnetic materials into dielectric materials has been found to achieve better impedance matching, but lead to increase in weight and decrease in chemical stability, therefore limiting their practical applications. In this work, metal-free electromagnetic coupling was achieved by the design of nitrogen-doped nanodiamond/graphene hybrids. Polyaniline is used to self-assembled bridge the nanodiamond and graphene, and the carbonization is carried out for construction and regulation of the C•••N polarization and nitrogen doping. The carbonized hybrid exhibits remarkably enhanced broadband electromagnetic absorption with the optimal reflection loss value around -47.7 dB at 13.8 GHz with an ultrathin thickness of 1.8 mm. The enhancement in electromagnetic absorption is confirmed to result from nitrogen doped ND induced magnetic dissipation and the C•••N multi-polarization modes, as well as the multiple interfacial structures. This work opens a new route realizing lightweight electromagnetic absorption through constructing nitrogen doped carbon nanomaterial.

Enhancing adversarial attacks with resize-invariant and logical ensemble.

In black-box scenarios, most transfer-based attacks usually improve the transferability of adversarial examples by optimizing the gradient calculation of the input image. Unfortunately, since the gradient information is only calculated and optimized for each pixel point in the image individually, the generated adversarial examples tend to overfit the local model and have poor transferability to the target model. To tackle the issue, we propose a resize-invariant method (RIM) and a logical ensemble transformation method (LETM) to enhance the transferability of adversarial examples. Specifically, RIM is inspired by the resize-invariant property of Deep Neural Networks (DNNs). The range of resizable pixel is first divided into multiple intervals, and then the input image is randomly resized and padded within each interval. Finally, LETM performs logical ensemble of multiple images after RIM transformation to calculate the final gradient update direction. The proposed method adequately considers the information of each pixel in the image and the surrounding pixels. The probability of duplication of image transformations is minimized and the overfitting effect of adversarial examples is effectively mitigated. Numerous experiments on the ImageNet dataset show that our approach outperforms other advanced methods and is capable of generating more transferable adversarial examples.