In image, video and even real physics domains, adversarial examples can mislead deep models to produce wrong predictions. Transfer-based attacks against black-box models are more in line with realistic scenarios, but adversarial examples made on surrogate model have a low success rate when transferred to the target model due to overfitting the source model. We study the Stochastic Weight Averaging strategy in the domain generalization process and propose a Stochastic Perturbation Averaging method (SPA). Specifically, we add stochastic perturbations to the examples during the gradient descent attack, and we design a Central Amplification method (CAM) to enhance this random variation, then SPA stabilizes the iteration direction by computing the gradient average of the perturbed examples to find a relatively flat local minimum of the loss function. SPA is an efficient and general strategy which can significantly improve the transferability of the gradient-based attack methods. For instance, the average attack success rate of the adversarial examples produced based on four single models against seven pre-trained models reached 90.10%, which is the best result so far. Code is available at https://github.con yangrongbo/SPA.

Transfer-based attacks against black-box neural network models have received increasing attention because they are more realistic scenarios, but how to produce highly transferable adversarial examples on the surrogate model becomes critical. In this work, we find that if the attack direction of the original example is controlled from the beginning, the produced adversarial examples will be more transferable. Specifically, we propose the Output Direction Controller (ODC) to initialize the example direction so that the example starts off with a deviation from the true direction or toward the target direction. ODC is a simple and extensible component that can be combined with various transfer-based attack methods and significantly improve the transferability of the adversarial examples. On the ImageNet dataset, we optimize the baseline method by ODC to improve the success rate of untargeted attacks by an average of 11.79% and targeted attacks by an average of 3.38%. Code is available at https://github.com/yangrongbo/ODC.

Through-silicon-vias (TSVs) can potentially be used to implement inductors in 3-D integrated systems for minimal footprint and large inductance. However, different from conventional 2-D spiral inductors, TSV inductors are fully buried in the lossy substrate, thus suffering from low quality factors. In this paper, we systematically examine how various process and design parameters affect their performance. A few interesting phenomena that are unique to TSV inductors are observed. We then propose a novel shield mechanism utilizing the microchannel, a technique conventionally used for heat removal, to reduce the substrate loss. The technique increases the quality factor and inductance of the TSV inductor by up to 21× and 17×, respectively. Finally, since full-wave simulations of 3-D structures are time-consuming, we develop a set of compressed sensing-based design strategies for microchannel-shielded TSV inductors, which only requires a minimal number of simulations. It enables us to implement microchannel-shielded TSV inductors of up to 5.44× reduced area compared with spiral inductors of the same design specs (quality factor, inductance, and frequency). To the best of our knowledge, this is the very first in-depth study on TSV inductors to make them practical for high-frequency applications. We hope our study shall point out a new and exciting research direction for 3-D integrated circuit designers.