9 research outputs found
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
Neural networks are vulnerable to adversarial examples, which poses a threat
to their application in security sensitive systems. We propose high-level
representation guided denoiser (HGD) as a defense for image classification.
Standard denoiser suffers from the error amplification effect, in which small
residual adversarial noise is progressively amplified and leads to wrong
classifications. HGD overcomes this problem by using a loss function defined as
the difference between the target model's outputs activated by the clean image
and denoised image. Compared with ensemble adversarial training which is the
state-of-the-art defending method on large images, HGD has three advantages.
First, with HGD as a defense, the target model is more robust to either
white-box or black-box adversarial attacks. Second, HGD can be trained on a
small subset of the images and generalizes well to other images and unseen
classes. Third, HGD can be transferred to defend models other than the one
guiding it. In NIPS competition on defense against adversarial attacks, our HGD
solution won the first place and outperformed other models by a large margin
Exploring Transferability of Multimodal Adversarial Samples for Vision-Language Pre-training Models with Contrastive Learning
Vision-language pre-training models (VLP) are vulnerable, especially to
multimodal adversarial samples, which can be crafted by adding imperceptible
perturbations on both original images and texts. However, under the black-box
setting, there have been no works to explore the transferability of multimodal
adversarial attacks against the VLP models. In this work, we take CLIP as the
surrogate model and propose a gradient-based multimodal attack method to
generate transferable adversarial samples against the VLP models. By applying
the gradient to optimize the adversarial images and adversarial texts
simultaneously, our method can better search for and attack the vulnerable
images and text information pairs. To improve the transferability of the
attack, we utilize contrastive learning including image-text contrastive
learning and intra-modal contrastive learning to have a more generalized
understanding of the underlying data distribution and mitigate the overfitting
of the surrogate model so that the generated multimodal adversarial samples
have a higher transferability for VLP models. Extensive experiments validate
the effectiveness of the proposed method
Silicon photonic MEMS switches based on split waveguide crossings
The continuous push for high-performance photonic switches is one of the most
crucial premises for the sustainable scaling of programmable and reconfigurable
photonic circuits for a wide spectrum of applications. Large-scale photonic
switches constructed with a large number of 22 elementary switches
impose stringent requirements on the elementary switches. In contrast to
conventional elementary switches based on mode interference or mode coupling,
here we propose and realize a brand-new silicon MEMS 22 elementary
switch based on a split waveguide crossing (SWX) consisting of two halves. With
this structure, the propagation direction of the incident light can be
manipulated to implement the OFF and ON states by splitting or combining the
two halves of the SWX, respectively. More specifically, we introduce
refractive-index engineering by incorporating subwavelength-tooth (SWT)
structures on both reflecting facets to further reduce the excess loss in the
ON state. Such a unique switching mechanism features a compact footprint on a
standard SOI wafer and enables excellent photonic performance with low excess
loss of 0.1-0.52/0.1-0.47dB and low crosstalk of -37/-22.5dB over an
ultrawide bandwidth of 1400-1700nm for the OFF/ON states in simulation, while
in experiment, excess loss of 0.15-0.52/0.42-0.66dB and crosstalk of
-45.5/-25dB over the bandwidth of 1525-1605 nm for the OFF/ON states have
been measured.Furthermore, excellent MEMS characteristics such as near-zero
steady-state power consumption, low switching energy of sub-pJ, switching speed
of {\mu}s-scale, durability beyond 10^9 switching cycles, and overall device
robustness have been achieved. Finally, a 1616 switch using Benes
topology has also been fabricated and characterized as a proof of concept,
further validating the suitability of the SWX switches for large-scale
integration