Physical adversarial attacks against object detectors have seen increasing
success in recent years. However, these attacks require direct access to the
object of interest in order to apply a physical patch. Furthermore, to hide
multiple objects, an adversarial patch must be applied to each object. In this
paper, we propose a contactless translucent physical patch containing a
carefully constructed pattern, which is placed on the camera's lens, to fool
state-of-the-art object detectors. The primary goal of our patch is to hide all
instances of a selected target class. In addition, the optimization method used
to construct the patch aims to ensure that the detection of other (untargeted)
classes remains unharmed. Therefore, in our experiments, which are conducted on
state-of-the-art object detection models used in autonomous driving, we study
the effect of the patch on the detection of both the selected target class and
the other classes. We show that our patch was able to prevent the detection of
42.27% of all stop sign instances while maintaining high (nearly 80%) detection
of the other classes.

本文提出了一种无接触、半透明的物理贴片，通过在摄像头镜头上放置一个精心构造的图案，欺骗了最先进的目标检测器。我们的贴片的主要目标是隐藏所选目标类别的所有实例，并确保其他类别的检测受到保护。实验表明，我们的贴片能够防止检测所有停车标志实例的 42.27％，同时保持了其他类别的高检测率（近 80％）。

半透明贴片：一种物理通用攻击目标检测器的方法

The Translucent Patch: A Physical and Universal Attack on Object  Detectors

Coded-illumination can enable quantitative phase microscopy of transparent
samples with minimal hardware requirements. Intensity images are captured with
different source patterns and a non-linear phase retrieval optimization
reconstructs the image. The non-linear nature of the processing makes
optimizing the illumination pattern designs complicated. Traditional techniques
for experimental design (e.g. condition number optimization, spectral analysis)
consider only linear measurement formation models and linear reconstructions.
Deep neural networks (DNNs) can efficiently represent the non-linear process
and can be optimized over via training in an end-to-end framework. However,
DNNs typically require a large amount of training examples and parameters to
properly learn the phase retrieval process, without making use of the known
physical models. Here, we aim to use both our knowledge of the physics and the
power of machine learning together. We develop a new data-driven approach to
optimizing coded-illumination patterns for a LED array microscope for a given
phase reconstruction algorithm. Our method incorporates both the physics of the
measurement scheme and the non-linearity of the reconstruction algorithm into
the design problem. This enables efficient parameterization, which allows us to
use only a small number of training examples to learn designs that generalize
well in the experimental setting without retraining. We show experimental
results for both a well-characterized phase target and mouse fibroblast cells
using coded-illumination patterns optimized for a sparsity-based phase
reconstruction algorithm. Our learned design results using 2 measurements
demonstrate similar accuracy to Fourier Ptychography with 69 measurements.

本文提出了一种新的数据驱动方法，通过机器学习技术和物理学知识相结合，优化了 LED 阵列显微镜的编码照明模式，并将其应用于稀疏性相位重建算法，证明了该方法可以显著提高实验结果的准确性，且只需较少的训练数据。