Facial recognition systems are increasingly deployed by private corporations,
government agencies, and contractors for consumer services and mass
surveillance programs alike. These systems are typically built by scraping
social media profiles for user images. Adversarial perturbations have been
proposed for bypassing facial recognition systems. However, existing methods
fail on full-scale systems and commercial APIs. We develop our own adversarial
filter that accounts for the entire image processing pipeline and is
demonstrably effective against industrial-grade pipelines that include face
detection and large scale databases. Additionally, we release an easy-to-use
webtool that significantly degrades the accuracy of Amazon Rekognition and the
Microsoft Azure Face Recognition API, reducing the accuracy of each to below
1%.

该文研究了面部识别系统，并提出了可以应对这种系统的对抗滤镜，实验表明这种滤镜可以有效的通过包含面部检测和大规模数据库的工业流水线，同时发布了易于使用的 Web 工具来降低 Amazon Rekognition 和 Microsoft Azure Face Recognition API 的准确性。

LowKey: 利用对抗攻击保护社交媒体用户免受面部识别的侵害

LowKey: Leveraging Adversarial Attacks to Protect Social Media Users  from Facial Recognition

Machine learning techniques work best when the data used for training
resembles the data used for evaluation. This holds true for learned
single-image denoising algorithms, which are applied to real raw camera sensor
readings but, due to practical constraints, are often trained on synthetic
image data. Though it is understood that generalizing from synthetic to real
data requires careful consideration of the noise properties of image sensors,
the other aspects of a camera's image processing pipeline (gain, color
correction, tone mapping, etc) are often overlooked, despite their significant
effect on how raw measurements are transformed into finished images. To address
this, we present a technique to "unprocess" images by inverting each step of an
image processing pipeline, thereby allowing us to synthesize realistic raw
sensor measurements from commonly available internet photos. We additionally
model the relevant components of an image processing pipeline when evaluating
our loss function, which allows training to be aware of all relevant
photometric processing that will occur after denoising. By processing and
unprocessing model outputs and training data in this way, we are able to train
a simple convolutional neural network that has 14%-38% lower error rates and is
9x-18x faster than the previous state of the art on the Darmstadt Noise
Dataset, and generalizes to sensors outside of that dataset as well.

通过反演图像处理流水线的每个步骤，我们提出一种从常见网络照片中合成真实的原始传感器测量结果的技术，并在评估损失函数时建模图像处理流水线的相关组件，从而训练一个简单的卷积神经网络，相对于先前的最新技术，在 Darmstadt 噪声数据集上具有 14％-38％更低的误差率，并且是先前技术的 9 倍 - 18 倍更快，同时可以概括到该数据集之外的传感器。

去加工了的图像用于学习的原始去噪

Unprocessing Images for Learned Raw Denoising

Many creative ideas are being proposed for image sensor designs, and these
may be useful in applications ranging from consumer photography to computer
vision. To understand and evaluate each new design, we must create a
corresponding image processing pipeline that transforms the sensor data into a
form that is appropriate for the application. The need to design and optimize
these pipelines is time-consuming and costly. We explain a method that combines
machine learning and image systems simulation that automates the pipeline
design. The approach is based on a new way of thinking of the image processing
pipeline as a large collection of local linear filters. We illustrate how the
method has been used to design pipelines for novel sensor architectures in
consumer photography applications.

本文提出一种结合机器学习和图像系统仿真的方法，自动化处理传感器数据的流程设计，以此应用于图像传感器设计及其相应的相关领域，例如消费类摄影。