Image scaling is an integral part of machine learning and computer vision
systems. Unfortunately, this preprocessing step is vulnerable to so-called
image-scaling attacks where an attacker makes unnoticeable changes to an image
so that it becomes a new image after scaling. This opens up new ways for
attackers to control the prediction or to improve poisoning and backdoor
attacks. While effective techniques exist to prevent scaling attacks, their
detection has not been rigorously studied yet. Consequently, it is currently
not possible to reliably spot these attacks in practice.
This paper presents the first in-depth systematization and analysis of
detection methods for image-scaling attacks. We identify two general detection
paradigms and derive novel methods from them that are simple in design yet
significantly outperform previous work. We demonstrate the efficacy of these
methods in a comprehensive evaluation with all major learning platforms and
scaling algorithms. First, we show that image-scaling attacks modifying the
entire scaled image can be reliably detected even under an adaptive adversary.
Second, we find that our methods provide strong detection performance even if
only minor parts of the image are manipulated. As a result, we can introduce a
novel protection layer against image-scaling attacks.

图像缩放是机器学习和计算机视觉系统中的重要组成部分，然而，这一预处理步骤容易受到所谓的图像缩放攻击的影响。本文首次对图像缩放攻击的检测方法进行深入系统化和分析，提出了两种普遍的检测范例，并从中推导出简单但在设计上明显优于以前方法的新方法。通过全面评估所有主要学习平台和缩放算法，我们证明了这些方法的有效性。因此，我们可引入一种新的保护层来抵御图像缩放攻击。

机器学习中图像伸缩攻击的检测

On the Detection of Image-Scaling Attacks in Machine Learning

Backdoors and poisoning attacks are a major threat to the security of
machine-learning and vision systems. Often, however, these attacks leave
visible artifacts in the images that can be visually detected and weaken the
efficacy of the attacks. In this paper, we propose a novel strategy for hiding
backdoor and poisoning attacks. Our approach builds on a recent class of
attacks against image scaling. These attacks enable manipulating images such
that they change their content when scaled to a specific resolution. By
combining poisoning and image-scaling attacks, we can conceal the trigger of
backdoors as well as hide the overlays of clean-label poisoning. Furthermore,
we consider the detection of image-scaling attacks and derive an adaptive
attack. In an empirical evaluation, we demonstrate the effectiveness of our
strategy. First, we show that backdoors and poisoning work equally well when
combined with image-scaling attacks. Second, we demonstrate that current
detection defenses against image-scaling attacks are insufficient to uncover
our manipulations. Overall, our work provides a novel means for hiding traces
of manipulations, being applicable to different poisoning approaches.

本文提出了一种新的策略，旨在通过利用图像缩放攻击来隐藏后门和毒化攻击，并探讨了检测图像缩放攻击的防御。同时证明了该策略对各种毒化方案都可行，但现有的检测防御措施不足以揭示我们的操作。

利用图像缩放攻击植入后门和毒化神经网络

Backdooring and Poisoning Neural Networks with Image-Scaling Attacks

In vision-enabled autonomous systems such as robots and autonomous cars,
video object detection plays a crucial role, and both its speed and accuracy
are important factors to provide reliable operation. The key insight we show in
this paper is that speed and accuracy are not necessarily a trade-off when it
comes to image scaling. Our results show that re-scaling the image to a lower
resolution will sometimes produce better accuracy. Based on this observation,
we propose a novel approach, dubbed AdaScale, which adaptively selects the
input image scale that improves both accuracy and speed for video object
detection. To this end, our results on ImageNet VID and mini
YouTube-BoundingBoxes datasets demonstrate 1.3 points and 2.7 points mAP
improvement with 1.6x and 1.8x speedup, respectively. Additionally, we improve
state-of-the-art video acceleration work by an extra 1.25x speedup with
slightly better mAP on ImageNet VID dataset.

本文介绍了一种被称为 AdaScale 的新方法，该方法在视频对象检测方面提供了更好的准确性和速度，并展示了 - imagenet VID 和 mini YouTube - 边界框数据集上的结果。