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

Answer Set Programming (ASP) is an increasingly popular framework for
declarative programming that admits the description of problems by means of
rules and constraints that form a disjunctive logic program. In particular,
many AI problems such as reasoning in a nonmonotonic setting can be directly
formulated in ASP. Although the main problems of ASP are of high computational
complexity, located at the second level of the Polynomial Hierarchy, several
restrictions of ASP have been identified in the literature, under which ASP
problems become tractable.
In this paper we use the concept of backdoors to identify new restrictions
that make ASP problems tractable. Small backdoors are sets of atoms that
represent "clever reasoning shortcuts" through the search space and represent a
hidden structure in the problem input. The concept of backdoors is widely used
in the areas of propositional satisfiability and constraint satisfaction. We
show that it can be fruitfully adapted to ASP. We demonstrate how backdoors can
serve as a unifying framework that accommodates several tractable restrictions
of ASP known from the literature. Furthermore, we show how backdoors allow us
to deploy recent algorithmic results from parameterized complexity theory to
the domain of answer set programming.

本文介绍了如何使用 backdoors 的概念来识别能够使 ASP 问题可行的新限制，展示了 backdoors 如何作为一个统一的框架，能够容纳文献中已知的几个可行限制，并展示了如何利用 backdoors 将参数化复杂性理论的最新算法结果应用到答案集编程领域。