There are various costs for attackers to manipulate the features of security
classifiers. The costs are asymmetric across features and to the directions of
changes, which cannot be precisely captured by existing cost models based on
$L_p$-norm robustness. In this paper, we utilize such domain knowledge to
increase the attack cost of evading classifiers, specifically, tree ensemble
models that are widely used by security tasks. We propose a new cost modeling
method to capture the feature manipulation cost as constraint, and then we
integrate the cost-driven constraint into the node construction process to
train robust tree ensembles. During the training process, we use the constraint
to find data points that are likely to be perturbed given the feature
manipulation cost, and we use a new robust training algorithm to optimize the
quality of the trees. Our cost-aware training method can be applied to
different types of tree ensembles, including gradient boosted decision trees
and random forest models. Using Twitter spam detection as the case study, our
evaluation results show that we can increase the attack cost by 10.6X compared
to the baseline. Moreover, our robust training method using cost-driven
constraint can achieve higher accuracy, lower false positive rate, and stronger
cost-aware robustness than the state-of-the-art training method using
$L_\infty$-norm cost model. Our code is available at
this https URL

该论文提出了一种基于成本驱动的约束来训练鲁棒性决策树模型的成本建模方法，在 Twitter 垃圾邮件检测等场景中显示出比现有方法更高的准确性、更低的误报率和更强的成本感知鲁棒性。

面向安全应用的成本感知鲁棒树集成

Cost-Aware Robust Tree Ensembles for Security Applications

We investigate the problem of manually correcting errors from an automatic
speech transcript in a cost-sensitive fashion. This is done by specifying a
fixed time budget, and then automatically choosing location and size of
segments for correction such that the number of corrected errors is maximized.
The core components, as suggested by previous research [1], are a utility model
that estimates the number of errors in a particular segment, and a cost model
that estimates annotation effort for the segment. In this work we propose a
dynamic updating framework that allows for the training of cost models during
the ongoing transcription process. This removes the need for transcriber
enrollment prior to the actual transcription, and improves correction
efficiency by allowing highly transcriber-adaptive cost modeling. We first
confirm and analyze the improvements afforded by this method in a simulated
study. We then conduct a realistic user study, observing efficiency
improvements of 15% relative on average, and 42% for the participants who
deviated most strongly from our initial, transcriber-agnostic cost model.
Moreover, we find that our updating framework can capture dynamically changing
factors, such as transcriber fatigue and topic familiarity, which we observe to
have a large influence on the transcriber's working behavior.

本文研究了在成本敏感的情况下，如何以固定的时间预算自动选择错误修正的位置和大小，以最大化修正的错误数量，并提出了一种动态更新框架来训练纠错成本模型，进而实现高度适应性。该方法在模拟和实际的用户研究中均获得了有效的效率改进。

逆时间转录

Transcribing Against Time

View-graph is an essential input to large-scale structure from motion (SfM)
pipelines. Accuracy and efficiency of large-scale SfM is crucially dependent on
the input view-graph. Inconsistent or inaccurate edges can lead to inferior or
wrong reconstruction. Most SfM methods remove `undesirable' images and pairs
using several, fixed heuristic criteria, and propose tailor-made solutions to
achieve specific reconstruction objectives such as efficiency, accuracy, or
disambiguation. In contrast to these disparate solutions, we propose a single
optimization framework that can be used to achieve these different
reconstruction objectives with task-specific cost modeling. We also construct a
very efficient network-flow based formulation for its approximate solution. The
abstraction brought on by this selection mechanism separates the challenges
specific to datasets and reconstruction objectives from the standard SfM
pipeline and improves its generalization. This paper demonstrates the
application of the proposed view-graph framework with standard SfM pipeline for
two particular use-cases, (i) accurate and ghost-free reconstructions of highly
ambiguous datasets using costs based on disambiguation priors, and (ii)
accurate and efficient reconstruction of large-scale Internet datasets using
costs based on commonly used priors.

本研究提出了一种基于优化框架和网络流算法的视图图优化框架，可用于大规模运动结构三维重建，并通过成本模型实现了高精度、高效、准确重建的任务目标，该模型可以抽象出数据集和重建目标中独特的挑战，并显著改善了通用 SfM 管道的表现。