Deep neural networks (DNNs) can easily be cheated by some imperceptible but
purposeful noise added to images, and erroneously classify them. Previous
defensive work mostly focused on retraining the models or detecting the noise,
but has either shown limited success rates or been attacked by new adversarial
examples. Instead of focusing on adversarial images or the interior of DNN
models, we observed that adversarial examples generated by different algorithms
can be identified based on the output of DNNs (logits). Logit can serve as an
exterior feature to train detectors. Then, we propose HOLMES (Hierarchically
Organized Light-weight Multiple dEtector System) to reinforce DNNs by detecting
potential adversarial examples to minimize the threats they may bring in
practical. HOLMES is able to distinguish \textit{unseen} adversarial examples
from multiple attacks with high accuracy and low false positive rates than
single detector systems even in an adaptive model. To ensure the diversity and
randomness of detectors in HOLMES, we use two methods: training dedicated
detectors for each label and training detectors with top-k logits. Our
effective and inexpensive strategies neither modify original DNN models nor
require its internal parameters. HOLMES is not only compatible with all kinds
of learning models (even only with external APIs), but also complementary to
other defenses to achieve higher detection rates (may also fully protect the
system against various adversarial examples).

通过监测深度神经网络（DNNs）的输出（logits），我们提出了 HOLMES（Hierarchically Organized Light-weight Multiple dEtector System），用于检测潜在的对抗性示例，以加强 DNNs 并最小化其对实际环境的威胁。HOLMES 能够高精度地区分来自多种攻击方式产生的未知对抗性示例，并且相较于单一检测系统具有较低的误报率，在适应性模型中表现出色。

HOLMES：使用多个检测器检测对抗样本

HOLMES: to Detect Adversarial Examples with Multiple Detectors

Convolutional Neural Networks (CNNs) are nowadays the model of choice in
Computer Vision, thanks to their ability to automatize the feature extraction
process in visual tasks. However, the knowledge acquired during training is
fully subsymbolic, and hence difficult to understand and explain to end users.
In this paper, we propose a new technique called HOLMES (HOLonym-MEronym based
Semantic inspection) that decomposes a label into a set of related concepts,
and provides component-level explanations for an image classification model.
Specifically, HOLMES leverages ontologies, web scraping and transfer learning
to automatically construct meronym (parts)-based detectors for a given holonym
(class). Then, it produces heatmaps at the meronym level and finally, by
probing the holonym CNN with occluded images, it highlights the importance of
each part on the classification output. Compared to state-of-the-art saliency
methods, HOLMES takes a step further and provides information about both where
and what the holonym CNN is looking at, without relying on densely annotated
datasets and without forcing concepts to be associated to single computational
units. Extensive experimental evaluation on different categories of objects
(animals, tools and vehicles) shows the feasibility of our approach. On
average, HOLMES explanations include at least two meronyms, and the ablation of
a single meronym roughly halves the holonym model confidence. The resulting
heatmaps were quantitatively evaluated using the
deletion/insertion/preservation curves. All metrics were comparable to those
achieved by GradCAM, while offering the advantage of further decomposing the
heatmap in human-understandable concepts, thus highlighting both the relevance
of meronyms to object classification, as well as HOLMES ability to capture it.
The code is available at this https URL

提出了一种名为 HOLMES 的技术，利用本体论、网络爬虫和迁移学习自动构建基于部分的探测器，通过遮挡图像来突出显示每个部分对分类输出的重要性，并在不依赖于密集注释数据集和将概念与单个计算单元关联的情况下，提供关于 CNN 在何处以及何物上的信息。

基于 HOLonym-MEronym 的语义检查技术用于卷积图像分类器

HOLMES: HOLonym-MEronym based Semantic inspection for Convolutional  Image Classifiers

In this study, we explore the potential of Multimodal Large Language Models
(MLLMs) in improving embodied decision-making processes for agents. While Large
Language Models (LLMs) have been widely used due to their advanced reasoning
skills and vast world knowledge, MLLMs like GPT4-Vision offer enhanced visual
understanding and reasoning capabilities. We investigate whether
state-of-the-art MLLMs can handle embodied decision-making in an end-to-end
manner and whether collaborations between LLMs and MLLMs can enhance
decision-making. To address these questions, we introduce a new benchmark
called PCA-EVAL, which evaluates embodied decision-making from the perspectives
of Perception, Cognition, and Action. Additionally, we propose HOLMES, a
multi-agent cooperation framework that allows LLMs to leverage MLLMs and APIs
to gather multimodal information for informed decision-making. We compare
end-to-end embodied decision-making and HOLMES on our benchmark and find that
the GPT4-Vision model demonstrates strong end-to-end embodied decision-making
abilities, outperforming GPT4-HOLMES in terms of average decision accuracy
(+3%). However, this performance is exclusive to the latest GPT4-Vision model,
surpassing the open-source state-of-the-art MLLM by 26%. Our results indicate
that powerful MLLMs like GPT4-Vision hold promise for decision-making in
embodied agents, offering new avenues for MLLM research.

通过 PCA-EVAL 基准和 HOLMES 多智能体协作框架的比较，我们发现 GPT4-Vision 模型在端到端的具体决策制定能力方面表现出色，平均决策准确率比 GPT4-HOLMES 高出 3%，但这种性能仅适用于最新的 GPT4-Vision 模型，超过开源最先进的多模态大型语言模型的 26%。我们的结果表明，像 GPT4-Vision 这样功能强大的多模态大型语言模型对具体决策有很大的潜力，为多模态大型语言模型研究开辟了新的途径。