The vulnerability of deep neural networks to adversarial patches has
motivated numerous defense strategies for boosting model robustness. However,
the prevailing defenses depend on single observation or pre-established
adversary information to counter adversarial patches, often failing to be
confronted with unseen or adaptive adversarial attacks and easily exhibiting
unsatisfying performance in dynamic 3D environments. Inspired by active human
perception and recurrent feedback mechanisms, we develop Embodied Active
Defense (EAD), a proactive defensive strategy that actively contextualizes
environmental information to address misaligned adversarial patches in 3D
real-world settings. To achieve this, EAD develops two central recurrent
sub-modules, i.e., a perception module and a policy module, to implement two
critical functions of active vision. These models recurrently process a series
of beliefs and observations, facilitating progressive refinement of their
comprehension of the target object and enabling the development of strategic
actions to counter adversarial patches in 3D environments. To optimize learning
efficiency, we incorporate a differentiable approximation of environmental
dynamics and deploy patches that are agnostic to the adversary strategies.
Extensive experiments demonstrate that EAD substantially enhances robustness
against a variety of patches within just a few steps through its action policy
in safety-critical tasks (e.g., face recognition and object detection), without
compromising standard accuracy. Furthermore, due to the attack-agnostic
characteristic, EAD facilitates excellent generalization to unseen attacks,
diminishing the averaged attack success rate by 95 percent across a range of
unseen adversarial attacks.

通过活跃的感知和反馈机制，Embodied Active Defense（EAD）开发了一种主动的防御策略，积极地上下文化环境信息来解决 3D 实际环境中的对抗修补问题，提高模型的鲁棒性。

具身主动防御：利用循环反馈对抗对抗性补丁

Embodied Active Defense: Leveraging Recurrent Feedback to Counter  Adversarial Patches

We present an end-to-end procedure for embodied exploration based on two
biologically inspired computations: predictive coding and uncertainty
minimization. The procedure can be applied to any exploration setting in a
task-independent and intrinsically driven manner. We first demonstrate our
approach in a maze navigation task and show that our model is capable of
discovering the underlying transition distribution and reconstructing the
spatial features of the environment. Second, we apply our model to the more
complex task of active vision, where an agent must actively sample its visual
environment to gather information. We show that our model is able to build
unsupervised representations that allow it to actively sample and efficiently
categorize sensory scenes. We further show that using these representations as
input for downstream classification leads to superior data efficiency and
learning speed compared to other baselines, while also maintaining lower
parameter complexity. Finally, the modularity of our model allows us to analyze
its internal mechanisms and to draw insight into the interactions between
perception and action during exploratory behavior.

运用预测编码和不确定性最小化两种生物启发式计算方式，提出了一种端到端的嵌入式探索过程，可用于解决任务无关的探索问题。在迷宫导航和积极视觉任务中，我们的模型均能够通过建立无监督表示，对感知场景进行主动采样、高效分类，学习效率更高、数据效率更高。此外，模型的模块化设计允许分析其内部机制以及在探索行为中，感知与行动之间的相互作用。

基于预测编码和不确定性最小化的主动感知

Active Sensing with Predictive Coding and Uncertainty Minimization

Visual recognition systems mounted on autonomous moving agents face the
challenge of unconstrained data, but simultaneously have the opportunity to
improve their performance by moving to acquire new views of test data. In this
work, we first show how a recurrent neural network-based system may be trained
to perform end-to-end learning of motion policies suited for this "active
recognition" setting. Further, we hypothesize that active vision requires an
agent to have the capacity to reason about the effects of its motions on its
view of the world. To verify this hypothesis, we attempt to induce this
capacity in our active recognition pipeline, by simultaneously learning to
forecast the effects of the agent's motions on its internal representation of
the environment conditional on all past views. Results across two challenging
datasets confirm both that our end-to-end system successfully learns meaningful
policies for active category recognition, and that "learning to look ahead"
further boosts recognition performance.

通过训练循环神经网络模型的运动策略，结合学习预测代理在移动中对环境的影响，我们成功实现了主动学习视觉识别，并提高了识别性能。