Adversarial patches exemplify the tangible manifestation of the threat posed
by adversarial attacks on Machine Learning (ML) models in real-world scenarios.
Robustness against these attacks is of the utmost importance when designing
computer vision applications, especially for safety-critical domains such as
CCTV systems. In most practical situations, monitoring open spaces requires
multi-view systems to overcome acquisition challenges such as occlusion
handling. Multiview object systems are able to combine data from multiple
views, and reach reliable detection results even in difficult environments.
Despite its importance in real-world vision applications, the vulnerability of
multiview systems to adversarial patches is not sufficiently investigated. In
this paper, we raise the following question: Does the increased performance and
information sharing across views offer as a by-product robustness to
adversarial patches? We first conduct a preliminary analysis showing promising
robustness against off-the-shelf adversarial patches, even in an extreme
setting where we consider patches applied to all views by all persons in
Wildtrack benchmark. However, we challenged this observation by proposing two
new attacks: (i) In the first attack, targeting a multiview CNN, we maximize
the global loss by proposing gradient projection to the different views and
aggregating the obtained local gradients. (ii) In the second attack, we focus
on a Transformer-based multiview framework. In addition to the focal loss, we
also maximize the transformer-specific loss by dissipating its attention
blocks. Our results show a large degradation in the detection performance of
victim multiview systems with our first patch attack reaching an attack success
rate of 73% , while our second proposed attack reduced the performance of its
target detector by 62%

在实际场景中机器学习模型受到对抗性攻击的威胁时，对抗性贴纸充分展示了这种威胁的具体体现。多视角系统具有在复杂环境中结合多视角数据，从而实现可靠检测结果的能力，然而多视角系统对对抗性贴纸的脆弱性尚未得到充分研究。本文通过提出两种新的攻击方法来挑战之前对多视角系统的观测结果，并取得了令人满意的攻击成功率。

愚弄九头蛇：对多视角物体检测系统的对抗攻击

Fool the Hydra: Adversarial Attacks against Multi-view Object Detection  Systems

This paper proposes the Hierarchical Functional Maximal Correlation Algorithm
(HFMCA), a hierarchical methodology that characterizes dependencies across two
hierarchical levels in multiview systems. By framing view similarities as
dependencies and ensuring contrastivity by imposing orthonormality, HFMCA
achieves faster convergence and increased stability in self-supervised
learning. HFMCA defines and measures dependencies within image hierarchies,
from pixels and patches to full images. We find that the network topology for
approximating orthonormal basis functions aligns with a vanilla CNN, enabling
the decomposition of density ratios between neighboring layers of feature maps.
This approach provides powerful interpretability, revealing the resemblance
between supervision and self-supervision through the lens of internal
representations.

通过将视图相似性视为依赖关系并通过正交性确保对比性，HFMCA 在自监督学习中实现了更快的收敛和增加的稳定性，从像素和补丁到全图定义和测量图像层次结构内的依赖关系，提供了强大的解释性并揭示了监督和自监督之间的相似性。