增强具身主动防御:利用自适应交互在对抗的三维环境稳健的视觉感知。

Xiao Yang, Lingxuan Wu, Lizhong Wang, Chengyang Ying, Hang Su, Jun Zhu
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引用次数: 0

摘要

3D环境中的对抗性攻击已经成为视觉感知系统可靠性的重大威胁,特别是在身份验证和自动驾驶等安全敏感应用中。这些攻击使用对抗性补丁和3D对象,通过利用复杂场景中的漏洞来操纵深度神经网络(DNN)预测。现有的防御机制,如对抗性训练和净化,主要采用被动策略来增强鲁棒性。然而,这些方法通常依赖于预先定义的对抗策略假设,限制了它们在动态3D环境中的适应性。为了应对这些挑战,我们引入了强化具身主动防御(REIN-EAD),这是一种主动防御框架,利用自适应探索和与环境的互动来提高3D对抗环境中的感知鲁棒性。通过实现平衡即时预测准确性和预测熵最小化的多步骤目标,REIN-EAD可以在多步骤范围内优化防御策略。此外,REIN-EAD涉及一种面向不确定性的奖励形成机制,该机制促进了有效的策略更新,从而减少了计算开销,并在不需要可微分环境的情况下支持实际应用。综合实验验证了REIN-EAD的有效性,证明了攻击成功率的大幅降低,同时保持了不同任务的标准准确性。值得注意的是,REIN-EAD对不可见攻击和自适应攻击表现出强大的泛化能力,使其适用于现实世界的复杂任务,包括3D对象分类、人脸识别和自动驾驶。通过将主动策略学习与具体场景交互相结合,REIN-EAD建立了一种可扩展且适应性强的方法,用于在动态和对抗的3D环境中保护基于dnn的感知系统。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Reinforced Embodied Active Defense: Exploiting Adaptive Interaction for Robust Visual Perception in Adversarial 3D Environments.

Adversarial attacks in 3D environments have emerged as a critical threat to the reliability of visual perception systems, particularly in safety-sensitive applications such as identity verification and autonomous driving. These attacks employ adversarial patches and 3D objects to manipulate deep neural network (DNN) predictions by exploiting vulnerabilities within complex scenes. Existing defense mechanisms, such as adversarial training and purification, primarily employ passive strategies to enhance robustness. However, these approaches often rely on pre-defined assumptions about adversarial tactics, limiting their adaptability in dynamic 3D settings. To address these challenges, we introduce Reinforced Embodied Active Defense (REIN-EAD), a proactive defense framework that leverages adaptive exploration and interaction with the environment to improve perception robustness in 3D adversarial contexts. By implementing a multi-step objective that balances immediate prediction accuracy with predictive entropy minimization, REIN-EAD optimizes defense strategies over a multi-step horizon. Additionally, REIN-EAD involves an uncertainty-oriented reward-shaping mechanism that facilitates efficient policy updates, thereby reducing computational overhead and supporting real-world applicability without the need for differentiable environments. Comprehensive experiments validate the effectiveness of REIN-EAD, demonstrating a substantial reduction in attack success rates while preserving standard accuracy across diverse tasks. Notably, REIN-EAD exhibits robust generalization to unseen and adaptive attacks, making it suitable for real-world complex tasks, including 3D object classification, face recognition and autonomous driving. By integrating proactive policy learning with embodied scene interaction, REIN-EAD establishes a scalable and adaptable approach for securing DNN-based perception systems in dynamic and adversarial 3D environments.

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