论文标题
PointDP：针对3D点云识别的对抗攻击的扩散驱动净化
PointDP: Diffusion-driven Purification against Adversarial Attacks on 3D Point Cloud Recognition
论文作者
论文摘要
3D点云正在成为许多现实世界应用中的关键数据表示形式，例如自动驾驶，机器人技术和医学成像。尽管深度学习的成功进一步加速了物理世界中3D点云的采用，但深度学习因其易受对抗性攻击而臭名昭著。在这项工作中，我们首先确定最先进的经验防御，对抗性训练，由于梯度混淆，在适用于3D点云模型方面有一个重大限制。我们进一步提出了PointDP，这是一种纯化策略，利用扩散模型来防御3D对抗攻击。我们对六个代表性3D点云体系结构进行了广泛的评估，并利用10+强和适应性攻击来证明其较低的鲁棒性。我们的评估表明，在强烈攻击下，PointDP比最先进的纯化方法取得了明显的鲁棒性。在不久的将来将包括对随机平滑的认证防御措施的结果。
3D Point cloud is becoming a critical data representation in many real-world applications like autonomous driving, robotics, and medical imaging. Although the success of deep learning further accelerates the adoption of 3D point clouds in the physical world, deep learning is notorious for its vulnerability to adversarial attacks. In this work, we first identify that the state-of-the-art empirical defense, adversarial training, has a major limitation in applying to 3D point cloud models due to gradient obfuscation. We further propose PointDP, a purification strategy that leverages diffusion models to defend against 3D adversarial attacks. We extensively evaluate PointDP on six representative 3D point cloud architectures, and leverage 10+ strong and adaptive attacks to demonstrate its lower-bound robustness. Our evaluation shows that PointDP achieves significantly better robustness than state-of-the-art purification methods under strong attacks. Results of certified defenses on randomized smoothing combined with PointDP will be included in the near future.
