Adversarial Attacks on Monocular Depth Estimation

Abstract

Recent advances of deep learning have brought exceptional performance on many computer vision tasks such as semantic segmentation and depth estimation. However, the vulnerability of deep neural networks towards adversarial examples have caused grave concerns for real-world deployment. In this paper, we present to the best of our knowledge the first systematic study of adversarial attacks on monocular depth estimation, an important task of 3D scene understanding in scenarios such as autonomous driving and robot navigation. In order to understand the impact of adversarial attacks on depth estimation, we first define a taxonomy of different attack scenarios for depth estimation, including non-targeted attacks, targeted attacks and universal attacks. We then adapt several state-of-the-art attack methods for classification on the field of depth estimation. Besides, multi-task attacks are introduced to further improve the attack performance for universal attacks. Experimental results show that it is possible to generate significant errors on depth estimation. In particular, we demonstrate that our methods can conduct targeted attacks on given objects (such as a car), resulting in depth estimation 3-4x away from the ground truth (e.g., from 20m to 80m).

References in corpus (11)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
• Depth Map Prediction from a Single Image using a Multi-Scale Deep Network
• ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Models
• Learning Depth from Single Monocular Images Using Deep Convolutional Neural Fields
• Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples
• GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks
• Understanding Adversarial Training: Increasing Local Stability of Neural Nets through Robust Optimization
• Robustness May Be at Odds with Accuracy
• Certifying Some Distributional Robustness with Principled Adversarial Training
• Analysis of Deep Networks for Monocular Depth Estimation Through Adversarial Attacks with Proposal of a Defense Method

Cited by in corpus (5)

• Adversarial Patch Attacks on Monocular Depth Estimation Networks
• Regional Homogeneity: Towards Learning Transferable Universal Adversarial Perturbations Against Defenses
• Adversarial Attacks on Monocular Pose Estimation
• Monocular Depth Estimators: Vulnerabilities and Attacks
• Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization