Monocular Depth Estimators: Vulnerabilities and Attacks
Recent advancements of neural networks lead to reliable monocular depth estimation. Monocular depth estimated techniques have the upper hand over traditional depth estimation techniques as it only needs one image during inference. Depth estimation is one of the essential tasks in robotics, and monocular depth estimation has a wide variety of safety-critical applications like in self-driving cars and surgical devices. Thus, the robustness of such techniques is very crucial. It has been shown in recent works that these deep neural networks are highly vulnerable to adversarial samples for tasks like classification, detection and segmentation. These adversarial samples can completely ruin the output of the system, making their credibility in real-time deployment questionable. In this paper, we investigate the robustness of the most state-of-the-art monocular depth estimation networks against adversarial attacks. Our experiments show that tiny perturbations on an image that are invisible to the naked eye (perturbation attack) and corruption less than about 1% of an image (patch attack) can affect the depth estimation drastically. We introduce a novel deep feature annihilation loss that corrupts the hidden feature space representation forcing the decoder of the network to output poor depth maps. The white-box and black-box test compliments the effectiveness of the proposed attack. We also perform adversarial example transferability tests, mainly cross-data transferability.
Code (0)
등록된 구현이 없습니다.
Tasks
DecoderDepth EstimationMonocular Depth EstimationSelf-Driving CarsSimilar Papers 제목 키워드 기반
Black-box Adversarial Attacks on Monocular Depth Estimation Using Evolutionary Multi-objective Optimization
This paper proposes an adversarial attack method to deep neural networks (DNNs) for monocular depth estimation, i.e., estimating the depth from a single image. Single image depth estimation has improved drastically in re…
Adversarial AttackDepth Estimationimage-classificationImage Classification+1Adversarial Patch Attacks on Monocular Depth Estimation Networks
Thanks to the excellent learning capability of deep convolutional neural networks (CNN), monocular depth estimation using CNNs has achieved great success in recent years. However, depth estimation from a monocular image …
Adversarial AttackDepth EstimationMonocular Depth EstimationAdversarial Attacks on Monocular Pose Estimation
Advances in deep learning have resulted in steady progress in computer vision with improved accuracy on tasks such as object detection and semantic segmentation. Nevertheless, deep neural networks are vulnerable to adver…
Depth EstimationMonocular Depth EstimationObject DetectionPose Estimation+2Repurposing Diffusion-Based Image Generators for Monocular Depth Estimation
Monocular depth estimation is a fundamental computer vision task. Recovering 3D depth from a single image is geometrically ill-posed and requires scene understanding, so it is not surprising that the rise of deep learnin…
Depth EstimationGPUMonocular Depth EstimationScene Understanding+1Targeted Adversarial Perturbations for Monocular Depth Prediction
We study the effect of adversarial perturbations on the task of monocular depth prediction. Specifically, we explore the ability of small, imperceptible additive perturbations to selectively alter the perceived geometry …
Adversarial AttackAdversarial DefenseDepth EstimationDepth Prediction+2