The outstanding performance of deep neural networks has promoted deep learning
applications in a broad set of domains. However, the potential risks caused by adversarial …

Concerns about cybersecurity and attack methods have risen in the information age. Many
techniques are used to detect or deter attacks, such as intrusion detection systems (IDSs) …

In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite
various prior studies on its security issues, all of them only consider attacks on camera-or …

In Autonomous Vehicles (AVs), one fundamental pillar is perception, which leverages
sensors like cameras and LiDARs (Light Detection and Ranging) to understand the driving …

Deep learning has substantially boosted the performance of Monocular Depth Estimation
(MDE), a critical component in fully vision-based autonomous driving (AD) systems (eg …

Adversarial attacks on machine learning models have seen increasing interest in the past
years. By making only subtle changes to the input of a convolutional neural network, the …

Abstract 3D object detection is an essential perception task in autonomous driving to
understand the environments. The Bird's-Eye-View (BEV) representations have significantly …

We investigate a new method for injecting backdoors into machine learning models, based
on compromising the loss-value computation in the model-training code. We use it to …

We present an approach to mitigating the risks of malicious image editing posed by large
diffusion models. The key idea is to immunize images so as to make them resistant to …

It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-
called physical adversarial examples deceive DNN-based decision makers by attaching …