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Found 6 papers, 2 papers with code
Turning a Curse into a Blessing: Enabling In-Distribution-Data-Free Backdoor Removal via Stabilized Model Inversion
Our work is the first to provide a thorough understanding of leveraging model inversion for effective backdoor removal by addressing key questions about reconstructed samples' properties, perceptual similarity, and the potential presence of backdoor triggers.
Adversarial Unlearning of Backdoors via Implicit Hypergradient
Particularly, its performance is more robust to the variation on triggers, attack settings, poison ratio, and clean data size.
Sensor Adversarial Traits: Analyzing Robustness of 3D Object Detection Sensor Fusion Models
A critical aspect of autonomous vehicles (AVs) is the object detection stage, which is increasingly being performed with sensor fusion models: multimodal 3D object detection models which utilize both 2D RGB image data and 3D data from a LIDAR sensor as inputs.
Rethinking the Backdoor Attacks' Triggers: A Frequency Perspective
Acknowledging previous attacks' weaknesses, we propose a practical way to create smooth backdoor triggers without high-frequency artifacts and study their detectability.
Minority Reports Defense: Defending Against Adversarial Patches
We propose a defense against patch attacks based on partially occluding the image around each candidate patch location, so that a few occlusions each completely hide the patch.
Adversarial Sensor Attack on LiDAR-based Perception in Autonomous Driving
In contrast to prior work that concentrates on camera-based perception, in this work we perform the first security study of LiDAR-based perception in AV settings, which is highly important but unexplored.
