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Found 9 papers, 4 papers with code
Jailbreaking Multimodal Large Language Models via Shuffle Inconsistency
Despite achieving some progress, they have a low attack success rate on commercial closed-source MLLMs.
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?
To systematically evaluate VLMs' robustness to real-world 3D variations, we propose AdvDreamer, the first framework that generates physically reproducible adversarial 3D transformation (Adv-3DT) samples from single-view images.
OODFace: Benchmarking Robustness of Face Recognition under Common Corruptions and Appearance Variations
With the rise of deep learning, facial recognition technology has seen extensive research and rapid development.
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applications.
The RoboDrive Challenge: Drive Anytime Anywhere in Any Condition
In the realm of autonomous driving, robust perception under out-of-distribution conditions is paramount for the safe deployment of vehicles.
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models.
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
3D object detection is an important task in autonomous driving to perceive the surroundings.
Benchmarking Robustness of 3D Object Detection to Common Corruptions
3D object detection is an important task in autonomous driving to perceive the surroundings.
ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints
Recent studies have demonstrated that visual recognition models lack robustness to distribution shift.
