401 citations · 1.2k across the 88 of their papers we have counts for
45 papers · 1 filter
UniNDM: A Unified Noise-driven Detection and Mitigation Framework Against Sexual Content in Text-to-Image Generation
Yao Huang, Yitong Sun, Huanran Chen +8
Despite the impressive generative capabilities of text-to-image diffusion models, they remain vulnerable to implicit sexual prompts, where subtle cues disguised as benign terms or…
Benchmarking the Trustworthiness in Multimodal LLMs for Video Understanding
Youze Wang, Zijun Chen, Ruoyu Chen +8
Recent advancements in multimodal large language models for video understanding (videoLLMs) have enhanced their capacity to process complex spatiotemporal data. However, challenges…
AdvDreamer Unveils: Are Vision-Language Models Truly Ready for Real-World 3D Variations?
Shouwei Ruan, Hanqing Liu, Yao Huang +5
Vision Language Models (VLMs) have exhibited remarkable generalization capabilities, yet their robustness in dynamic real-world scenarios remains largely unexplored. To systematica…
Exploring Aleatoric Uncertainty in Object Detection via Vision Foundation Models
Peng Cui, Guande He, Dan Zhang +3
Datasets collected from the open world unavoidably suffer from various forms of randomness or noiseness, leading to the ubiquity of aleatoric (data) uncertainty. Quantifying such u…
Real-world Adversarial Defense against Patch Attacks based on Diffusion Model
Xingxing Wei, Caixin Kang, Yinpeng Dong +4
Adversarial patches present significant challenges to the robustness of deep learning models, making the development of effective defenses become critical for real-world applicatio…
T2VSafetyBench: Evaluating the Safety of Text-to-Video Generative Models
Yibo Miao, Yifan Zhu, Yinpeng Dong +3
The recent development of Sora leads to a new era in text-to-video (T2V) generation. Along with this comes the rising concern about its security risks. The generated videos may con…
AutoBreach: Universal and Adaptive Jailbreaking with Efficient Wordplay-Guided Optimization
Jiawei Chen, Xiao Yang, Zhengwei Fang +4
Despite the widespread application of large language models (LLMs) across various tasks, recent studies indicate that they are susceptible to jailbreak attacks, which can render th…
Omniview-Tuning: Boosting Viewpoint Invariance of Vision-Language Pre-training Models
Shouwei Ruan, Yinpeng Dong, Hanqing Liu +3
Vision-Language Pre-training (VLP) models like CLIP have achieved remarkable success in computer vision and particularly demonstrated superior robustness to distribution shifts of…
Exploring the Transferability of Visual Prompting for Multimodal Large Language Models
Yichi Zhang, Yinpeng Dong, Siyuan Zhang +3
Although Multimodal Large Language Models (MLLMs) have demonstrated promising versatile capabilities, their performance is still inferior to specialized models on downstream tasks,…
FaceCat: Enhancing Face Recognition Security with a Unified Diffusion Model
Jiawei Chen, Xiao Yang, Yinpeng Dong +2
Face anti-spoofing (FAS) and adversarial detection (FAD) have been regarded as critical technologies to ensure the safety of face recognition systems. However, due to limited pract…
Embodied Active Defense: Leveraging Recurrent Feedback to Counter Adversarial Patches
Lingxuan Wu, Xiao Yang, Yinpeng Dong +3
The vulnerability of deep neural networks to adversarial patches has motivated numerous defense strategies for boosting model robustness. However, the prevailing defenses depend on…
Towards Transferable Targeted 3D Adversarial Attack in the Physical World
Yao Huang, Yinpeng Dong, Shouwei Ruan +3
Compared with transferable untargeted attacks, transferable targeted adversarial attacks could specify the misclassification categories of adversarial samples, posing a greater thr…
Machine Vision Therapy: Multimodal Large Language Models Can Enhance Visual Robustness via Denoising In-Context Learning
Zhuo Huang, Chang Liu, Yinpeng Dong +3
Although vision models such as Contrastive Language-Image Pre-Training (CLIP) show impressive generalization performance, their zero-shot robustness is still limited under Out-of-D…
How Robust is Google's Bard to Adversarial Image Attacks?
Yinpeng Dong, Huanran Chen, Jiawei Chen +6
Multimodal Large Language Models (MLLMs) that integrate text and other modalities (especially vision) have achieved unprecedented performance in various multimodal tasks. However,…
Root Pose Decomposition Towards Generic Non-rigid 3D Reconstruction with Monocular Videos
Yikai Wang, Yinpeng Dong, Fuchun Sun +1
This work focuses on the 3D reconstruction of non-rigid objects based on monocular RGB video sequences. Concretely, we aim at building high-fidelity models for generic object categ…
Improving Viewpoint Robustness for Visual Recognition via Adversarial Training
Shouwei Ruan, Yinpeng Dong, Hang Su +3
Viewpoint invariance remains challenging for visual recognition in the 3D world, as altering the viewing directions can significantly impact predictions for the same object. While…
Towards Viewpoint-Invariant Visual Recognition via Adversarial Training
Shouwei Ruan, Yinpeng Dong, Hang Su +3
Visual recognition models are not invariant to viewpoint changes in the 3D world, as different viewing directions can dramatically affect the predictions given the same object. Alt…
Distributional Modeling for Location-Aware Adversarial Patches
Xingxing Wei, Shouwei Ruan, Yinpeng Dong +1
Adversarial patch is one of the important forms of performing adversarial attacks in the physical world. To improve the naturalness and aggressiveness of existing adversarial patch…
DIFFender: Diffusion-Based Adversarial Defense against Patch Attacks
Caixin Kang, Yinpeng Dong, Zhengyi Wang +4
Adversarial attacks, particularly patch attacks, pose significant threats to the robustness and reliability of deep learning models. Developing reliable defenses against patch atta…
Robust Classification via a Single Diffusion Model
Huanran Chen, Yinpeng Dong, Zhengyi Wang +4
Diffusion models have been applied to improve adversarial robustness of image classifiers by purifying the adversarial noises or generating realistic data for adversarial training.…
Towards Effective Adversarial Textured 3D Meshes on Physical Face Recognition
Xiao Yang, Chang Liu, Longlong Xu +5
Face recognition is a prevailing authentication solution in numerous biometric applications. Physical adversarial attacks, as an important surrogate, can identify the weaknesses of…
Compacting Binary Neural Networks by Sparse Kernel Selection
Yikai Wang, Wenbing Huang, Yinpeng Dong +2
Binary Neural Network (BNN) represents convolution weights with 1-bit values, which enhances the efficiency of storage and computation. This paper is motivated by a previously reve…
Benchmarking Robustness of 3D Object Detection to Common Corruptions in Autonomous Driving
Yinpeng Dong, Caixin Kang, Jinlai Zhang +6
3D object detection is an important task in autonomous driving to perceive the surroundings. Despite the excellent performance, the existing 3D detectors lack the robustness to rea…
Understanding the Robustness of 3D Object Detection with Bird's-Eye-View Representations in Autonomous Driving
Zijian Zhu, Yichi Zhang, Hai Chen +5
3D object detection is an essential perception task in autonomous driving to understand the environments. The Bird's-Eye-View (BEV) representations have significantly improved the…