58 citations · 168 across the 34 of their papers we have counts for
17 papers · 1 filter
REVEAL: Robust Evolution of Vision-Language Models for Explainable AI-Video Detection
Qingyuan Liu, Yun-Yun Tsai, Ruijian Zha +4
The rapid advancement of AI-generated video poses challenges to digital authenticity and security. Current detection methods, often trained on specific datasets, struggle with the…
Turns Out I'm Not Real: Towards Robust Detection of AI-Generated Videos
Qingyuan Liu, Pengyuan Shi, Yun-Yun Tsai +2
The impressive achievements of generative models in creating high-quality videos have raised concerns about digital integrity and privacy vulnerabilities. Recent works to combat De…
Interpreting and Controlling Vision Foundation Models via Text Explanations
Haozhe Chen, Junfeng Yang, Carl Vondrick +1
Large-scale pre-trained vision foundation models, such as CLIP, have become de facto backbones for various vision tasks. However, due to their black-box nature, understanding the u…
Test-time Detection and Repair of Adversarial Samples via Masked Autoencoder
Yun-Yun Tsai, Ju-Chin Chao, Albert Wen +4
Training-time defenses, known as adversarial training, incur high training costs and do not generalize to unseen attacks. Test-time defenses solve these issues but most existing te…
Convolutional Visual Prompt for Robust Visual Perception
Yun-Yun Tsai, Chengzhi Mao, Junfeng Yang
Vision models are often vulnerable to out-of-distribution (OOD) samples without adapting. While visual prompts offer a lightweight method of input-space adaptation for large-scale…
Adversarially Robust Video Perception by Seeing Motion
Lingyu Zhang, Chengzhi Mao, Junfeng Yang +1
Despite their excellent performance, state-of-the-art computer vision models often fail when they encounter adversarial examples. Video perception models tend to be more fragile un…
Understanding Zero-Shot Adversarial Robustness for Large-Scale Models
Chengzhi Mao, Scott Geng, Junfeng Yang +2
Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks. Yet imperceptible adversarial perturbations can significantly reduce…
Doubly Right Object Recognition: A Why Prompt for Visual Rationales
Chengzhi Mao, Revant Teotia, Amrutha Sundar +4
Many visual recognition models are evaluated only on their classification accuracy, a metric for which they obtain strong performance. In this paper, we investigate whether compute…
Robust Perception through Equivariance
Chengzhi Mao, Lingyu Zhang, Abhishek Joshi +3
Deep networks for computer vision are not reliable when they encounter adversarial examples. In this paper, we introduce a framework that uses the dense intrinsic constraints in na…
Shadows Shed Light on 3D Objects
Ruoshi Liu, Sachit Menon, Chengzhi Mao +3
3D reconstruction is a fundamental problem in computer vision, and the task is especially challenging when the object to reconstruct is partially or fully occluded. We introduce a…
Causal Transportability for Visual Recognition
Chengzhi Mao, Kevin Xia, James Wang +4
Visual representations underlie object recognition tasks, but they often contain both robust and non-robust features. Our main observation is that image classifiers may perform poo…
Using Multiple Self-Supervised Tasks Improves Model Robustness
Matthew Lawhon, Chengzhi Mao, Junfeng Yang
Deep networks achieve state-of-the-art performance on computer vision tasks, yet they fail under adversarial attacks that are imperceptible to humans. In this paper, we propose a n…
Discrete Representations Strengthen Vision Transformer Robustness
Chengzhi Mao, Lu Jiang, Mostafa Dehghani +3
Vision Transformer (ViT) is emerging as the state-of-the-art architecture for image recognition. While recent studies suggest that ViTs are more robust than their convolutional cou…
Adversarial Attacks are Reversible with Natural Supervision
Chengzhi Mao, Mia Chiquier, Hao Wang +2
We find that images contain intrinsic structure that enables the reversal of many adversarial attacks. Attack vectors cause not only image classifiers to fail, but also collaterall…
Generative Interventions for Causal Learning
Chengzhi Mao, Augustine Cha, Amogh Gupta +3
We introduce a framework for learning robust visual representations that generalize to new viewpoints, backgrounds, and scene contexts. Discriminative models often learn naturally…
Multitask Learning Strengthens Adversarial Robustness
Chengzhi Mao, Amogh Gupta, Vikram Nitin +4
Although deep networks achieve strong accuracy on a range of computer vision benchmarks, they remain vulnerable to adversarial attacks, where imperceptible input perturbations fool…
AdvSPADE: Realistic Unrestricted Attacks for Semantic Segmentation
Guangyu Shen, Chengzhi Mao, Junfeng Yang +1
Due to the inherent robustness of segmentation models, traditional norm-bounded attack methods show limited effect on such type of models. In this paper, we focus on generating unr…