114 citations · 848 across the 42 of their papers we have counts for
14 papers · 1 filter
Exposing the Fake: Effective Diffusion-Generated Images Detection
Ruipeng Ma, Jinhao Duan, Fei Kong +2
Image synthesis has seen significant advancements with the advent of diffusion-based generative models like Denoising Diffusion Probabilistic Models (DDPM) and text-to-image diffus…
Unlearnable Examples Give a False Sense of Data Privacy: Understanding and Relearning
Pucheng Dang, Xing Hu, Kaidi Xu +5
Unlearnable examples are proposed to prevent third parties from exploiting unauthorized data, which generates unlearnable examples by adding imperceptible perturbations to public p…
Unlearnable Examples for Diffusion Models: Protect Data from Unauthorized Exploitation
Zhengyue Zhao, Jinhao Duan, Xing Hu +6
Diffusion models have demonstrated remarkable performance in image generation tasks, paving the way for powerful AIGC applications. However, these widely-used generative models can…
Improve Video Representation with Temporal Adversarial Augmentation
Jinhao Duan, Quanfu Fan, Hao Cheng +2
Recent works reveal that adversarial augmentation benefits the generalization of neural networks (NNs) if used in an appropriate manner. In this paper, we introduce Temporal Advers…
Caterpillar: A Pure-MLP Architecture with Shifted-Pillars-Concatenation
Jin Sun, Xiaoshuang Shi, Zhiyuan Wang +3
Modeling in Computer Vision has evolved to MLPs. Vision MLPs naturally lack local modeling capability, to which the simplest treatment is combined with convolutional layers. Convol…
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Jinhao Duan, Fei Kong, Shiqi Wang +2
Diffusion-based generative models have shown great potential for image synthesis, but there is a lack of research on the security and privacy risks they may pose. In this paper, we…
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks
Husheng Han, Xing Hu, Kaidi Xu +7
DNN-based video object detection (VOD) powers autonomous driving and video surveillance industries with rising importance and promising opportunities. However, adversarial patch at…
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers
Husheng Han, Kaidi Xu, Xing Hu +6
Adversarial patch attacks that craft the pixels in a confined region of the input images show their powerful attack effectiveness in physical environments even with noises or defor…
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Shaokai Ye, Kailu Wu, Mu Zhou +6
Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target…
Adversarial T-shirt! Evading Person Detectors in A Physical World
Kaidi Xu, Gaoyuan Zhang, Sijia Liu +6
It is known that deep neural networks (DNNs) are vulnerable to adversarial attacks. The so-called physical adversarial examples deceive DNN-based decisionmakers by attaching advers…
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
Caiwen Ding, Shuo Wang, Ning Liu +3
Deep neural networks (DNNs), as the basis of object detection, will play a key role in the development of future autonomous systems with full autonomy. The autonomous systems have…
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Pu Zhao, Sijia Liu, Pin-Yu Chen +4
Robust machine learning is currently one of the most prominent topics which could potentially help shaping a future of advanced AI platforms that not only perform well in average c…
Interpreting Adversarial Examples by Activation Promotion and Suppression
Kaidi Xu, Sijia Liu, Gaoyuan Zhang +5
It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers. However, i…
Adversarial Robustness vs Model Compression, or Both?
Shaokai Ye, Kaidi Xu, Sijia Liu +6
It is well known that deep neural networks (DNNs) are vulnerable to adversarial attacks, which are implemented by adding crafted perturbations onto benign examples. Min-max robust…