591 citations · 1k across the 33 of their papers we have counts for
20 papers · 1 filter
Ethical-Lens: Curbing Malicious Usages of Open-Source Text-to-Image Models
Yuzhu Cai, Sheng Yin, Yuxi Wei +5
The burgeoning landscape of text-to-image models, exemplified by innovations such as Midjourney and DALLE 3, has revolutionized content creation across diverse sectors. However, th…
Seed Feature Maps-based CNN Models for LEO Satellite Remote Sensing Services
Zhichao Lu, Chuntao Ding, Shangguang Wang +3
Deploying high-performance convolutional neural network (CNN) models on low-earth orbit (LEO) satellites for rapid remote sensing image processing has attracted significant interes…
TFormer: A Transmission-Friendly ViT Model for IoT Devices
Zhichao Lu, Chuntao Ding, Felix Juefei-Xu +3
Deploying high-performance vision transformer (ViT) models on ubiquitous Internet of Things (IoT) devices to provide high-quality vision services will revolutionize the way we live…
JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting
Qing Guo, Xiaoguang Li, Felix Juefei-Xu +3
Image inpainting aims to restore the missing regions of corrupted images and make the recovery result identical to the originally complete image, which is different from the common…
Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer
Lan Fu, Hongkai Yu, Felix Juefei-Xu +3
In recent years, image and video surveillance have made considerable progresses to the Intelligent Transportation Systems (ITS) with the help of deep Convolutional Neural Networks…
DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking
Ziyi Cheng, Xuhong Ren, Felix Juefei-Xu +4
Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking. Recent works mainly focus on constructing effecti…
AdvHaze: Adversarial Haze Attack
Ruijun Gao, Qing Guo, Felix Juefei-Xu +2
In recent years, adversarial attacks have drawn more attention for their value on evaluating and improving the robustness of machine learning models, especially, neural network mod…
Auto-Exposure Fusion for Single-Image Shadow Removal
Lan Fu, Changqing Zhou, Qing Guo +5
Shadow removal is still a challenging task due to its inherent background-dependent and spatial-variant properties, leading to unknown and diverse shadow patterns. Even powerful st…
Fooling LiDAR Perception via Adversarial Trajectory Perturbation
Yiming Li, Congcong Wen, Felix Juefei-Xu +1
LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions. When autonomous vehicl…
Countering Malicious DeepFakes: Survey, Battleground, and Horizon
Felix Juefei-Xu, Run Wang, Yihao Huang +3
The creation or manipulation of facial appearance through deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and…
EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining
Qing Guo, Jingyang Sun, Felix Juefei-Xu +4
Single-image deraining is rather challenging due to the unknown rain model. Existing methods often make specific assumptions of the rain model, which can hardly cover many diverse…
Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack
Yupeng Cheng, Qing Guo, Felix Juefei-Xu +4
Image denoising can remove natural noise that widely exists in images captured by multimedia devices due to low-quality imaging sensors, unstable image transmission processes, or l…
DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms
Hua Qi, Qing Guo, Felix Juefei-Xu +5
As the GAN-based face image and video generation techniques, widely known as DeepFakes, have become more and more matured and realistic, there comes a pressing and urgent demand fo…
FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction
Yihao Huang, Felix Juefei-Xu, Run Wang +7
At this moment, GAN-based image generation methods are still imperfect, whose upsampling design has limitations in leaving some certain artifact patterns in the synthesized image.…
Watch out! Motion is Blurring the Vision of Your Deep Neural Networks
Qing Guo, Felix Juefei-Xu, Xiaofei Xie +5
The state-of-the-art deep neural networks (DNNs) are vulnerable against adversarial examples with additive random-like noise perturbations. While such examples are hardly found in…
FakeLocator: Robust Localization of GAN-Based Face Manipulations
Yihao Huang, Felix Juefei-Xu, Qing Guo +2
Full face synthesis and partial face manipulation by virtue of the generative adversarial networks (GANs) and its variants have raised wide public concerns. In the multi-media fore…
Amora: Black-box Adversarial Morphing Attack
Run Wang, Felix Juefei-Xu, Qing Guo +4
Nowadays, digital facial content manipulation has become ubiquitous and realistic with the success of generative adversarial networks (GANs), making face recognition (FR) systems s…
RankGAN: A Maximum Margin Ranking GAN for Generating Faces
Rahul Dey, Felix Juefei-Xu, Vishnu Naresh Boddeti +1
We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, t…
Perturbative Neural Networks
Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides
Convolutional neural networks are witnessing wide adoption in computer vision systems with numerous applications across a range of visual recognition tasks. Much of this progress i…
Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking
Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides
Traditional generative adversarial networks (GAN) and many of its variants are trained by minimizing the KL or JS-divergence loss that measures how close the generated data distrib…