Search Results for author: Felix Juefei-Xu

Found 39 papers, 12 papers with code

Adversarial Relighting against Face Recognition

no code implementations18 Aug 2021 Ruijun Gao, Qing Guo, Qian Zhang, Felix Juefei-Xu, Hongkai Yu, Wei Feng

To this end, we first propose the physical model-based adversarial relighting attack (ARA) denoted as albedo-quotient-based adversarial relighting attack (AQ-ARA).

Adversarial Attack Face Recognition

CarveNet: Carving Point-Block for Complex 3D Shape Completion

no code implementations28 Jul 2021 Qing Guo, Zhijie Wang, Felix Juefei-Xu, Di Lin, Lei Ma, Wei Feng, Yang Liu

3D point cloud completion is very challenging because it heavily relies on the accurate understanding of the complex 3D shapes (e. g., high-curvature, concave/convex, and hollowed-out 3D shapes) and the unknown & diverse patterns of the partially available point clouds.

Data Augmentation Point Cloud Completion

Learning to Adversarially Blur Visual Object Tracking

no code implementations26 Jul 2021 Qing Guo, Ziyi Cheng, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yang Liu, Jianjun Zhao

In this work, we explore the robustness of visual object trackers against motion blur from a new angle, i. e., adversarial blur attack (ABA).

Visual Object Tracking Visual Tracking

AdvFilter: Predictive Perturbation-aware Filtering against Adversarial Attack via Multi-domain Learning

no code implementations14 Jul 2021 Yihao Huang, Qing Guo, Felix Juefei-Xu, Lei Ma, Weikai Miao, Yang Liu, Geguang Pu

To this end, we first comprehensively investigate two kinds of pixel denoising methods for adversarial robustness enhancement (i. e., existing additive-based and unexplored filtering-based methods) under the loss functions of image-level and semantic-level restorations, respectively, showing that pixel-wise filtering can obtain much higher image quality (e. g., higher PSNR) as well as higher robustness (e. g., higher accuracy on adversarial examples) than existing pixel-wise additive-based method.

Adversarial Attack Denoising

JPGNet: Joint Predictive Filtering and Generative Network for Image Inpainting

1 code implementation9 Jul 2021 Qing Guo, Xiaoguang Li, Felix Juefei-Xu, Hongkai Yu, Yang Liu, Song Wang

Note that, our method as a novel framework for the image inpainting problem can benefit any existing generation-based methods.

Image Inpainting

Sparta: Spatially Attentive and Adversarially Robust Activation

no code implementations18 May 2021 Qing Guo, Felix Juefei-Xu, Changqing Zhou, Yang Liu, Song Wang

In both cases, Sparta leads to CNNs with higher robustness than the vanilla ReLU, verifying the flexibility and versatility of the proposed method.

AVA: Adversarial Vignetting Attack against Visual Recognition

no code implementations12 May 2021 Binyu Tian, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Xiaohong Li, Yang Liu

Moreover, we propose the geometry-aware level-set optimization method to solve the adversarial vignetting regions and physical parameters jointly.

Let There be Light: Improved Traffic Surveillance via Detail Preserving Night-to-Day Transfer

no code implementations11 May 2021 Lan Fu, Hongkai Yu, Felix Juefei-Xu, Jinlong Li, Qing Guo, Song Wang

As one of the state-of-the-art perception approaches, detecting the interested objects in each frame of video surveillance is widely desired by ITS.

Object Detection

AdvHaze: Adversarial Haze Attack

no code implementations28 Apr 2021 Ruijun Gao, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng

We also visualize the correlation matrices, which inspire us to jointly apply different perturbations to improve the success rate of the attack.

Adversarial Attack

DeepMix: Online Auto Data Augmentation for Robust Visual Object Tracking

no code implementations23 Apr 2021 Ziyi Cheng, Xuhong Ren, Felix Juefei-Xu, Wanli Xue, Qing Guo, Lei Ma, Jianjun Zhao

Online updating of the object model via samples from historical frames is of great importance for accurate visual object tracking.

Data Augmentation Visual Object Tracking

Fooling LiDAR Perception via Adversarial Trajectory Perturbation

1 code implementation29 Mar 2021 Yiming Li, Congcong Wen, Felix Juefei-Xu, Chen Feng

LiDAR point clouds collected from a moving vehicle are functions of its trajectories, because the sensor motion needs to be compensated to avoid distortions.

3D Object Detection Autonomous Vehicles +1

Auto-Exposure Fusion for Single-Image Shadow Removal

1 code implementation CVPR 2021 Lan Fu, Changqing Zhou, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Wei Feng, Yang Liu, Song Wang

We conduct extensive experiments on the ISTD, ISTD+, and SRD datasets to validate our method's effectiveness and show better performance in shadow regions and comparable performance in non-shadow regions over the state-of-the-art methods.

Image Shadow Removal Shadow Removal

Countering Malicious DeepFakes: Survey, Battleground, and Horizon

1 code implementation27 Feb 2021 Felix Juefei-Xu, Run Wang, Yihao Huang, Qing Guo, Lei Ma, Yang Liu

The creation and the manipulation of facial appearance via deep generative approaches, known as DeepFake, have achieved significant progress and promoted a wide range of benign and malicious applications.

DeepFake Detection Face Swapping

Sparta: Spatially Attentive and Adversarially Robust Activations

no code implementations1 Jan 2021 Qing Guo, Felix Juefei-Xu, Changqing Zhou, Lei Ma, Xiaofei Xie, Wei Feng, Yang Liu

Moreover, comprehensive evaluations have demonstrated two important properties of our method: First, superior transferability across DNNs.

Denoising

Adversarial Exposure Attack on Diabetic Retinopathy Imagery

no code implementations19 Sep 2020 Yupeng Cheng, Felix Juefei-Xu, Qing Guo, Huazhu Fu, Xiaofei Xie, Shang-Wei Lin, Weisi Lin, Yang Liu

In this paper, we study this problem from the viewpoint of adversarial attack and identify a totally new task, i. e., adversarial exposure attack generating adversarial images by tuning image exposure to mislead the DNNs with significantly high transferability.

Adversarial Attack

Bias Field Poses a Threat to DNN-based X-Ray Recognition

no code implementations19 Sep 2020 Binyu Tian, Qing Guo, Felix Juefei-Xu, Wen Le Chan, Yupeng Cheng, Xiaohong Li, Xiaofei Xie, Shengchao Qin

Our method reveals the potential threat to the DNN-based X-ray automated diagnosis and can definitely benefit the development of bias-field-robust automated diagnosis system.

Adversarial Attack

EfficientDeRain: Learning Pixel-wise Dilation Filtering for High-Efficiency Single-Image Deraining

1 code implementation19 Sep 2020 Qing Guo, Jingyang Sun, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Wei Feng, Yang Liu

To fill this gap, in this paper, we regard the single-image deraining as a general image-enhancing problem and originally propose a model-free deraining method, i. e., EfficientDeRain, which is able to process a rainy image within 10~ms (i. e., around 6~ms on average), over 80 times faster than the state-of-the-art method (i. e., RCDNet), while achieving similar de-rain effects.

Data Augmentation Single Image Deraining

Making Images Undiscoverable from Co-Saliency Detection

no code implementations19 Sep 2020 Ruijun Gao, Qing Guo, Felix Juefei-Xu, Hongkai Yu, Huazhu Fu, Wei Feng, Yang Liu, Song Wang

In this paper, we address this problem from the perspective of adversarial attacks and identify a novel task: adversarial co-saliency attack.

Adversarial Attack Co-Salient Object Detection +2

It's Raining Cats or Dogs? Adversarial Rain Attack on DNN Perception

no code implementations19 Sep 2020 Liming Zhai, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Lei Ma, Wei Feng, Shengchao Qin, Yang Liu

With this generator, we further propose the adversarial rain attack against the image classification and object detection, where the rain factors are guided by the various DNNs.

Adversarial Attack Autonomous Driving +3

FakeRetouch: Evading DeepFakes Detection via the Guidance of Deliberate Noise

no code implementations19 Sep 2020 Yihao Huang, Felix Juefei-Xu, Qing Guo, Xiaofei Xie, Lei Ma, Weikai Miao, Yang Liu, Geguang Pu

Thus we use a combination of additive noise and deep image filtering to reconstruct the fake images, and we name our method FakeRetouch.

DeepFake Detection Face Swapping +1

Pasadena: Perceptually Aware and Stealthy Adversarial Denoise Attack

no code implementations14 Jul 2020 Yupeng Cheng, Qing Guo, Felix Juefei-Xu, Wei Feng, Shang-Wei Lin, Weisi Lin, Yang Liu

To this end, we initiate the very first attempt to study this problem from the perspective of adversarial attack and propose the adversarial denoise attack.

Adversarial Attack Common Sense Reasoning +2

FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

no code implementations13 Jun 2020 Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Lei Ma, Xiaofei Xie, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu

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.

DeepFake Detection Face Swapping +2

DeepRhythm: Exposing DeepFakes with Attentional Visual Heartbeat Rhythms

no code implementations13 Jun 2020 Hua Qi, Qing Guo, Felix Juefei-Xu, Xiaofei Xie, Lei Ma, Wei Feng, Yang Liu, Jianjun Zhao

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 for effective DeepFakes detectors.

DeepFake Detection Face Swapping +2

FakeLocator: Robust Localization of GAN-Based Face Manipulations

no code implementations27 Jan 2020 Yihao Huang, Felix Juefei-Xu, Run Wang, Qing Guo, Xiaofei Xie, Lei Ma, Jianwen Li, Weikai Miao, Yang Liu, Geguang Pu

To the best of our knowledge, this is the very first attempt to solve the GAN-based fake localization problem with a gray-scale fakeness prediction map that preserves more information of fake regions.

Face Generation Semantic Segmentation

Amora: Black-box Adversarial Morphing Attack

no code implementations9 Dec 2019 Run Wang, Felix Juefei-Xu, Qing Guo, Yihao Huang, Xiaofei Xie, Lei Ma, Yang Liu

In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by manipulating facial content, called \textbf{\underline{a}dversarial \underline{mor}phing \underline{a}ttack} (a. k. a.

Adversarial Attack Dictionary Learning +3

SPARK: Spatial-aware Online Incremental Attack Against Visual Tracking

1 code implementation ECCV 2020 Qing Guo, Xiaofei Xie, Felix Juefei-Xu, Lei Ma, Zhongguo Li, Wanli Xue, Wei Feng, Yang Liu

We identify that online object tracking poses two new challenges: 1) it is difficult to generate imperceptible perturbations that can transfer across frames, and 2) real-time trackers require the attack to satisfy a certain level of efficiency.

Adversarial Attack Video Object Tracking +2

FakeSpotter: A Simple yet Robust Baseline for Spotting AI-Synthesized Fake Faces

no code implementations13 Sep 2019 Run Wang, Felix Juefei-Xu, Lei Ma, Xiaofei Xie, Yihao Huang, Jian Wang, Yang Liu

In recent years, generative adversarial networks (GANs) and its variants have achieved unprecedented success in image synthesis.

Face Detection Face Recognition +2

RankGAN: A Maximum Margin Ranking GAN for Generating Faces

1 code implementation19 Dec 2018 Rahul Dey, Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides

We present a new stage-wise learning paradigm for training generative adversarial networks (GANs).

Face Generation

Secure Deep Learning Engineering: A Software Quality Assurance Perspective

no code implementations10 Oct 2018 Lei Ma, Felix Juefei-Xu, Minhui Xue, Qiang Hu, Sen Chen, Bo Li, Yang Liu, Jianjun Zhao, Jianxiong Yin, Simon See

Over the past decades, deep learning (DL) systems have achieved tremendous success and gained great popularity in various applications, such as intelligent machines, image processing, speech processing, and medical diagnostics.

Metamorphic Relation Based Adversarial Attacks on Differentiable Neural Computer

no code implementations7 Sep 2018 Alvin Chan, Lei Ma, Felix Juefei-Xu, Xiaofei Xie, Yang Liu, Yew Soon Ong

Deep neural networks (DNN), while becoming the driving force of many novel technology and achieving tremendous success in many cutting-edge applications, are still vulnerable to adversarial attacks.

Question Answering

DeepHunter: Hunting Deep Neural Network Defects via Coverage-Guided Fuzzing

no code implementations4 Sep 2018 Xiaofei Xie, Lei Ma, Felix Juefei-Xu, Hongxu Chen, Minhui Xue, Bo Li, Yang Liu, Jianjun Zhao, Jianxiong Yin, Simon See

In company with the data explosion over the past decade, deep neural network (DNN) based software has experienced unprecedented leap and is becoming the key driving force of many novel industrial applications, including many safety-critical scenarios such as autonomous driving.

Autonomous Driving Quantization

Perturbative Neural Networks

3 code implementations CVPR 2018 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.

DeepMutation: Mutation Testing of Deep Learning Systems

4 code implementations14 May 2018 Lei Ma, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Felix Juefei-Xu, Chao Xie, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang

To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i. e., training data and training programs).

Software Engineering

DeepGauge: Multi-Granularity Testing Criteria for Deep Learning Systems

no code implementations20 Mar 2018 Lei Ma, Felix Juefei-Xu, Fuyuan Zhang, Jiyuan Sun, Minhui Xue, Bo Li, Chunyang Chen, Ting Su, Li Li, Yang Liu, Jianjun Zhao, Yadong Wang

Deep learning (DL) defines a new data-driven programming paradigm that constructs the internal system logic of a crafted neuron network through a set of training data.

Adversarial Attack Defect Detection

Gang of GANs: Generative Adversarial Networks with Maximum Margin Ranking

1 code implementation17 Apr 2017 Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides

A recent advance called the WGAN based on Wasserstein distance can improve on the KL and JS-divergence based GANs, and alleviate the gradient vanishing, instability, and mode collapse issues that are common in the GAN training.

Local Binary Convolutional Neural Networks

6 code implementations CVPR 2017 Felix Juefei-Xu, Vishnu Naresh Boddeti, Marios Savvides

We propose local binary convolution (LBC), an efficient alternative to convolutional layers in standard convolutional neural networks (CNN).

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