Search Results for author: Lei Ma

Found 40 papers, 8 papers with code

ArchRepair: Block-Level Architecture-Oriented Repairing for Deep Neural Networks

no code implementations26 Nov 2021 Hua Qi, Zhijie Wang, Qing Guo, Jianlang Chen, Felix Juefei-Xu, Lei Ma, Jianjun Zhao

In this work, as the first attempt, we initiate to repair DNNs by jointly optimizing the architecture and weights at a higher (i. e., block) level.

Fine-tuning

GraphSearchNet: Enhancing GNNs via Capturing Global Dependency for Semantic Code Search

no code implementations4 Nov 2021 Shangqing Liu, Xiaofei Xie, Lei Ma, JingKai Siow, Yang Liu

Code search aims to retrieve the relevant code fragments based on a natural language query to improve the software productivity and quality.

Code Search

SCFlow: Optical Flow Estimation for Spiking Camera

no code implementations8 Oct 2021 Liwen Hu, Rui Zhao, Ziluo Ding, Ruiqin Xiong, Lei Ma, Tiejun Huang

Optical flow estimation has achieved remarkable success in image-based and event-based vision, but % existing methods cannot be directly applied in spike stream from spiking camera.

Event-based vision Motion Estimation +1

SkullEngine: A Multi-stage CNN Framework for Collaborative CBCT Image Segmentation and Landmark Detection

no code implementations7 Oct 2021 Qin Liu, Han Deng, Chunfeng Lian, Xiaoyang Chen, Deqiang Xiao, Lei Ma, Xu Chen, Tianshu Kuang, Jaime Gateno, Pew-Thian Yap, James J. Xia

We propose a multi-stage coarse-to-fine CNN-based framework, called SkullEngine, for high-resolution segmentation and large-scale landmark detection through a collaborative, integrated, and scalable JSD model and three segmentation and landmark detection refinement models.

Semantic Segmentation

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

1 code implementation ICCV 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, 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 Adversarial Robustness +1

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

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

AttackDist: Characterizing Zero-day Adversarial Samples by Counter Attack

no code implementations1 Jan 2021 Simin Chen, Zihe Song, Lei Ma, Cong Liu, Wei Yang

We first theoretically clarify under which condition AttackDist can provide a certified detecting performance, then show that a potential application of AttackDist is distinguishing zero-day adversarial examples without knowing the mechanisms of new attacks.

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

Dodging DeepFake Detection via Implicit Spatial-Domain Notch Filtering

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

We believe that producing DeepFakes that are highly realistic and ``detection evasive'' can serve the ultimate goal of improving future generation DeepFake detection capabilities.

DeepFake Detection Face Swapping +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

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

FakePolisher: Making DeepFakes More Detection-Evasive by Shallow Reconstruction

1 code implementation13 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

Satellite-Terrestrial Channel Characterization in High-Speed Railway Environment at 22.6 GHz

no code implementations11 Jun 2020 Lei Ma, Ke Guan, Dong Yan, Danping He, Nuno R. Leonor, Bo Ai, Junhyeong Kim

In this paper, the satellite-terrestrial channel at 22. 6 GHz is characterized for a typical high-speed railway (HSR) environment.

Stealthy and Efficient Adversarial Attacks against Deep Reinforcement Learning

no code implementations14 May 2020 Jianwen Sun, Tianwei Zhang, Xiaofei Xie, Lei Ma, Yan Zheng, Kangjie Chen, Yang Liu

Adversarial attacks against conventional Deep Learning (DL) systems and algorithms have been widely studied, and various defenses were proposed.

Adversarial Attack

Towards Characterizing Adversarial Defects of Deep Learning Software from the Lens of Uncertainty

no code implementations24 Apr 2020 Xiyue Zhang, Xiaofei Xie, Lei Ma, Xiaoning Du, Qiang Hu, Yang Liu, Jianjun Zhao, Meng Sun

Based on this, we propose an automated testing technique to generate multiple types of uncommon AEs and BEs that are largely missed by existing techniques.

Adversarial Attack

Byzantine-resilient Decentralized Stochastic Gradient Descent

no code implementations20 Feb 2020 Shangwei Guo, Tianwei Zhang, Han Yu, Xiaofei Xie, Lei Ma, Tao Xiang, Yang Liu

It guarantees that each benign node in a decentralized system can train a correct model under very strong Byzantine attacks with an arbitrary number of faulty nodes.

Edge-computing Image Classification

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

An Empirical Study towards Characterizing Deep Learning Development and Deployment across Different Frameworks and Platforms

no code implementations15 Sep 2019 Qianyu Guo, Sen Chen, Xiaofei Xie, Lei Ma, Qiang Hu, Hongtao Liu, Yang Liu, Jianjun Zhao, Xiaohong Li

However, the differences in architecture designs and implementations of existing frameworks and platforms bring new challenges for DL software development and deployment.

Adversarial Attack Adversarial Robustness +1

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

Machine Learning Testing: Survey, Landscapes and Horizons

no code implementations19 Jun 2019 Jie M. Zhang, Mark Harman, Lei Ma, Yang Liu

This paper provides a comprehensive survey of Machine Learning Testing (ML testing) research.

Autonomous Driving Fairness +2

Hierarchy Neighborhood Discriminative Hashing for An Unified View of Single-Label and Multi-Label Image retrieval

no code implementations10 Jan 2019 Lei Ma, Hongliang Li, Qingbo Wu, Fanman Meng, King Ngi Ngan

Finally, we propose a hierarchy neighborhood discriminative hashing loss to unify the single-label and multilabel image retrieval problem with a one-stream deep neural network architecture.

Multi-Label Image Retrieval Semantic Similarity +1

DeepCruiser: Automated Guided Testing for Stateful Deep Learning Systems

no code implementations13 Dec 2018 Xiaoning Du, Xiaofei Xie, Yi Li, Lei Ma, Jianjun Zhao, Yang Liu

Our in-depth evaluation on a state-of-the-art speech-to-text DL system demonstrates the effectiveness of our technique in improving quality and reliability of stateful DL systems.

An Orchestrated Empirical Study on Deep Learning Frameworks and Platforms

no code implementations13 Nov 2018 Qianyu Guo, Xiaofei Xie, Lei Ma, Qiang Hu, Ruitao Feng, Li Li, Yang Liu, Jianjun Zhao, Xiaohong Li

Up to the present, it still lacks a comprehensive study on how current diverse DL frameworks and platforms influence the DL software development process.

Autonomous Driving

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

Combinatorial Testing for Deep Learning Systems

no code implementations20 Jun 2018 Lei Ma, Fuyuan Zhang, Minhui Xue, Bo Li, Yang Liu, Jianjun Zhao, Yadong Wang

Deep learning (DL) has achieved remarkable progress over the past decade and been widely applied to many safety-critical applications.

Defect Detection

DeepLaser: Practical Fault Attack on Deep Neural Networks

no code implementations15 Jun 2018 Jakub Breier, Xiaolu Hou, Dirmanto Jap, Lei Ma, Shivam Bhasin, Yang Liu

As deep learning systems are widely adopted in safety- and security-critical applications, such as autonomous vehicles, banking systems, etc., malicious faults and attacks become a tremendous concern, which potentially could lead to catastrophic consequences.

Autonomous Vehicles

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

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