Search Results for author: Cong Liu

Found 20 papers, 5 papers with code

Practical Poisoning Attacks on Neural Networks

no code implementations ECCV 2020 Junfeng Guo, Cong Liu

Importantly, we show that the effectiveness of BlackCard can be intuitively guaranteed by a set of analytical reasoning and observations, through exploiting an essential characteristic of gradient-descent optimization which is pervasively adopted in DNN models.

Data Poisoning

Automatically eliminating seam lines with Poisson editing in complex relative radiometric normalization mosaicking scenarios

no code implementations14 Jun 2021 Shiqi Liu, Jie Lian, Xuchen Zhan, Cong Liu, Yuze Tian, Hongwei Duan

Relative radiometric normalization (RRN) mosaicking among multiple remote sensing images is crucial for the downstream tasks, including map-making, image recognition, semantic segmentation, and change detection.

Semantic Segmentation

Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition

1 code implementation7 May 2021 Bangjie Yin, Wenxuan Wang, Taiping Yao, Junfeng Guo, Zelun Kong, Shouhong Ding, Jilin Li, Cong Liu

Deep neural networks, particularly face recognition models, have been shown to be vulnerable to both digital and physical adversarial examples.

Adversarial Attack Face Generation +2

PredCoin: Defense against Query-based Hard-label Attack

no code implementations4 Feb 2021 Junfeng Guo, Yaswanth Yadlapalli, Thiele Lothar, Ang Li, Cong Liu

PredCoin poisons the gradient estimation step, an essential component of most QBHL attacks.

Hard-label Attack

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.

Tensor Completion via Convolutional Sparse Coding Regularization

no code implementations2 Dec 2020 Zhebin Wu, Tianchi Liao, Chuan Chen, Cong Liu, Zibin Zheng, Xiongjun Zhang

On the contrary, in the field of signal processing, Convolutional Sparse Coding (CSC) can provide a good representation of the high-frequency component of the image, which is generally associated with the detail component of the data.

CAVA: A Visual Analytics System for Exploratory Columnar Data Augmentation Using Knowledge Graphs

1 code implementation7 Sep 2020 Dylan Cashman, Shenyu Xu, Subhajit Das, Florian Heimerl, Cong Liu, Shah Rukh Humayoun, Michael Gleicher, Alex Endert, Remco Chang

In this paper, we present CAVA, a system that integrates data curation and data augmentation with the traditional data exploration and analysis tasks, enabling information foraging in-situ during analysis.

Data Augmentation Knowledge Graphs

Deep Partial Updating

no code implementations6 Jul 2020 Zhongnan Qu, Cong Liu, Junfeng Guo, Lothar Thiele

Emerging edge intelligence applications require the server to continuously retrain and update deep neural networks deployed on remote edge nodes in order to leverage newly collected data samples.

PoisHygiene: Detecting and Mitigating Poisoning Attacks in Neural Networks

no code implementations24 Mar 2020 Junfeng Guo, Ting Wang, Cong Liu

Being able to detect and mitigate poisoning attacks, typically categorized into backdoor and adversarial poisoning (AP), is critical in enabling safe adoption of DNNs in many application domains.

Data Poisoning

PhysGAN: Generating Physical-World-Resilient Adversarial Examples for Autonomous Driving

no code implementations CVPR 2020 Zelun Kong, Junfeng Guo, Ang Li, Cong Liu

We compare PhysGAN with a set of state-of-the-art baseline methods including several of our self-designed ones, which further demonstrate the robustness and efficacy of our approach.

Autonomous Driving Image Classification

Episodic Training for Domain Generalization

2 code implementations ICCV 2019 Da Li, Jianshu Zhang, Yongxin Yang, Cong Liu, Yi-Zhe Song, Timothy M. Hospedales

In this paper, we build on this strong baseline by designing an episodic training procedure that trains a single deep network in a way that exposes it to the domain shift that characterises a novel domain at runtime.

Domain Generalization

DeepBillboard: Systematic Physical-World Testing of Autonomous Driving Systems

no code implementations27 Dec 2018 Husheng Zhou, Wei Li, Yuankun Zhu, Yuqun Zhang, Bei Yu, Lingming Zhang, Cong Liu

Furthermore, DeepBillboard is sufficiently robust and resilient for generating physical-world adversarial billboard tests for real-world driving under various weather conditions.

Autonomous Driving

DeepRoad: GAN-based Metamorphic Autonomous Driving System Testing

1 code implementation7 Feb 2018 Mengshi Zhang, Yuqun Zhang, Lingming Zhang, Cong Liu, Sarfraz Khurshid

In this paper, we propose DeepRoad, an unsupervised framework to automatically generate large amounts of accurate driving scenes to test the consistency of DNN-based autonomous driving systems across different scenes.

Software Engineering

BENCHIP: Benchmarking Intelligence Processors

no code implementations23 Oct 2017 Jinhua Tao, Zidong Du, Qi Guo, Huiying Lan, Lei Zhang, Shengyuan Zhou, Lingjie Xu, Cong Liu, Haifeng Liu, Shan Tang, Allen Rush, Willian Chen, Shaoli Liu, Yunji Chen, Tianshi Chen

The variety of emerging intelligence processors requires standard benchmarks for fair comparison and system optimization (in both software and hardware).

Improved Word Embeddings with Implicit Structure Information

no code implementations COLING 2016 Jie Shen, Cong Liu

Distributed word representation is an efficient method for capturing semantic and syntactic word relations.

Dependency Parsing Language Modelling +5

Modelling Sentence Pairs with Tree-structured Attentive Encoder

1 code implementation COLING 2016 Yao Zhou, Cong Liu, Yan Pan

We describe an attentive encoder that combines tree-structured recursive neural networks and sequential recurrent neural networks for modelling sentence pairs.

Paraphrase Identification Semantic Similarity

Feedforward Sequential Memory Networks: A New Structure to Learn Long-term Dependency

no code implementations28 Dec 2015 Shiliang Zhang, Cong Liu, Hui Jiang, Si Wei, Li-Rong Dai, Yu Hu

In this paper, we propose a novel neural network structure, namely \emph{feedforward sequential memory networks (FSMN)}, to model long-term dependency in time series without using recurrent feedback.

Language Modelling Speech Recognition +1

A Divide-and-Conquer Method for Scalable Low-Rank Latent Matrix Pursuit

no code implementations CVPR 2013 Yan Pan, Hanjiang Lai, Cong Liu, Shuicheng Yan

To address this issue, we provide a scalable solution for large-scale low-rank latent matrix pursuit by a divide-andconquer method.

Event Detection

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