Search Results for author: Kaidi Xu

Found 30 papers, 14 papers with code

Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks

no code implementations ICML 2020 Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Mingyi Hong, Una-May O'Reilly

In this paper, we study the problem of constrained min-max optimization in a black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.

Toward Robust Spiking Neural Network Against Adversarial Perturbation

no code implementations12 Apr 2022 Ling Liang, Kaidi Xu, Xing Hu, Lei Deng, Yuan Xie

To the best of our knowledge, this is the first analysis on robust training of SNNs.

ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers

no code implementations NeurIPS 2021 Husheng Han, Kaidi Xu, Xing Hu, Xiaobing Chen, Ling Liang, Zidong Du, Qi Guo, Yanzhi Wang, Yunji Chen

Our experimental results show that the certified accuracy is increased from 36. 3% (the state-of-the-art certified detection) to 60. 4% on the ImageNet dataset, largely pushing the certified defenses for practical use.

Generating Realistic Physical Adversarial Examplesby Patch Transformer Network

no code implementations29 Sep 2021 Quanfu Fan, Kaidi Xu, Chun-Fu Chen, Sijia Liu, Gaoyuan Zhang, David Daniel Cox, Xue Lin

Physical adversarial attacks apply carefully crafted adversarial perturbations onto real objects to maliciously alter the prediction of object classifiers or detectors.

A Branch and Bound Framework for Stronger Adversarial Attacks of ReLU Networks

no code implementations29 Sep 2021 huan zhang, Shiqi Wang, Kaidi Xu, Yihan Wang, Suman Jana, Cho-Jui Hsieh, J Zico Kolter

In this work, we formulate an adversarial attack using a branch-and-bound (BaB) procedure on ReLU neural networks and search adversarial examples in the activation space corresponding to binary variables in a mixed integer programming (MIP) formulation.

Adversarial Attack

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification

no code implementations NeurIPS 2021 Shiqi Wang, huan zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J Zico Kolter

We develop $\beta$-CROWN, a new bound propagation based method that can fully encode neuron split constraints in branch-and-bound (BaB) based complete verification via optimizable parameters $\beta$.

Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression

no code implementations15 Jun 2021 Sheng Lin, Wei Jiang, Wei Wang, Kaidi Xu, Yanzhi Wang, Shan Liu, Songnan Li

Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.

Neural Network Compression

Mixture of Robust Experts (MoRE):A Robust Denoising Method towards multiple perturbations

no code implementations21 Apr 2021 Kaidi Xu, Chenan Wang, Hao Cheng, Bhavya Kailkhura, Xue Lin, Ryan Goldhahn

To tackle the susceptibility of deep neural networks to examples, the adversarial training has been proposed which provides a notion of robust through an inner maximization problem presenting the first-order embedded within the outer minimization of the training loss.

Adversarial Robustness Denoising

Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Robustness Verification

3 code implementations NeurIPS 2021 Shiqi Wang, huan zhang, Kaidi Xu, Xue Lin, Suman Jana, Cho-Jui Hsieh, J. Zico Kolter

Compared to the typically tightest but very costly semidefinite programming (SDP) based incomplete verifiers, we obtain higher verified accuracy with three orders of magnitudes less verification time.

Adversarial Attack

On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning

1 code implementation ICLR 2021 Ren Wang, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Chuang Gan, Meng Wang

Despite the generalization power of the meta-model, it remains elusive that how adversarial robustness can be maintained by MAML in few-shot learning.

Adversarial Attack Adversarial Robustness +3

Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework

no code implementations21 Dec 2020 Pranay Sharma, Kaidi Xu, Sijia Liu, Pin-Yu Chen, Xue Lin, Pramod K. Varshney

In this work, we focus on the study of stochastic zeroth-order (ZO) optimization which does not require first-order gradient information and uses only function evaluations.

Low-Complexity Joint Power Allocation and Trajectory Design for UAV-Enabled Secure Communications with Power Splitting

no code implementations23 Aug 2020 Kaidi Xu, Ming-Min Zhao, Yunlong Cai, Lajos Hanzo

An unmanned aerial vehicle (UAV)-aided secure communication system is conceived and investigated, where the UAV transmits legitimate information to a ground user in the presence of an eavesdropper (Eve).

Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems

1 code implementation15 Jun 2020 Qiyu Hu, Yunlong Cai, Qingjiang Shi, Kaidi Xu, Guanding Yu, Zhi Ding

Then, we implement the proposed deepunfolding framework to solve the sum-rate maximization problem for precoding design in MU-MIMO systems.

Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond

5 code implementations NeurIPS 2020 Kaidi Xu, Zhouxing Shi, huan zhang, Yihan Wang, Kai-Wei Chang, Minlie Huang, Bhavya Kailkhura, Xue Lin, Cho-Jui Hsieh

Linear relaxation based perturbation analysis (LiRPA) for neural networks, which computes provable linear bounds of output neurons given a certain amount of input perturbation, has become a core component in robustness verification and certified defense.


Defending against Backdoor Attack on Deep Neural Networks

no code implementations26 Feb 2020 Kaidi Xu, Sijia Liu, Pin-Yu Chen, Pu Zhao, Xue Lin

Although deep neural networks (DNNs) have achieved a great success in various computer vision tasks, it is recently found that they are vulnerable to adversarial attacks.

Backdoor Attack Data Poisoning

Towards an Efficient and General Framework of Robust Training for Graph Neural Networks

no code implementations25 Feb 2020 Kaidi Xu, Sijia Liu, Pin-Yu Chen, Mengshu Sun, Caiwen Ding, Bhavya Kailkhura, Xue Lin

To overcome these limitations, we propose a general framework which leverages the greedy search algorithms and zeroth-order methods to obtain robust GNNs in a generic and an efficient manner.

Adversarial T-shirt! Evading Person Detectors in A Physical World

1 code implementation ECCV 2020 Kaidi Xu, Gaoyuan Zhang, Sijia Liu, Quanfu Fan, Mengshu Sun, Hongge Chen, Pin-Yu Chen, Yanzhi Wang, Xue Lin

To the best of our knowledge, this is the first work that models the effect of deformation for designing physical adversarial examples with respect to-rigid objects such as T-shirts.

ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization

1 code implementation NeurIPS 2019 Xiangyi Chen, Sijia Liu, Kaidi Xu, Xingguo Li, Xue Lin, Mingyi Hong, David Cox

In this paper, we propose a zeroth-order AdaMM (ZO-AdaMM) algorithm, that generalizes AdaMM to the gradient-free regime.

Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML

1 code implementation30 Sep 2019 Sijia Liu, Songtao Lu, Xiangyi Chen, Yao Feng, Kaidi Xu, Abdullah Al-Dujaili, Minyi Hong, Una-May O'Reilly

In this paper, we study the problem of constrained robust (min-max) optimization ina black-box setting, where the desired optimizer cannot access the gradients of the objective function but may query its values.

REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs

no code implementations29 Sep 2019 Caiwen Ding, Shuo Wang, Ning Liu, Kaidi Xu, Yanzhi Wang, Yun Liang

To achieve real-time, highly-efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and develop an efficient processing element (PE) structure supporting the heterogeneous weight quantization, CONV dataflow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource.

Model Compression Object Detection +1

On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method

1 code implementation ICCV 2019 Pu Zhao, Sijia Liu, Pin-Yu Chen, Nghia Hoang, Kaidi Xu, Bhavya Kailkhura, Xue Lin

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 cases but also in worst cases or adverse situations.

Adversarial Attack Image Classification

Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective

1 code implementation10 Jun 2019 Kaidi Xu, Hongge Chen, Sijia Liu, Pin-Yu Chen, Tsui-Wei Weng, Mingyi Hong, Xue Lin

Graph neural networks (GNNs) which apply the deep neural networks to graph data have achieved significant performance for the task of semi-supervised node classification.

Adversarial Robustness Classification +2

Brain-inspired reverse adversarial examples

no code implementations28 May 2019 Shaokai Ye, Sia Huat Tan, Kaidi Xu, Yanzhi Wang, Chenglong Bao, Kaisheng Ma

On contrast, current state-of-the-art deep learning approaches heavily depend on the variety of training samples and the capacity of the network.


Interpreting Adversarial Examples by Activation Promotion and Suppression

no code implementations3 Apr 2019 Kaidi Xu, Sijia Liu, Gaoyuan Zhang, Mengshu Sun, Pu Zhao, Quanfu Fan, Chuang Gan, Xue Lin

It is widely known that convolutional neural networks (CNNs) are vulnerable to adversarial examples: images with imperceptible perturbations crafted to fool classifiers.

Adversarial Robustness

Adversarial Robustness vs Model Compression, or Both?

1 code implementation29 Mar 2019 Shaokai Ye, Kaidi Xu, Sijia Liu, Jan-Henrik Lambrechts, huan zhang, Aojun Zhou, Kaisheng Ma, Yanzhi Wang, Xue Lin

Furthermore, this work studies two hypotheses about weight pruning in the conventional setting and finds that weight pruning is essential for reducing the network model size in the adversarial setting, training a small model from scratch even with inherited initialization from the large model cannot achieve both adversarial robustness and high standard accuracy.

Adversarial Robustness Model Compression +1

Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM

2 code implementations23 Mar 2019 Shaokai Ye, Xiaoyu Feng, Tianyun Zhang, Xiaolong Ma, Sheng Lin, Zhengang Li, Kaidi Xu, Wujie Wen, Sijia Liu, Jian Tang, Makan Fardad, Xue Lin, Yongpan Liu, Yanzhi Wang

A recent work developed a systematic frame-work of DNN weight pruning using the advanced optimization technique ADMM (Alternating Direction Methods of Multipliers), achieving one of state-of-art in weight pruning results.

Model Compression Quantization

Structured Adversarial Attack: Towards General Implementation and Better Interpretability

1 code implementation ICLR 2019 Kaidi Xu, Sijia Liu, Pu Zhao, Pin-Yu Chen, huan zhang, Quanfu Fan, Deniz Erdogmus, Yanzhi Wang, Xue Lin

When generating adversarial examples to attack deep neural networks (DNNs), Lp norm of the added perturbation is usually used to measure the similarity between original image and adversarial example.

Adversarial Attack

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