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09/14/2023
Semantic Adversarial Attacks via Diffusion Models
Traditional adversarial attacks concentrate on manipulating clean exampl...
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08/31/2023
Communication-Efficient Decentralized Federated Learning via One-Bit Compressive Sensing
Decentralized federated learning (DFL) has gained popularity due to its ...
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08/23/2023
Does Physical Adversarial Example Really Matter to Autonomous Driving? Towards System-Level Effect of Adversarial Object Evasion Attack
In autonomous driving (AD), accurate perception is indispensable to achi...
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07/12/2023
Exposing the Fake: Effective Diffusion-Generated Images Detection
Image synthesis has seen significant advancements with the advent of dif...
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05/28/2023
Using Caterpillar to Nibble Small-Scale Images
Recently, MLP-based models have become popular and attained significant ...
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05/26/2023
An Efficient Membership Inference Attack for the Diffusion Model by Proximal Initialization
Recently, diffusion models have achieved remarkable success in generatin...
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04/28/2023
Improve Video Representation with Temporal Adversarial Augmentation
Recent works reveal that adversarial augmentation benefits the generaliz...
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02/02/2023
Are Diffusion Models Vulnerable to Membership Inference Attacks?
Diffusion-based generative models have shown great potential for image s...
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12/15/2022
Distributed-Training-and-Execution Multi-Agent Reinforcement Learning for Power Control in HetNet
In heterogeneous networks (HetNets), the overlap of small cells and the ...
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11/30/2022
Secrecy Rate Maximization of RIS-assisted SWIPT Systems: A Two-Timescale Beamforming Design Approach
Reconfigurable intelligent surfaces (RISs) achieve high passive beamform...
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09/20/2022
Audit and Improve Robustness of Private Neural Networks on Encrypted Data
Performing neural network inference on encrypted data without decryption...
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08/19/2022
Real-Time Robust Video Object Detection System Against Physical-World Adversarial Attacks
DNN-based video object detection (VOD) powers autonomous driving and vid...
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08/11/2022
General Cutting Planes for Bound-Propagation-Based Neural Network Verification
Bound propagation methods, when combined with branch and bound, are amon...
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04/12/2022
Toward Robust Spiking Neural Network Against Adversarial Perturbation
As spiking neural networks (SNNs) are deployed increasingly in real-worl...
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10/27/2021
ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers
Adversarial patch attacks that craft the pixels in a confined region of ...
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06/15/2021
Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression
Compressing Deep Neural Network (DNN) models to alleviate the storage an...
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03/11/2021
Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Complete and Incomplete Neural Network Verification
Recent works in neural network verification show that cheap incomplete v...
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02/20/2021
On Fast Adversarial Robustness Adaptation in Model-Agnostic Meta-Learning
Model-agnostic meta-learning (MAML) has emerged as one of the most succe...
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12/21/2020
Zeroth-Order Hybrid Gradient Descent: Towards A Principled Black-Box Optimization Framework
In this work, we focus on the study of stochastic zeroth-order (ZO) opti...
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11/27/2020
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers
Formal verification of neural networks (NNs) is a challenging and import...
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08/13/2020
MIMO-Aided Nonlinear Hybrid Transceiver Design for Multiuser mmWave Systems Relying on Tomlinson-Harashima Precoding
Hybrid analog-digital (A/D) transceivers designed for millimeter wave (m...
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06/15/2020
Iterative Algorithm Induced Deep-Unfolding Neural Networks: Precoding Design for Multiuser MIMO Systems
Optimization theory assisted algorithms have received great attention fo...
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02/28/2020
Automatic Perturbation Analysis on General Computational Graphs
Linear relaxation based perturbation analysis for neural networks, which...
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02/26/2020
Defending against Backdoor Attack on Deep Neural Networks
Although deep neural networks (DNNs) have achieved a great success in va...
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02/25/2020
Towards an Efficient and General Framework of Robust Training for Graph Neural Networks
Graph Neural Networks (GNNs) have made significant advances on several f...
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11/28/2019
Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation
Existing domain adaptation methods aim at learning features that can be ...
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10/18/2019
Evading Real-Time Person Detectors by Adversarial T-shirt
It is known that deep neural networks (DNNs) could be vulnerable to adve...
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10/15/2019
ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization
The adaptive momentum method (AdaMM), which uses past gradients to updat...
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09/30/2019
Min-Max Optimization without Gradients: Convergence and Applications to Adversarial ML
In this paper, we study the problem of constrained robust (min-max) opti...
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09/29/2019
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
Deep neural networks (DNNs), as the basis of object detection, will play...
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07/26/2019
On the Design of Black-box Adversarial Examples by Leveraging Gradient-free Optimization and Operator Splitting Method
Robust machine learning is currently one of the most prominent topics wh...
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06/10/2019
Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective
Graph neural networks (GNNs) which apply the deep neural networks to gra...
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05/28/2019
Brain-inspired reverse adversarial examples
A human does not have to see all elephants to recognize an animal as an ...
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04/03/2019
Interpreting Adversarial Examples by Activation Promotion and Suppression
It is widely known that convolutional neural networks (CNNs) are vulnera...
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03/29/2019
Second Rethinking of Network Pruning in the Adversarial Setting
It is well known that deep neural networks (DNNs) are vulnerable to adve...
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03/23/2019
Progressive DNN Compression: A Key to Achieve Ultra-High Weight Pruning and Quantization Rates using ADMM
Weight pruning and weight quantization are two important categories of D...
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10/17/2018
Progressive Weight Pruning of Deep Neural Networks using ADMM
Deep neural networks (DNNs) although achieving human-level performance i...
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08/05/2018
