Aishan Liu | DeepAI

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∙ 08/04/2023

RobustMQ: Benchmarking Robustness of Quantized Models

Quantization has emerged as an essential technique for deploying deep ne...

0 Yisong Xiao, et al. ∙

∙ 08/02/2023

Isolation and Induction: Training Robust Deep Neural Networks against Model Stealing Attacks

Despite the broad application of Machine Learning models as a Service (M...

0 Jun Guo, et al. ∙

∙ 07/01/2023

SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency

Extensive studies have shown that deep learning models are vulnerable to...

0 Yan Wang, et al. ∙

∙ 05/22/2023

Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks

Physical world adversarial attack is a highly practical and threatening ...

0 Simin Li, et al. ∙

∙ 05/19/2023

Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing

Machine learning (ML) systems have achieved remarkable performance acros...

0 Yisong Xiao, et al. ∙

∙ 02/19/2023

X-Adv: Physical Adversarial Object Attacks against X-ray Prohibited Item Detection

Adversarial attacks are valuable for evaluating the robustness of deep l...

0 Aishan Liu, et al. ∙

∙ 09/28/2022

Exploring the Relationship between Architecture and Adversarially Robust Generalization

Adversarial training has been demonstrated to be one of the most effecti...

0 Shiyu Tang, et al. ∙

∙ 09/20/2022

Rethinking Data Augmentation in Knowledge Distillation for Object Detection

Knowledge distillation (KD) has shown its effectiveness for object detec...

0 Jiawei Liang, et al. ∙

∙ 09/15/2022

Improving Robust Fairness via Balance Adversarial Training

Adversarial training (AT) methods are effective against adversarial atta...

0 Chunyu Sun, et al. ∙

∙ 09/12/2022

Adaptive Perturbation Generation for Multiple Backdoors Detection

Extensive evidence has demonstrated that deep neural networks (DNNs) are...

0 Yuhang Wang, et al. ∙

∙ 08/23/2022

Hierarchical Perceptual Noise Injection for Social Media Fingerprint Privacy Protection

Billions of people are sharing their daily life images on social media e...

0 Simin Li, et al. ∙

∙ 04/13/2022

Defensive Patches for Robust Recognition in the Physical World

To operate in real-world high-stakes environments, deep learning systems...

0 Jiakai Wang, et al. ∙

∙ 09/16/2021

Harnessing Perceptual Adversarial Patches for Crowd Counting

Crowd counting, which is significantly important for estimating the numb...

5 Shunchang Liu, et al. ∙

∙ 09/11/2021

RobustART: Benchmarking Robustness on Architecture Design and Training Techniques

Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...

4 Shiyu Tang, et al. ∙

∙ 08/24/2021

ARShoe: Real-Time Augmented Reality Shoe Try-on System on Smartphones

Virtual try-on technology enables users to try various fashion items usi...

10 Shan An, et al. ∙

∙ 03/01/2021

Dual Attention Suppression Attack: Generate Adversarial Camouflage in Physical World

Deep learning models are vulnerable to adversarial examples. As a more t...

0 Jiakai Wang, et al. ∙

∙ 03/01/2021

Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images

Security inspection is X-ray scanning for personal belongings in suitcas...

0 Renshuai Tao, et al. ∙

∙ 01/24/2021

A Comprehensive Evaluation Framework for Deep Model Robustness

Deep neural networks (DNNs) have achieved remarkable performance across ...

0 Aishan Liu, et al. ∙

∙ 12/03/2020

Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization

There is now extensive evidence demonstrating that deep neural networks ...

0 Aishan Liu, et al. ∙

∙ 10/16/2020

On the Guaranteed Almost Equivalence between Imitation Learning from Observation and Demonstration

Imitation learning from observation (LfO) is more preferable than imitat...

0 Zhihao Cheng, et al. ∙

∙ 05/19/2020

Patch Attack for Automatic Check-out

Adversarial examples are inputs with imperceptible perturbations that ea...

5 Aishan Liu, et al. ∙

∙ 05/19/2020

Adversarial Attacks for Embodied Agents

Adversarial attacks are valuable for providing insights into the blind-s...

40 Aishan Liu, et al. ∙

∙ 09/19/2019

Training Robust Deep Neural Networks via Adversarial Noise Propagation

Deep neural networks have been found vulnerable to noises like adversari...

0 Aishan Liu, et al. ∙

∙ 09/16/2019

Interpreting and Improving Adversarial Robustness with Neuron Sensitivity

Deep neural networks (DNNs) are vulnerable to adversarial examples where...

14 Chongzhi Zhang, et al. ∙

∙ 09/11/2019

Towards Noise-Robust Neural Networks via Progressive Adversarial Training

Adversarial examples, intentionally designed inputs tending to mislead d...

0 Hang Yu, et al. ∙

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