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08/04/2023
RobustMQ: Benchmarking Robustness of Quantized Models
Quantization has emerged as an essential technique for deploying deep ne...
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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...
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07/01/2023
SysNoise: Exploring and Benchmarking Training-Deployment System Inconsistency
Extensive studies have shown that deep learning models are vulnerable to...
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05/22/2023
Towards Benchmarking and Assessing Visual Naturalness of Physical World Adversarial Attacks
Physical world adversarial attack is a highly practical and threatening ...
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05/19/2023
Latent Imitator: Generating Natural Individual Discriminatory Instances for Black-Box Fairness Testing
Machine learning (ML) systems have achieved remarkable performance acros...
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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...
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09/28/2022
Exploring the Relationship between Architecture and Adversarially Robust Generalization
Adversarial training has been demonstrated to be one of the most effecti...
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09/20/2022
Rethinking Data Augmentation in Knowledge Distillation for Object Detection
Knowledge distillation (KD) has shown its effectiveness for object detec...
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09/15/2022
Improving Robust Fairness via Balance Adversarial Training
Adversarial training (AT) methods are effective against adversarial atta...
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09/12/2022
Adaptive Perturbation Generation for Multiple Backdoors Detection
Extensive evidence has demonstrated that deep neural networks (DNNs) are...
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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...
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04/13/2022
Defensive Patches for Robust Recognition in the Physical World
To operate in real-world high-stakes environments, deep learning systems...
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09/16/2021
Harnessing Perceptual Adversarial Patches for Crowd Counting
Crowd counting, which is significantly important for estimating the numb...
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09/11/2021
RobustART: Benchmarking Robustness on Architecture Design and Training Techniques
Deep neural networks (DNNs) are vulnerable to adversarial noises, which ...
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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...
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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...
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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...
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01/24/2021
A Comprehensive Evaluation Framework for Deep Model Robustness
Deep neural networks (DNNs) have achieved remarkable performance across ...
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12/03/2020
Towards Defending Multiple Adversarial Perturbations via Gated Batch Normalization
There is now extensive evidence demonstrating that deep neural networks ...
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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...
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05/19/2020
Patch Attack for Automatic Check-out
Adversarial examples are inputs with imperceptible perturbations that ea...
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05/19/2020
Adversarial Attacks for Embodied Agents
Adversarial attacks are valuable for providing insights into the blind-s...
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09/19/2019
Training Robust Deep Neural Networks via Adversarial Noise Propagation
Deep neural networks have been found vulnerable to noises like adversari...
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09/16/2019
Interpreting and Improving Adversarial Robustness with Neuron Sensitivity
Deep neural networks (DNNs) are vulnerable to adversarial examples where...
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09/11/2019
