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08/14/2023
AdvCLIP: Downstream-agnostic Adversarial Examples in Multimodal Contrastive Learning
Multimodal contrastive learning aims to train a general-purpose feature ...
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08/07/2023
A Four-Pronged Defense Against Byzantine Attacks in Federated Learning
Federated learning (FL) is a nascent distributed learning paradigm to tr...
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07/31/2023
Benchmarking and Analyzing Robust Point Cloud Recognition: Bag of Tricks for Defending Adversarial Examples
Deep Neural Networks (DNNs) for 3D point cloud recognition are vulnerabl...
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07/23/2023
Downstream-agnostic Adversarial Examples
Self-supervised learning usually uses a large amount of unlabeled data t...
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07/15/2023
Why Does Little Robustness Help? Understanding Adversarial Transferability From Surrogate Training
Adversarial examples (AEs) for DNNs have been shown to be transferable: ...
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04/21/2023
Denial-of-Service or Fine-Grained Control: Towards Flexible Model Poisoning Attacks on Federated Learning
Federated learning (FL) is vulnerable to poisoning attacks, where advers...
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04/18/2023
Masked Language Model Based Textual Adversarial Example Detection
Adversarial attacks are a serious threat to the reliable deployment of m...
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03/27/2023
Detecting Backdoors During the Inference Stage Based on Corruption Robustness Consistency
Deep neural networks are proven to be vulnerable to backdoor attacks. De...
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03/01/2023
Feature Extraction Matters More: Universal Deepfake Disruption through Attacking Ensemble Feature Extractors
Adversarial example is a rising way of protecting facial privacy securit...
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11/22/2022
PointCA: Evaluating the Robustness of 3D Point Cloud Completion Models Against Adversarial Examples
Point cloud completion, as the upstream procedure of 3D recognition and ...
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10/04/2022
Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints
Federated learning is a newly emerging distributed learning framework th...
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07/01/2022
BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label
Due to its powerful feature learning capability and high efficiency, dee...
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05/14/2022
Evaluating Membership Inference Through Adversarial Robustness
The usage of deep learning is being escalated in many applications. Due ...
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04/28/2022
Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection
Federated learning (FL) enables multiple clients to collaboratively trai...
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04/22/2022
Towards Privacy-Preserving Neural Architecture Search
Machine learning promotes the continuous development of signal processin...
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04/05/2022
Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting
Fine-tuning attacks are effective in removing the embedded watermarks in...
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03/08/2022
Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation
The data-centric machine learning aims to find effective ways to build a...
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03/07/2022
Protecting Facial Privacy: Generating Adversarial Identity Masks via Style-robust Makeup Transfer
While deep face recognition (FR) systems have shown amazing performance ...
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12/29/2021
Challenges and approaches for mitigating byzantine attacks in federated learning
Recently emerged federated learning (FL) is an attractive distributed le...
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05/08/2021
Self-Supervised Adversarial Example Detection by Disentangled Representation
Deep learning models are known to be vulnerable to adversarial examples ...
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02/22/2020
Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions
Neural networks provide better prediction performance than previous tech...
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10/29/2019
Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection
Collaborative learning allows multiple clients to train a joint model wi...
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09/20/2019
Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution
Searchable symmetric encryption (SSE) allows the data owner to outsource...
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09/16/2019