Shengshan Hu | DeepAI

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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...

0 Long Tang, et al. ∙

∙ 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 ...

0 Shengshan Hu, et al. ∙

∙ 10/04/2022

Shielding Federated Learning: Mitigating Byzantine Attacks with Less Constraints

Federated learning is a newly emerging distributed learning framework th...

0 Minghui Li, et al. ∙

∙ 07/01/2022

BadHash: Invisible Backdoor Attacks against Deep Hashing with Clean Label

Due to its powerful feature learning capability and high efficiency, dee...

0 Shengshan Hu, et al. ∙

∙ 05/14/2022

Evaluating Membership Inference Through Adversarial Robustness

The usage of deep learning is being escalated in many applications. Due ...

0 Zhaoxi Zhang, et al. ∙

∙ 04/28/2022

Shielding Federated Learning: Robust Aggregation with Adaptive Client Selection

Federated learning (FL) enables multiple clients to collaboratively trai...

0 Wei Wan, et al. ∙

∙ 04/22/2022

Towards Privacy-Preserving Neural Architecture Search

Machine learning promotes the continuous development of signal processin...

0 Fuyi Wang, et al. ∙

∙ 04/05/2022

Attention Distraction: Watermark Removal Through Continual Learning with Selective Forgetting

Fine-tuning attacks are effective in removing the embedded watermarks in...

4 Qi Zhong, et al. ∙

∙ 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...

0 Xiaogeng Liu, et al. ∙

∙ 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 ...

0 Shengshan Hu, et al. ∙

∙ 12/29/2021

Challenges and approaches for mitigating byzantine attacks in federated learning

Recently emerged federated learning (FL) is an attractive distributed le...

0 Shengshan Hu, et al. ∙

∙ 05/08/2021

Self-Supervised Adversarial Example Detection by Disentangled Representation

Deep learning models are known to be vulnerable to adversarial examples ...

0 Zhaoxi Zhang, et al. ∙

∙ 02/22/2020

Optimizing Privacy-Preserving Outsourced Convolutional Neural Network Predictions

Neural networks provide better prediction performance than previous tech...

0 Minghui Li, et al. ∙

∙ 10/29/2019

Shielding Collaborative Learning: Mitigating Poisoning Attacks through Client-Side Detection

Collaborative learning allows multiple clients to train a joint model wi...

0 Lingchen Zhao, et al. ∙

∙ 09/20/2019

Augmenting Encrypted Search: A Decentralized Service Realization with Enforced Execution

Searchable symmetric encryption (SSE) allows the data owner to outsource...

0 Shengshan Hu, et al. ∙

∙ 09/16/2019

VeriML: Enabling Integrity Assurances and Fair Payments for Machine Learning as a Service

Machine Learning as a Service (MLaaS) allows clients with limited resour...

0 Lingchen Zhao, et al. ∙