activity
20202025
most citedBackdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review

133 citations · 186 across the 19 of their papers we have counts for

collaborators
Showing 2022Show all

9 papers · 1 filter

cs.CV2022★ 1 cited

TransCAB: Transferable Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World

Hua Ma, Yinshan Li, Yansong Gao +6

Object detection is the foundation of various critical computer-vision tasks such as segmentation, object tracking, and event detection. To train an object detector with satisfacto…

cs.CR2022★ 1 cited

MLMSA: Multi-Label Multi-Side-Channel-Information enabled Deep Learning Attacks on APUF Variants

Yansong Gao, Jianrong Yao, Lihui Pang +4

To improve the modeling resilience of silicon strong physical unclonable functions (PUFs), in particular, the APUFs, that yield a very large number of challenge response pairs (CRP…

cs.CR2022★ 2 cited

CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences

Shang Wang, Yansong Gao, Anmin Fu +4

As a critical threat to deep neural networks (DNNs), backdoor attacks can be categorized into two types, i.e., source-agnostic backdoor attacks (SABAs) and source-specific backdoor…

cs.CR2022★ 5 cited

Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures

Huming Qiu, Hua Ma, Zhi Zhang +4

Since Deep Learning (DL) backdoor attacks have been revealed as one of the most insidious adversarial attacks, a number of countermeasures have been developed with certain assumpti…

cs.AI2022★ 5 cited

Towards Explainable Meta-Learning for DDoS Detection

Qianru Zhou, Rongzhen Li, Lei Xu +3

The Internet is the most complex machine humankind has ever built, and how to defense it from intrusions is even more complex. With the ever increasing of new intrusions, intrusion…

cs.CR2022

Towards Privacy-Preserving and Verifiable Federated Matrix Factorization

Xicheng Wan, Yifeng Zheng, Qun Li +3

Recent years have witnessed the rapid growth of federated learning (FL), an emerging privacy-aware machine learning paradigm that allows collaborative learning over isolated datase…

cs.CR2022★ 2 cited

Systematically Evaluation of Challenge Obfuscated APUFs

Yansong Gao, Jianrong Yao, Lihui Pang +4

As a well-known physical unclonable function that can provide huge number of challenge response pairs (CRP) with a compact design and fully compatibility with current electronic fa…

cs.LG2022★ 1 cited

PPA: Preference Profiling Attack Against Federated Learning

Chunyi Zhou, Yansong Gao, Anmin Fu +5

Federated learning (FL) trains a global model across a number of decentralized users, each with a local dataset. Compared to traditional centralized learning, FL does not require d…

cs.CV2022★ 16 cited

Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World

Hua Ma, Yinshan Li, Yansong Gao +7

Deep learning models have been shown to be vulnerable to recent backdoor attacks. A backdoored model behaves normally for inputs containing no attacker-secretly-chosen trigger and…