Search Results for author:
Found 12 papers, 1 papers with code
Vertical Federated Learning: Taxonomies, Threats, and Prospects
In a number of practical scenarios, VFL is more relevant than HFL as different companies (e. g., bank and retailer) hold different features (e. g., credit history and shopping history) for the same set of customers.
MACAB: Model-Agnostic Clean-Annotation Backdoor to Object Detection with Natural Trigger in Real-World
We observe that the backdoor effect of both misclassification and the cloaking are robustly achieved in the wild when the backdoor is activated with inconspicuously natural physical triggers.
CASSOCK: Viable Backdoor Attacks against DNN in The Wall of Source-Specific Backdoor Defences
Compared with a representative SSBA as a baseline ($SSBA_{Base}$), $CASSOCK$-based attacks have significantly advanced the attack performance, i. e., higher ASR and lower FPR with comparable CDA (clean data accuracy).
Towards A Critical Evaluation of Robustness for Deep Learning Backdoor Countermeasures
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 assumptions defined in their respective threat models.
Towards Explainable Meta-Learning for DDoS Detection
With the ever increasing of new intrusions, intrusion detection task rely on Artificial Intelligence more and more.
PPA: Preference Profiling Attack Against Federated Learning
By observing a user model's gradient sensitivity to a class, PPA can profile the sample proportion of the class in the user's local dataset, and thus the user's preference of the class is exposed.
Dangerous Cloaking: Natural Trigger based Backdoor Attacks on Object Detectors in the Physical World
The averaged ASR still remains sufficiently high to be 78% in the transfer learning attack scenarios evaluated on CenterNet.
NTD: Non-Transferability Enabled Backdoor Detection
A backdoor deep learning (DL) model behaves normally upon clean inputs but misbehaves upon trigger inputs as the backdoor attacker desires, posing severe consequences to DL model deployments.
Quantization Backdoors to Deep Learning Commercial Frameworks
This work reveals that the standard quantization toolkits can be abused to activate a backdoor.
RBNN: Memory-Efficient Reconfigurable Deep Binary Neural Network with IP Protection for Internet of Things
To this end, a 1-bit quantized DNN model or deep binary neural network maximizes the memory efficiency, where each parameter in a BNN model has only 1-bit.
VFL: A Verifiable Federated Learning with Privacy-Preserving for Big Data in Industrial IoT
If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted.
Cryptography and Security E.3; I.2.11
Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review
We have also reviewed the flip side of backdoor attacks, which are explored for i) protecting intellectual property of deep learning models, ii) acting as a honeypot to catch adversarial example attacks, and iii) verifying data deletion requested by the data contributor. Overall, the research on defense is far behind the attack, and there is no single defense that can prevent all types of backdoor attacks.
