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Found 5 papers, 2 papers with code
WAFFLE: Watermarking in Federated Learning
In this paper, we present WAFFLE, the first approach to watermark DNN models trained using federated learning.
Extraction of Complex DNN Models: Real Threat or Boogeyman?
However, model extraction attacks can steal the functionality of ML models using the information leaked to clients through the results returned via the API.
Making targeted black-box evasion attacks effective and efficient
We investigate how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks in a black-box setting.
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Existing watermarking schemes are ineffective against IP theft via model extraction since it is the adversary who trains the surrogate model.
Online Feature Ranking for Intrusion Detection Systems
Many current approaches to the design of intrusion detection systems apply feature selection in a static, non-adaptive fashion.
