dogear

enter for all results · esc to close

Neural Network Model Extraction Attacks in Edge Devicesby Hearing Architectural Hints

arxiv.orgsite

(arxiv) Through the acquisition of memory access events from bus snooping, layer sequence identification bythe LSTM-CTC model, layer topology connection according to the memory access pattern, and layer dimension estimation under data volume constraints, it demonstrates one can…

from
Audit Algorithms
added
2026-10-10
likes
0

similar

  1. Stealing Neural Networks via Timing Side Channels arxiv.org

    (arXiv) Stealing/approximating a model through timing attacks usin queries.

  2. TamperNN: Efficient Tampering Detection of Deployed Neural Nets arxiv.org

    (ISSRE) Algorithms to craft inputs that can detect the tampering with a remotely executed classifier model.

  3. Making targeted black-box evasion attacks effective andefficient arxiv.org

    (arXiv) Investigates how an adversary can optimally use its query budget for targeted evasion attacks against deep neural networks.

  4. Knockoff Nets: Stealing Functionality of Black-Box Models arxiv.org

    (CVPR) Ask to what extent can an adversary steal functionality of such "victim" models based solely on blackbox interactions: image in, predictions out.

  5. Extracting Training Data from Large Language Models arxiv.org

    (arxiv) Performs a training data extraction attack to recover individual training examples by querying the language model.

  6. Adversarial Model Extraction on Graph Neural Networks arxiv.org

    (AAAI Workshop on Deep Learning on Graphs: Methodologies and Applications) Introduces GNN model extraction and presents a preliminary approach for this.

Audit Algorithms › Papers > 2019: “(arxiv) Through the acquisition of memory access events from bus snooping, layer sequence identification bythe LSTM-CTC model, layer topology connection according to the memory access pattern, and layer dimension estimation under data volume constraints, it demonstrates one can…”