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Conflicting Interactions Among Protection Mechanisms for Machine
  Learning Models
v1v2v3 (latest)

Conflicting Interactions Among Protection Mechanisms for Machine Learning Models

AAAI Conference on Artificial Intelligence (AAAI), 2022
5 July 2022
S. Szyller
Nadarajah Asokan
    AAML
ArXiv (abs)PDFHTML

Papers citing "Conflicting Interactions Among Protection Mechanisms for Machine Learning Models"

12 / 12 papers shown
Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
Marcin Chrapek
Marcin Copik
Etienne Mettaz
Torsten Hoefler
81
0
0
23 Sep 2025
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Asim Waheed
Vasisht Duddu
Rui Zhang
S. Szyller
AAML
234
1
0
15 Sep 2025
Evading Data Provenance in Deep Neural Networks
Evading Data Provenance in Deep Neural Networks
Hongyu Zhu
Sichu Liang
Wenwen Wang
Zhuomeng Zhang
Fangqi Li
Shi-Lin Wang
AAML
256
1
0
01 Aug 2025
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Framework GNN-AID: Graph Neural Network Analysis Interpretation and Defense
Kirill Lukyanov
Mikhail Drobyshevskiy
Georgii Sazonov
Mikhail Soloviov
Ilya Makarov
GNN
446
0
0
06 May 2025
Robustness questions the interpretability of graph neural networks: what to do?
Robustness questions the interpretability of graph neural networks: what to do?
Kirill Lukyanov
Georgii Sazonov
Serafim Boyarsky
Ilya Makarov
AAML
908
1
0
05 May 2025
Fortify Your Foundations: Practical Privacy and Security for Foundation
  Model Deployments In The Cloud
Fortify Your Foundations: Practical Privacy and Security for Foundation Model Deployments In The Cloud
Marcin Chrapek
Anjo Vahldiek-Oberwagner
Marcin Spoczynski
Scott Constable
Mona Vij
Torsten Hoefler
316
4
0
08 Oct 2024
Reliable Model Watermarking: Defending Against Theft without
  Compromising on Evasion
Reliable Model Watermarking: Defending Against Theft without Compromising on Evasion
Markus Frey
Sichu Liang
Wentao Hu
Matthias Nau
Ju Jia
Shilin Wang
AAML
295
11
0
21 Apr 2024
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A
  Comprehensive Benchmark on the Tennessee Eastman Process
Adversarial Attacks and Defenses in Fault Detection and Diagnosis: A Comprehensive Benchmark on the Tennessee Eastman Process
Vitaliy Pozdnyakov
Aleksandr Kovalenko
Ilya Makarov
Mikhail Drobyshevskiy
Kirill Lukyanov
AAML
283
13
0
20 Mar 2024
SoK: Unintended Interactions among Machine Learning Defenses and Risks
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu
S. Szyller
Nadarajah Asokan
AAML
380
6
0
07 Dec 2023
On the Robustness of Dataset Inference
On the Robustness of Dataset Inference
S. Szyller
Rui Zhang
Enchao Gong
Nadarajah Asokan
AAML
264
9
0
24 Oct 2022
Cryptanalytic Extraction of Neural Network Models
Cryptanalytic Extraction of Neural Network ModelsAnnual International Cryptology Conference (CRYPTO), 2020
Nicholas Carlini
Matthew Jagielski
Ilya Mironov
FedMLMLAUMIACVAAML
442
152
0
10 Mar 2020
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Marco Tulio Ribeiro
Sameer Singh
Carlos Guestrin
FAttFaML
2.5K
19,805
0
16 Feb 2016
1