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M. Backes

42 papers hereh-index 7321k citations394 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author30
  • last author8

Across the 40 of 42 papers where every author was matched, so the position is known.

fields
  • cs.CR37
  • cs.CV1
  • cs.CY1
  • cs.LG1
  • cs.LO1
  • cs.SI1
same name
  • M. Backes — 175 papers
  • M. Backes — 45 papers, h 78
  • M. Backes — 11 papers, h 31
  • M. Backes — 6 papers
  • M. Backes — 5 papers
  • M. Backes — 2 papers

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20112023
most citedNode-Level Membership Inference Attacks Against Graph Neural Networks

50 citations · 185 across the 23 of their papers we have counts for

collaborators
Showing 2021Show all

4 papers · 1 filter

cs.CR2021★ 1 cited

Get a Model! Model Hijacking Attack Against Machine Learning Models

Ahmed Salem, Michael Backes, Yang Zhang

Machine learning (ML) has established itself as a cornerstone for various critical applications ranging from autonomous driving to authentication systems. However, with this increa…

cs.CR2021

Towards a Principled Approach for Dynamic Analysis of Android's Middleware

Oliver Schranz, Sebastian Weisgerber, Erik Derr +2

The Android middleware, in particular the so-called systemserver, is a crucial and central component to Android's security and robustness. To understand whether the systemserver pr…

cs.CR2021★ 50 cited

Node-Level Membership Inference Attacks Against Graph Neural Networks

Xinlei He, Rui Wen, Yixin Wu +3

Many real-world data comes in the form of graphs, such as social networks and protein structure. To fully utilize the information contained in graph data, a new family of machine l…

cs.CR2021

ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models

Yugeng Liu, Rui Wen, Xinlei He +6

Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied,…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.