Michael Backes

research

∙ 07/17/2023

Secure Composition of Robust and Optimising Compilers

To ensure that secure applications do not leak their secrets, they are r...

0 Matthis Kruse, et al. ∙

research

∙ 11/17/2022

Towards Good Practices in Evaluating Transfer Adversarial Attacks

Transfer adversarial attacks raise critical security concerns in real-wo...

0 Zhengyu Zhao, et al. ∙

research

∙ 10/17/2022

A Systematic Study of the Consistency of Two-Factor Authentication User Journeys on Top-Ranked Websites (Extended Version)

Heuristics for user experience state that users will transfer their expe...

0 Sanam Ghorbani Lyastani, et al. ∙

research

∙ 09/07/2022

Why So Toxic? Measuring and Triggering Toxic Behavior in Open-Domain Chatbots

Chatbots are used in many applications, e.g., automated agents, smart ho...

0 Wai Man Si, et al. ∙

research

∙ 11/22/2021

Bilingual Problems: Studying the Security Risks Incurred by Native Extensions in Scripting Languages

Scripting languages are continuously gaining popularity due to their eas...

0 Cristian-Alexandru Staicu, et al. ∙

research

∙ 10/11/2021

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

The Android middleware, in particular the so-called systemserver, is a c...

0 Oliver Schranz, et al. ∙

research

∙ 10/06/2021

Inference Attacks Against Graph Neural Networks

Graph is an important data representation ubiquitously existing in the r...

45 Zhikun Zhang, et al. ∙

research

∙ 05/08/2021

Mental Models of Adversarial Machine Learning

Although machine learning (ML) is widely used in practice, little is kno...

0 Lukas Bieringer, et al. ∙

research

∙ 03/27/2021

Graph Unlearning

The right to be forgotten states that a data subject has the right to er...

23 Min Chen, et al. ∙

research

∙ 02/10/2021

Node-Level Membership Inference Attacks Against Graph Neural Networks

Many real-world data comes in the form of graphs, such as social network...

10 Xinlei He, et al. ∙

research

∙ 02/04/2021

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

Inference attacks against Machine Learning (ML) models allow adversaries...

0 Yugeng Liu, et al. ∙

research

∙ 12/30/2020

PrivSyn: Differentially Private Data Synthesis

In differential privacy (DP), a challenging problem is to generate synth...

0 Zhikun Zhang, et al. ∙

research

∙ 10/07/2020

Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks

Backdoor attack against deep neural networks is currently being profound...

6 Ahmed Salem, et al. ∙

research

∙ 10/06/2020

BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models

The tremendous progress of autoencoders and generative adversarial netwo...

14 Ahmed Salem, et al. ∙

research

∙ 09/10/2020

Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning

While being deployed in many critical applications as core components, m...

7 Yang Zou, et al. ∙

research

∙ 07/14/2020

Adversarial Examples and Metrics

Adversarial examples are a type of attack on machine learning (ML) syste...

0 Nico Döttling, et al. ∙

research

∙ 06/12/2020

How many winning tickets are there in one DNN?

The recent lottery ticket hypothesis proposes that there is one sub-netw...

0 Kathrin Grosse, et al. ∙

research

∙ 06/11/2020

A new measure for overfitting and its implications for backdooring of deep learning

Overfitting describes the phenomenon that a machine learning model fits ...

0 Kathrin Grosse, et al. ∙

research

∙ 06/01/2020

BadNL: Backdoor Attacks Against NLP Models

Machine learning (ML) has progressed rapidly during the past decade and ...

0 Xiaoyi Chen, et al. ∙

research

∙ 06/01/2020

Adversarial Attacks on Classifiers for Eye-based User Modelling

An ever-growing body of work has demonstrated the rich information conte...

0 Inken Hagestedt, et al. ∙

research

∙ 05/25/2020

Decentralized Privacy-Preserving Proximity Tracing

This document describes and analyzes a system for secure and privacy-pre...

0 Carmela Troncoso, et al. ∙

research

∙ 05/05/2020

When Machine Unlearning Jeopardizes Privacy

The right to be forgotten states that a data owner has the right to eras...

10 Min Chen, et al. ∙

research

∙ 05/05/2020

Stealing Links from Graph Neural Networks

Graph data, such as social networks and chemical networks, contains a we...

0 Xinlei He, et al. ∙

research

∙ 04/19/2020

Trollthrottle – Raising the Cost of Astroturfing

Astroturfing, i.e., the fabrication of public discourse by private or st...

0 Ilkan Esiyok, et al. ∙

research

∙ 03/07/2020

Dynamic Backdoor Attacks Against Machine Learning Models

Machine learning (ML) has made tremendous progress during the past decad...

50 Ahmed Salem, et al. ∙

research

∙ 10/31/2019

Causality Control Flow

Causality has been the issue of philosophic debate since Hippocrates. It...

0 Robert Künnemann, et al. ∙

research

∙ 09/23/2019

MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples

In a membership inference attack, an attacker aims to infer whether a da...

0 Jinyuan Jia, et al. ∙

research

∙ 09/19/2019

Adversarial Vulnerability Bounds for Gaussian Process Classification

Machine learning (ML) classification is increasingly used in safety-crit...

6 Michael Thomas Smith, et al. ∙

research

∙ 09/09/2019

Proconda – Protected Control Data

Memory corruption vulnerabilities often enable attackers to take control...

0 Marie-Therese Walter, et al. ∙

research

∙ 04/01/2019

Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning

Machine learning (ML) has progressed rapidly during the past decade and ...

0 Ahmed Salem, et al. ∙

research

∙ 02/08/2019

Adversarial Initialization -- when your network performs the way I want

The increase in computational power and available data has fueled a wide...

0 Kathrin Grosse, et al. ∙

research

∙ 12/06/2018

The Limitations of Model Uncertainty in Adversarial Settings

Machine learning models are vulnerable to adversarial examples: minor pe...

0 Kathrin Grosse, et al. ∙

research

∙ 09/13/2018

Fidelius: Protecting User Secrets from Compromised Browsers

Users regularly enter sensitive data, such as passwords, credit card num...

0 Saba Eskandarian, et al. ∙

research

∙ 08/01/2018

MLCapsule: Guarded Offline Deployment of Machine Learning as a Service

With the widespread use of machine learning (ML) techniques, ML as a ser...

0 Lucjan Hanzlik, et al. ∙

research

∙ 06/06/2018

Killing Three Birds with one Gaussian Process: Analyzing Attack Vectors on Classification

The wide usage of Machine Learning (ML) has lead to research on the atta...

2 Kathrin Grosse, et al. ∙

research

∙ 06/04/2018

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

Machine learning (ML) has become a core component of many real-world app...

2 Ahmed Salem, et al. ∙

research

∙ 05/28/2018

Automated Verification of Accountability in Security Protocols

Accounting for misbehavior, instead of postulating trustworthiness, has ...

0 Robert Künnemann, et al. ∙

research

∙ 02/12/2018

Tagvisor: A Privacy Advisor for Sharing Hashtags

Hashtag has emerged as a widely used concept of popular culture and camp...

0 Yang Zhang, et al. ∙

research

∙ 12/24/2017

Studying the Impact of Managers on Password Strength and Reuse

Despite their well-known security problems, passwords are still the incu...

0 Sanam Ghorbani Lyastani, et al. ∙

research

∙ 11/17/2017

How Wrong Am I? - Studying Adversarial Examples and their Impact on Uncertainty in Gaussian Process Machine Learning Models

Machine learning models are vulnerable to adversarial examples: minor, i...

0 Kathrin Grosse, et al. ∙

research

∙ 11/15/2017

CTRL+Z: Recovering Anonymized Social Graphs

Social graphs derived from online social interactions contain a wealth o...

0 Yang Zhang, et al. ∙

research

∙ 05/15/2017

Simulated Penetration Testing and Mitigation Analysis

Penetration testing is a well-established practical concept for the iden...

0 Michael Backes, et al. ∙

research

∙ 02/21/2017

On the (Statistical) Detection of Adversarial Examples

Machine Learning (ML) models are applied in a variety of tasks such as n...

0 Kathrin Grosse, et al. ∙

research

∙ 06/14/2016

Adversarial Perturbations Against Deep Neural Networks for Malware Classification

Deep neural networks, like many other machine learning models, have rece...

0 Kathrin Grosse, et al. ∙

Michael Backes

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