50 citations · 185 across the 23 of their papers we have counts for
10 papers · 1 filter
BAAAN: Backdoor Attacks Against Autoencoder and GAN-Based Machine Learning Models
Ahmed Salem, Yannick Sautter, Michael Backes +2
The tremendous progress of autoencoders and generative adversarial networks (GANs) has led to their application to multiple critical tasks, such as fraud detection and sanitized da…
Don't Trigger Me! A Triggerless Backdoor Attack Against Deep Neural Networks
Ahmed Salem, Michael Backes, Yang Zhang
Backdoor attack against deep neural networks is currently being profoundly investigated due to its severe security consequences. Current state-of-the-art backdoor attacks require t…
Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning
Yang Zou, Zhikun Zhang, Michael Backes +1
While being deployed in many critical applications as core components, machine learning (ML) models are vulnerable to various security and privacy attacks. One major privacy attack…
Adversarial Examples and Metrics
Nico Döttling, Kathrin Grosse, Michael Backes +1
Adversarial examples are a type of attack on machine learning (ML) systems which cause misclassification of inputs. Achieving robustness against adversarial examples is crucial to…
How many winning tickets are there in one DNN?
Kathrin Grosse, Michael Backes
The recent lottery ticket hypothesis proposes that there is one sub-network that matches the accuracy of the original network when trained in isolation. We show that instead each n…
Adversarial Attacks on Classifiers for Eye-based User Modelling
Inken Hagestedt, Michael Backes, Andreas Bulling
An ever-growing body of work has demonstrated the rich information content available in eye movements for user modelling, e.g. for predicting users' activities, cognitive processes…