1 citations · 1 across the 1 of their papers we have counts for
9 papers · 1 filter
Two-in-One: A Model Hijacking Attack Against Text Generation Models
Wai Man Si, Michael Backes, Yang Zhang +1
Machine learning has progressed significantly in various applications ranging from face recognition to text generation. However, its success has been accompanied by different attac…
UnGANable: Defending Against GAN-based Face Manipulation
Zheng Li, Ning Yu, Ahmed Salem +3
Deepfakes pose severe threats of visual misinformation to our society. One representative deepfake application is face manipulation that modifies a victim's facial attributes in an…
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…
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,…
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…