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Feature Generating Networks for Zero-Shot Learning

Yongqin Xian, Tobias Lorenz, Bernt Schiele and Zeynep Akata in IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018

We propose a novel generative adversarial network that synthesizes CNN features conditioned on class-level semantic information, offering a shortcut directly from a semantic descriptor of a class to a class-conditional feature distribution.

Robustness Certification for Point Cloud Models

Tobias Lorenz, Anian Ruoss, Mislav Balunović, Gagandeep Singh and Martin Vechev in IEEE/CVF International Conference on Computer Vision (ICCV), 2021

We introduce 3DCertify, the first verifier able to certify robustness of point cloud models to semantic transformations.

Certifiers Make Neural Networks Vulnerable to Availability Attacks

Tobias Lorenz, Marta Kwiatkowska, Mario Fritz in 16th ACM Workshop on Artificial Intelligence and Security (AISec), 2023

We propose the first systematic analysis of availability attacks against neural network certifiers and demonstrate highly effective backdoor attacks that exploit the fallback strategy. Initial defenses were unable to mitigate this novel attack.

Adaptive Hierarchical Certification for Segmentation using Randomized Smoothing

Alaa Anani, Tobias Lorenz, Bernt Schiele, Mario Fritz in International Conference on Machine Learning (ICML), 2024

This paper introduces, for the first time, an adaptive hierarchical certification algorithm for image semantic segmentation that certifies image pixels within a hierarchy and proves the correctness of its guarantees.