Geometric DL transformer based surrogate simulator for modelling joint distributions between 3d environment and its influence on EM waves.

Solving black-box optimization problems on stochastic simulators using neural surrogates and reinforcement learning.

We reinforcement learn active learning policies to guide local-surrogate-based inverse problem optimisation.

Reinforcement learning policies to guide surrogate-based inverse problem optimisation.

Transformer-based neural surrogate to model mmWave path losses in dense urban scenarios.

NeRF-like neural surrogate to model differentiable wireless electromagnetic propagation effects in indoor environments.

GAN-based surrogate simulator to learn distributions of channel impulse response complex waveforms.

An image obfuscation network to remove privacy attribute information (such as by inverting, or maximizing uncertainty), while retaining image fidelity.

Differential Privacy Defenses and Sampling Attacks for Membership Inference
Shadi Rahimian, Tribhuvanesh Orekondy, Mario Fritz
AISec, 2021
paper  ·  bibtex

Differential Privacy approaches to defend against membership inference attacks.

A novel GAN to allow releasing sanitized forms of data with rigorous differential privacy guarantees.

An optimization-based defense against model stealing attacks, with perturbations crafted to poison resulting gradient signals.

Vision models encode meaningful information in predictions even on out-of-distribution natural images. We exploit this property to steal functionality of complex vision models.

Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang, Bernt Schiele Mario Fritz
FL NeurIPS, 2019
paper  ·  poster  ·  talk  ·  bibtex

Gradient parameter deltas in Federated Learning encodes user bias statistics of participating devices, raising deanonymization concerns.

Connecting Pixels to Privacy and Utility: Automatic Redaction of Private Information in Images
Tribhuvanesh Orekondy, Mario Fritz, Bernt Schiele
CVPR, 2018 (Spotlight)
paper  ·  poster  ·  project page  ·  video  ·  bibtex

Automatic method to identify and redact a broad range of private information spanning multiple modalities in visual content.

Towards a Visual Privacy Advisor: Understanding and Predicting Privacy Risks in Images
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
ICCV, 2017
paper  ·  poster  ·  extended abstract (VSM@ICCV)  ·  project page  ·  bibtex

An approach to understand and predict a wide spectrum of privacy risks in images.

HADES: Hierarchical Approximate Decoding for Structured Prediction
Tribhuvanesh Orekondy (under supervision of Martin Jaggi, Aurelien Lucchi, Thomas Hoffman )
Master Thesis, 2016
paper  ·  project page  ·  bibtex

A fast structured output learning algorithm, which works by approximately decoding oracles to various extents.