765 citations · 3.7k across the 105 of their papers we have counts for
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Not Using the Car to See the Sidewalk: Quantifying and Controlling the Effects of Context in Classification and Segmentation
Rakshith Shetty, Bernt Schiele, Mario Fritz
Importance of visual context in scene understanding tasks is well recognized in the computer vision community. However, to what extent the computer vision models for image classifi…
Knockoff Nets: Stealing Functionality of Black-Box Models
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
Machine Learning (ML) models are increasingly deployed in the wild to perform a wide range of tasks. In this work, we ask to what extent can an adversary steal functionality of suc…
Attributing Fake Images to GANs: Learning and Analyzing GAN Fingerprints
Ning Yu, Larry Davis, Mario Fritz
Recent advances in Generative Adversarial Networks (GANs) have shown increasing success in generating photorealistic images. But they also raise challenges to visual forensics and…
Bayesian Prediction of Future Street Scenes using Synthetic Likelihoods
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
For autonomous agents to successfully operate in the real world, the ability to anticipate future scene states is a key competence. In real-world scenarios, future states become in…
Answering Visual What-If Questions: From Actions to Predicted Scene Descriptions
M. Wagner, H. Basevi, R. Shetty +4
In-depth scene descriptions and question answering tasks have greatly increased the scope of today's definition of scene understanding. While such tasks are in principle open ended…
Diverse Conditional Image Generation by Stochastic Regression with Latent Drop-Out Codes
Yang He, Bernt Schiele, Mario Fritz
Recent advances in Deep Learning and probabilistic modeling have led to strong improvements in generative models for images. On the one hand, Generative Adversarial Networks (GANs)…
MLCapsule: Guarded Offline Deployment of Machine Learning as a Service
Lucjan Hanzlik, Yang Zhang, Kathrin Grosse +4
With the widespread use of machine learning (ML) techniques, ML as a service has become increasingly popular. In this setting, an ML model resides on a server and users can query i…
Fashion is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources
Hosnieh Sattar, Gerard Pons-Moll, Mario Fritz
To study the correlation between clothing garments and body shape, we collected a new dataset (Fashion Takes Shape), which includes images of users with clothing category annotatio…
Adversarial Scene Editing: Automatic Object Removal from Weak Supervision
Rakshith Shetty, Mario Fritz, Bernt Schiele
While great progress has been made recently in automatic image manipulation, it has been limited to object centric images like faces or structured scene datasets. In this work, we…
Accurate and Diverse Sampling of Sequences based on a "Best of Many" Sample Objective
Apratim Bhattacharyya, Bernt Schiele, Mario Fritz
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem has been formalized…
Bayesian Prediction of Future Street Scenes through Importance Sampling based Optimization
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
For autonomous agents to successfully operate in the real world, anticipation of future events and states of their environment is a key competence. This problem can be formalized a…
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models
Ahmed Salem, Yang Zhang, Mathias Humbert +3
Machine learning (ML) has become a core component of many real-world applications and training data is a key factor that drives current progress. This huge success has led Internet…
Sequential Attacks on Agents for Long-Term Adversarial Goals
Edgar Tretschk, Seong Joon Oh, Mario Fritz
Reinforcement learning (RL) has advanced greatly in the past few years with the employment of effective deep neural networks (DNNs) on the policy networks. With the great effective…
Gradient-Leaks: Understanding and Controlling Deanonymization in Federated Learning
Tribhuvanesh Orekondy, Seong Joon Oh, Yang Zhang +2
Federated Learning (FL) systems are gaining popularity as a solution to training Machine Learning (ML) models from large-scale user data collected on personal devices (e.g., smartp…
A Hybrid Model for Identity Obfuscation by Face Replacement
Qianru Sun, Ayush Tewari, Weipeng Xu +3
As more and more personal photos are shared and tagged in social media, avoiding privacy risks such as unintended recognition becomes increasingly challenging. We propose a new hyb…
Deep Appearance Maps
Maxim Maximov, Laura Leal-Taixé, Mario Fritz +1
We propose a deep representation of appearance, i. e., the relation of color, surface orientation, viewer position, material and illumination. Previous approaches have useddeep lea…