765 citations · 3.7k across the 105 of their papers we have counts for
12 papers · 1 filter
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation
Yang He, Shadi Rahimian, Bernt Schiele +1
Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collecti…
Towards Causal VQA: Revealing and Reducing Spurious Correlations by Invariant and Covariant Semantic Editing
Vedika Agarwal, Rakshith Shetty, Mario Fritz
Despite significant success in Visual Question Answering (VQA), VQA models have been shown to be notoriously brittle to linguistic variations in the questions. Due to deficiencies…
"Best-of-Many-Samples" Distribution Matching
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders…
GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models
Dingfan Chen, Ning Yu, Yang Zhang +1
Deep learning has achieved overwhelming success, spanning from discriminative models to generative models. In particular, deep generative models have facilitated a new level of per…
VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity
Sahar Abdelnabi, Katharina Krombholz, Mario Fritz
Phishing websites are still a major threat in today's Internet ecosystem. Despite numerous previous efforts, similarity-based detection methods do not offer sufficient protection f…
Conditional Flow Variational Autoencoders for Structured Sequence Prediction
Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz +2
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world. Prior work for s…
Interpretability Beyond Classification Output: Semantic Bottleneck Networks
Max Losch, Mario Fritz, Bernt Schiele
Today's deep learning systems deliver high performance based on end-to-end training. While they deliver strong performance, these systems are hard to interpret. To address this iss…
Prediction Poisoning: Towards Defenses Against DNN Model Stealing Attacks
Tribhuvanesh Orekondy, Bernt Schiele, Mario Fritz
High-performance Deep Neural Networks (DNNs) are increasingly deployed in many real-world applications e.g., cloud prediction APIs. Recent advances in model functionality stealing…
SampleFix: Learning to Generate Functionally Diverse Fixes
Hossein Hajipour, Apratim Bhattacharyya, Cristian-Alexandru Staicu +1
Automatic program repair holds the potential of dramatically improving the productivity of programmers during the software development process and correctness of software in genera…
Body Shape Privacy in Images: Understanding Privacy and Preventing Automatic Shape Extraction
Hosnieh Sattar, Katharina Krombholz, Gerard Pons-Moll +1
Modern approaches to pose and body shape estimation have recently achieved strong performance even under challenging real-world conditions. Even from a single image of a clothed pe…
Learning Manipulation under Physics Constraints with Visual Perception
Wenbin Li, Aleš Leonardis, Jeannette Bohg +1
Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing nov…
Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning
Ahmed Salem, Apratim Bhattacharya, Michael Backes +2
Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a…