765 citations · 3.7k across the 105 of their papers we have counts for
116 papers
From Attachments to SEO: Click Here to Learn More about Clickbait PDFs!
Giada Stivala, Sahar Abdelnabi, Andrea Mengascini +3
Clickbait PDFs are PDF documents that do not embed malware but trick victims into visiting malicious web pages leading to attacks like password theft or drive-by download. While re…
MargCTGAN: A "Marginally'' Better CTGAN for the Low Sample Regime
Tejumade Afonja, Dingfan Chen, Mario Fritz
The potential of realistic and useful synthetic data is significant. However, current evaluation methods for synthetic tabular data generation predominantly focus on downstream tas…
From Bad to Worse: Using Private Data to Propagate Disinformation on Online Platforms with a Greater Efficiency
Protik Bose Pranto, Waqar Hassan Khan, Sahar Abdelnabi +3
We outline a planned experiment to investigate if personal data (e.g., demographics and behavioral patterns) can be used to selectively expose individuals to disinformation such th…
B-cos Alignment for Inherently Interpretable CNNs and Vision Transformers
Moritz Böhle, Navdeeppal Singh, Mario Fritz +1
We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace t…
Private and Collaborative Kaplan-Meier Estimators
Shadi Rahimian, Raouf Kerkouche, Ina Kurth +1
Kaplan-Meier estimators are essential tools in survival analysis, capturing the survival behavior of a cohort. Their accuracy improves with large, diverse datasets, encouraging dat…
Client-specific Property Inference against Secure Aggregation in Federated Learning
Raouf Kerkouche, Gergely Ács, Mario Fritz
Federated learning has become a widely used paradigm for collaboratively training a common model among different participants with the help of a central server that coordinates the…
Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection
Kai Greshake, Sahar Abdelnabi, Shailesh Mishra +3
Large Language Models (LLMs) are increasingly being integrated into various applications. The functionalities of recent LLMs can be flexibly modulated via natural language prompts.…
Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
Derui Zhu, Dingfan Chen, Jens Grossklags +1
In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applicati…
CodeLMSec Benchmark: Systematically Evaluating and Finding Security Vulnerabilities in Black-Box Code Language Models
Hossein Hajipour, Keno Hassler, Thorsten Holz +2
Large language models (LLMs) for automatic code generation have achieved breakthroughs in several programming tasks. Their advances in competition-level programming problems have m…
FedLAP-DP: Federated Learning by Sharing Differentially Private Loss Approximations
Hui-Po Wang, Dingfan Chen, Raouf Kerkouche +1
Conventional gradient-sharing approaches for federated learning (FL), such as FedAvg, rely on aggregation of local models and often face performance degradation under differential…
Holistically Explainable Vision Transformers
Moritz Böhle, Mario Fritz, Bernt Schiele
Transformers increasingly dominate the machine learning landscape across many tasks and domains, which increases the importance for understanding their outputs. While their attenti…
Private Set Generation with Discriminative Information
Dingfan Chen, Raouf Kerkouche, Mario Fritz
Differentially private data generation techniques have become a promising solution to the data privacy challenge -- it enables sharing of data while complying with rigorous privacy…
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…
Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems
Sahar Abdelnabi, Mario Fritz
Mis- and disinformation are a substantial global threat to our security and safety. To cope with the scale of online misinformation, researchers have been working on automating fac…
RelaxLoss: Defending Membership Inference Attacks without Losing Utility
Dingfan Chen, Ning Yu, Mario Fritz
As a long-term threat to the privacy of training data, membership inference attacks (MIAs) emerge ubiquitously in machine learning models. Existing works evidence strong connection…
B-cos Networks: Alignment is All We Need for Interpretability
Moritz Böhle, Mario Fritz, Bernt Schiele
We present a new direction for increasing the interpretability of deep neural networks (DNNs) by promoting weight-input alignment during training. For this, we propose to replace t…
Practical Challenges in Differentially-Private Federated Survival Analysis of Medical Data
Shadi Rahimian, Raouf Kerkouche, Ina Kurth +1
Survival analysis or time-to-event analysis aims to model and predict the time it takes for an event of interest to happen in a population or an individual. In the medical context…
Open-Domain, Content-based, Multi-modal Fact-checking of Out-of-Context Images via Online Resources
Sahar Abdelnabi, Rakibul Hasan, Mario Fritz
Misinformation is now a major problem due to its potential high risks to our core democratic and societal values and orders. Out-of-context misinformation is one of the easiest and…
ProgFed: Effective, Communication, and Computation Efficient Federated Learning by Progressive Training
Hui-Po Wang, Sebastian U. Stich, Yang He +1
Federated learning is a powerful distributed learning scheme that allows numerous edge devices to collaboratively train a model without sharing their data. However, training is res…
Optimising for Interpretability: Convolutional Dynamic Alignment Networks
Moritz Böhle, Mario Fritz, Bernt Schiele
We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA Nets), which are performant classifiers with a high degree of inherent inte…
Euro-PVI: Pedestrian Vehicle Interactions in Dense Urban Centers
Apratim Bhattacharyya, Daniel Olmeda Reino, Mario Fritz +1
Accurate prediction of pedestrian and bicyclist paths is integral to the development of reliable autonomous vehicles in dense urban environments. The interactions between vehicle a…
Beyond the Spectrum: Detecting Deepfakes via Re-Synthesis
Yang He, Ning Yu, Margret Keuper +1
The rapid advances in deep generative models over the past years have led to highly {realistic media, known as deepfakes,} that are commonly indistinguishable from real to human ey…
Convolutional Dynamic Alignment Networks for Interpretable Classifications
Moritz Böhle, Mario Fritz, Bernt Schiele
We introduce a new family of neural network models called Convolutional Dynamic Alignment Networks (CoDA-Nets), which are performant classifiers with a high degree of inherent inte…
"What's in the box?!": Deflecting Adversarial Attacks by Randomly Deploying Adversarially-Disjoint Models
Sahar Abdelnabi, Mario Fritz
Machine learning models are now widely deployed in real-world applications. However, the existence of adversarial examples has been long considered a real threat to such models. Wh…