765 citations · 3.7k across the 105 of their papers we have counts for
21 papers · 1 filter
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…
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…
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.…
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…
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…
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models
Yugeng Liu, Rui Wen, Xinlei He +6
Inference attacks against Machine Learning (ML) models allow adversaries to learn sensitive information about training data, model parameters, etc. While researchers have studied,…
Responsible Disclosure of Generative Models Using Scalable Fingerprinting
Ning Yu, Vladislav Skripniuk, Dingfan Chen +2
Over the past years, deep generative models have achieved a new level of performance. Generated data has become difficult, if not impossible, to be distinguished from real data. Wh…
Adversarial Watermarking Transformer: Towards Tracing Text Provenance with Data Hiding
Sahar Abdelnabi, Mario Fritz
Recent advances in natural language generation have introduced powerful language models with high-quality output text. However, this raises concerns about the potential misuse of s…
Sampling Attacks: Amplification of Membership Inference Attacks by Repeated Queries
Shadi Rahimian, Tribhuvanesh Orekondy, Mario Fritz
Machine learning models have been shown to leak information violating the privacy of their training set. We focus on membership inference attacks on machine learning models which a…
Artificial Fingerprinting for Generative Models: Rooting Deepfake Attribution in Training Data
Ning Yu, Vladislav Skripniuk, Sahar Abdelnabi +1
Photorealistic image generation has reached a new level of quality due to the breakthroughs of generative adversarial networks (GANs). Yet, the dark side of such deepfakes, the mal…
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…
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…
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…
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…
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…
: Author Attribute Anonymity by Adversarial Training of Neural Machine Translation
Rakshith Shetty, Bernt Schiele, Mario Fritz
Text-based analysis methods allow to reveal privacy relevant author attributes such as gender, age and identify of the text's author. Such methods can compromise the privacy of an…
Ubic: Bridging the gap between digital cryptography and the physical world
Mark Simkin, Dominique Schroeder, Andreas Bulling +1
Advances in computing technology increasingly blur the boundary between the digital domain and the physical world. Although the research community has developed a large number of c…