57 citations · 143 across the 16 of their papers we have counts for
16 papers
How Context Attribution Handles What the Model Already Knows
Quoc-Huy Trinh, Lin Zhu, Sebastian Szyller
Context attribution methods for large language models (LLMs) identify which input context contributes to the model response. Recent works show the initial success in attributing th…
Amulet: a Python Library for Assessing Interactions Among ML Defenses and Risks
Asim Waheed, Vasisht Duddu, Sebastian Szyller
Machine learning (ML) models are susceptible to various risks to security, privacy, and fairness. Most defenses are designed to protect against each risk individually (intended int…
Atlas: A Framework for ML Lifecycle Provenance & Transparency
Marcin Spoczynski, Marcela S. Melara, Sebastian Szyller
The rapid adoption of open source machine learning (ML) datasets and models exposes today's AI applications to critical risks like data poisoning and supply chain attacks across th…
Soft Token Attacks Cannot Reliably Audit Unlearning in Large Language Models
Haokun Chen, Sebastian Szyller, Weilin Xu +1
Large language models (LLMs) are trained using massive datasets, which often contain undesirable content such as harmful texts, personal information, and copyrighted material. To a…
Imperceptible Adversarial Examples in the Physical World
Weilin Xu, Sebastian Szyller, Cory Cornelius +5
Adversarial examples in the digital domain against deep learning-based computer vision models allow for perturbations that are imperceptible to human eyes. However, producing simil…
SoK: Unintended Interactions among Machine Learning Defenses and Risks
Vasisht Duddu, Sebastian Szyller, N. Asokan
Machine learning (ML) models cannot neglect risks to security, privacy, and fairness. Several defenses have been proposed to mitigate such risks. When a defense is effective in mit…
LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked
Mansi Phute, Alec Helbling, Matthew Hull +4
Large language models (LLMs) are popular for high-quality text generation but can produce harmful content, even when aligned with human values through reinforcement learning. Adver…
False Claims against Model Ownership Resolution
Jian Liu, Rui Zhang, Sebastian Szyller +2
Deep neural network (DNN) models are valuable intellectual property of model owners, constituting a competitive advantage. Therefore, it is crucial to develop techniques to protect…
On the Robustness of Dataset Inference
Sebastian Szyller, Rui Zhang, Jian Liu +1
Machine learning (ML) models are costly to train as they can require a significant amount of data, computational resources and technical expertise. Thus, they constitute valuable i…
Conflicting Interactions Among Protection Mechanisms for Machine Learning Models
Sebastian Szyller, N. Asokan
Nowadays, systems based on machine learning (ML) are widely used in different domains. Given their popularity, ML models have become targets for various attacks. As a result, resea…
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning
Vasisht Duddu, Sebastian Szyller, N. Asokan
Data used to train machine learning (ML) models can be sensitive. Membership inference attacks (MIAs), attempting to determine whether a particular data record was used to train an…
Good Artists Copy, Great Artists Steal: Model Extraction Attacks Against Image Translation Models
Sebastian Szyller, Vasisht Duddu, Tommi Gröndahl +1
Machine learning models are typically made available to potential client users via inference APIs. Model extraction attacks occur when a malicious client uses information gleaned f…
Detecting organized eCommerce fraud using scalable categorical clustering
Samuel Marchal, Sebastian Szyller
Online retail, eCommerce, frequently falls victim to fraud conducted by malicious customers (fraudsters) who obtain goods or services through deception. Fraud coordinated by groups…
Extraction of Complex DNN Models: Real Threat or Boogeyman?
Buse Gul Atli, Sebastian Szyller, Mika Juuti +2
Recently, machine learning (ML) has introduced advanced solutions to many domains. Since ML models provide business advantage to model owners, protecting intellectual property of M…
DAWN: Dynamic Adversarial Watermarking of Neural Networks
Sebastian Szyller, Buse Gul Atli, Samuel Marchal +1
Training machine learning (ML) models is expensive in terms of computational power, amounts of labeled data and human expertise. Thus, ML models constitute intellectual property (I…
PRADA: Protecting against DNN Model Stealing Attacks
Mika Juuti, Sebastian Szyller, Samuel Marchal +1
Machine learning (ML) applications are increasingly prevalent. Protecting the confidentiality of ML models becomes paramount for two reasons: (a) a model can be a business advantag…