Set-Membership Inference Attacks using Data Watermarking
In this work, we propose a set-membership inference attack for generative models using deep image watermarking techniques. In particular, we demonstrate how conditional sampling from a generative model can reveal the watermark that was injected into parts of the training data. Our empirical results demonstrate that the proposed watermarking technique is a principled approach for detecting the non-consensual use of image data in training generative models.
Code (0)
등록된 구현이 없습니다.
Tasks
Inference AttackMembership Inference AttackSimilar Papers 제목 키워드 기반
Watermarking for Proprietary Dataset Protection
A growing body of literature suggests that training data membership inference problems are fundamentally hard tasks in modern language modeling settings. We argue that output watermarking techniques are the right gadget …
Robust Membership Encoding: Inference Attacks and Copyright Protection for Deep Learning
Machine learning as a service (MLaaS), and algorithm marketplaces are on a rise. Data holders can easily train complex models on their data using third party provided learning codes. Training accurate ML models requires …
Deep LearningModel CompressionCan Watermarking Large Language Models Prevent Copyrighted Text Generation and Hide Training Data?
Large Language Models (LLMs) have demonstrated impressive capabilities in generating diverse and contextually rich text. However, concerns regarding copyright infringement arise as LLMs may inadvertently produce copyrigh…
Text GenerationLabel-Only Membership Inference Attacks
Membership inference attacks are one of the simplest forms of privacy leakage for machine learning models: given a data point and model, determine whether the point was used to train the model. Existing membership infere…
L2 RegularizationOn the (In)Feasibility of Attribute Inference Attacks on Machine Learning Models
With an increase in low-cost machine learning APIs, advanced machine learning models may be trained on private datasets and monetized by providing them as a service. However, privacy researchers have demonstrated that th…
AttributeBIG-bench Machine LearningInference Attack