Year: 2023
Type: preprint
Abstract: Recent research has highlighted the vulnerability of Deep Neural Networks (DNNs) against data poisoning attacks. These attacks aim to inject poisoning samples into the models' training dataset such that the trained models have inference failures. While previous studies have executed different types of attacks, one major challenge that greatly limits their effectiveness is the uncertainty of the re-training process after the injection of poisoning samples, including the re-training initialization or algorithms. To address this challenge, we propose a novel attack method called ''Sharpness-Aware Data Poisoning Attack (SAPA)''. In particular, it leverages the concept of DNNs' loss landscape sharpness to optimize the poisoning effect on the worst re-trained model. It helps enhance the preservation of the poisoning effect, regardless of the specific retraining procedure employed. Extensive experiments demonstrate that SAPA offers a general and principled strategy that significantly enhances various types of poisoning attacks.
Source: arXiv (Cornell University)
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G125128811, G1432373144, Collaborative Research: Advanced Quantitative and Computational Methods for STEM Education Research, CAREER: Real-World Networks: Modeling and Analysis of Signed Networks with Positive and Negative Links, III:Medium:Computation and Communication Efficient Distributed Learning, Collaborative Research: SaTC: CORE: Medium: Safeguarding Next-Generation Emergency Services (NG-9-1-1) over Cellular Networks: From Design to Practice, Intelligent, Adaptive Program with Just-in-time Feedback for Preservice Teachers, Collaborative Research: III: Medium: Graph Neural Networks for Heterophilous Data: Advancing the Theory, Models, and Applications, TRTech-PGR: Maize Hub: An Open-source Simulation Platform & Resources Integrating Extensive Field Trial Data, DNA Sequences, and Historical Weather Records, G7452299184, TRTech-PGR: Connecting sequences to functions within and between species through computational modeling and experimental studies
OpenAlex ID: W4378474075