AttrBkd uses fine-grained stylistic attributes as backdoor triggers, achieving higher human-reported subtlety and comparable or higher attack success than prior conspicuous triggers.
Adversarial Machine Learning -- Industry Perspectives
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
abstract
Based on interviews with 28 organizations, we found that industry practitioners are not equipped with tactical and strategic tools to protect, detect and respond to attacks on their Machine Learning (ML) systems. We leverage the insights from the interviews and we enumerate the gaps in perspective in securing machine learning systems when viewed in the context of traditional software security development. We write this paper from the perspective of two personas: developers/ML engineers and security incident responders who are tasked with securing ML systems as they are designed, developed and deployed ML systems. The goal of this paper is to engage researchers to revise and amend the Security Development Lifecycle for industrial-grade software in the adversarial ML era.
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes
AttrBkd uses fine-grained stylistic attributes as backdoor triggers, achieving higher human-reported subtlety and comparable or higher attack success than prior conspicuous triggers.