Adversarial ML | Jason S. Lucas

AI Robustness & Adversarial Safety

Sun, 01 Feb 2026 00:00:00 +0000

AI systems deployed in the real world must withstand adversarial manipulation and perform reliably across the full spectrum of human language variation. This project investigates how dialect diversity, authorship obfuscation, and expert-level text editing expose critical vulnerabilities in content detection systems. From stress-testing harmful content classifiers across 50 English dialects to evaluating robustness against sophisticated evasion techniques, this work reveals that the Digital Language Divide is not only a gap in language coverage but also a security vulnerability—one that adversaries can exploit when AI systems are brittle to linguistic variation.

Related Publications:

DIA-HARM (2026) — Harmful content detection robustness across 50 dialects
Authorship Obfuscation in Multilingual MGT Detection (2024, EMNLP)
BEEMO (2025, NAACL) — Expert-edited machine-generated outputs benchmark

BLUFF: Benchmarking in Low-resoUrce Languages for detecting Falsehoods and Fake news

Sun, 01 Feb 2026 00:00:00 +0000

BLUFF is the largest multilingual fake news detection benchmark to date, spanning 79 languages (20 high-resource “big-head” + 59 low-resource “long-tail”) with over 202,000 samples. The benchmark combines human-written fact-checked content from 130 IFCN-certified organizations with LLM-generated content from 19 diverse models.

Key contributions include:

AXL-CoI (Adversarial Cross-Lingual Agentic Chain-of-Interactions): A multi-agentic framework using 10 fake chains and 8 real chains for controlled multilingual content generation
mPURIFY: A 4-stage quality filtering pipeline with 32 features across 5 dimensions, ensuring dataset integrity through asymmetric evaluation thresholds
Bidirectional translation: English↔X coverage across 70+ languages with 4 prompt variants
Comprehensive evaluation: State-of-the-art detectors suffer up to 25.3% Macro-F1 degradation on low-resource versus high-resource languages

Resources:

Fighting Fire with Fire: The Dual Role of LLMs in Crafting and Detecting Elusive Disinformation

Tue, 05 Dec 2023 00:00:00 +0000

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