  It is commonly perceived that online fake news and reliable news exhibit
stark differences in writing styles, such as the use of sensationalist versus
objective language. However, we emphasize that style-related features can also
be exploited for style-based attacks. Notably, the rise of powerful Large
Language Models (LLMs) has enabled malicious users to mimic the style of
trustworthy news outlets at minimal cost. Our analysis reveals that
LLM-camouflaged fake news content leads to substantial performance degradation
of state-of-the-art text-based detectors (up to 38% decrease in F1 Score),
posing a significant challenge for automated detection in online ecosystems. To
address this, we introduce SheepDog, a style-agnostic fake news detector robust
to news writing styles. SheepDog achieves this adaptability through
LLM-empowered news reframing, which customizes each article to match different
writing styles using style-oriented reframing prompts. By employing
style-agnostic training, SheepDog enhances its resilience to stylistic
variations by maximizing prediction consistency across these diverse
reframings. Furthermore, SheepDog extracts content-focused veracity
attributions from LLMs, where the news content is evaluated against a set of
fact-checking rationales. These attributions provide supplementary information
and potential interpretability that assist veracity prediction. On three
benchmark datasets, empirical results show that SheepDog consistently yields
significant improvements over competitive baselines and enhances robustness
against LLM-empowered style attacks.

Computation and Language