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News plays a significant role in shaping people's beliefs and opinions. Fakenews has always been a problem, which wasn't exposed to the mass public untilthe past election cycle for the 45th President of the United States. Whilequite a few detection methods have been proposed to combat fake news since2015, they focus mainly on linguistic aspects of an article without any factchecking. In this paper, we argue that these models have the potential tomisclassify fact-tampering fake news as well as under-written real news.Through experiments on Fakebox, a state-of-the-art fake news detector, we showthat fact tampering attacks can be effective. To address these weaknesses, weargue that fact checking should be adopted in conjunction with linguisticcharacteristics analysis, so as to truly separate fake news from real news. Acrowdsourced knowledge graph is proposed as a straw man solution to collectingtimely facts about news events.

Fake News Detection via NLP is Vulnerable to Adversarial Attacks