Adversarial Data Poisoning for Fake News Detection: How to Make a Model Misclassify a Target News without Modifying It. (arXiv:2312.15228v2 [cs.LG] UPDATED)

Fake news detection models are critical to countering disinformation but can
be manipulated through adversarial attacks. In this position paper, we analyze
how an attacker can compromise the performance of an online learning detector
on specific news content without being able to manipulate the original target
news. In some contexts, such as social networks, where the attacker cannot
exert complete control over all the information, this scenario can indeed be
quite plausible. Therefore, we show how an attacker could potentially …

adversarial adversarial attacks attacker attacks compromise critical data data poisoning detection detector disinformation fake fake news online learning performance poisoning target