Threat actors manipulate fake news detectors, and this has become a huge concern for users.
With the industrial journey towards digitization, information and misinformation are all over the web. Lately, it is getting trickier for internet users to identify authentic facts. With fraudulent content flooded around social media channels, many platforms are increasingly deploying fake news detectors.
Social media giants like Facebook and Twitter have already added warning tags into posts to extend awareness. Thus, users can flag online articles as false or misleading – based on the headline and content of the story. Besides, the latest methodologies have considered user engagements and network features patterns.
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These additional measures are for the content of the story in order to strengthen posts’ accuracies. However, there have been growing concerns around the fake news detectors being manipulated – based on user comments. This can show or flag genuine news as counterfeit and fake content as genuine.
The researchers from Penn State’s College of Information Sciences and Technology have recently claimed this as a risk or attack approach. It could provide the adversaries with the ability to influence the news detector’s assessment of the content – even though they are not the original author of the story.
They assessed the quality of artificially generated public comments after in-depth analysis to realize if individuals are able to differentiate them from the comments generated by real users. Principally, the rivals can use random social media profiles for posting malicious comments.
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Industry experts are working on solutions that can find and read relevance to the article. Simply put, a substantial effort to fool the detector! In fact, fake news had always been found to be promoted deliberately in order to widen political divides, undermine people’s confidence, or create community division.
Certainly, the attackers can easily exploit such dependency on users’ engagement to manipulate the detection models. Often spammer posts malicious comments on articles, highlighting the importance of having robust fake news detection models.
And hence, instead of misleading the detector by attacking the content or source of the post, such commenters are capable of attacking the sensor. Clearly, the online era sees an exponential rise in adversarial attacks. This demands the requirement for an advanced framework to generate, optimize, and add malicious comments.
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