Employing paraphrasing tools to conceal plagiarized text is a severe threat to academic integrity. To enable the detection of machine-paraphrased text, we evaluate the effectiveness of five pre-trained word embedding models combined with machine learning classifiers and state-of-the-art neural language models. We analyze preprints of research papers, graduation theses, and Wikipedia articles, which we paraphrased using different configurations of the tools SpinBot and SpinnerChief. The best performing technique, Longformer, achieved an average F1 score of 80.99% (F1=99.68% for SpinBot and F1=71.64% for SpinnerChief cases), while human evaluators achieved F1=78.4% for SpinBot and F1=65.6% for SpinnerChief cases. We show that the automated classification alleviates shortcomings of widely-used text-matching systems, such as Turnitin and PlagScan. To facilitate future research, all data, code, and two web applications showcasing our contributions are openly available.

Cheaters ruin online competitive games, but Valve might be aiming to solve the problem once-and-for-all. Valve really doesn't like cheaters, and the company is working on an artificial intelligence to combat them head on. In a Reddit post, Valve confirmed that it has started work on an AI that is learning the difference between cheaters and highly skilled players. The AI has already started reporting bad eggs. More: Valve responds to requests to shut down'Counter-Strike' weapon skin trading On the Global Offensive Subreddit, users posited why Valve had not created some kind of safeguard against cheaters who use spinbots.