TweepFake：关于检测Deepfake推文
TweepFake: about Detecting Deepfake Tweets
摘要：
语言建模方面的最新进展显著提高了深度神经模型的生成能力：2019年，OpenAI发布了GPT-2，这是一种预先训练的语言模型，可以自主生成连贯、非平凡且类似人类的文本样本。从那时起，更强大的文本生成模型已经开发出来。对手可以利用这些巨大的生成能力来增强社交机器人，这些机器人将有能力编写看似合理的deepfake消息，希望污染公共辩论。为了防止这种情况，开发Deepfake社交媒体消息检测系统至关重要。然而，据我们所知，还没有人解决过在Twitter或Facebook等社交网络上检测机器生成文本的问题。为了帮助这一检测领域的研究，我们收集了\real deepfake tweepfake tweet的第一个数据集TweepFake。这是真实的，因为每个deepfake tweet实际上都是在Twitter上发布的。我们收集了来自23个机器人的推文，模仿了17个人类账户。机器人基于各种生成技术，即，马尔可夫链，RNN，RNN+Markov，LSTM，GPT-2。我们还随机选择了机器人模仿的人类推文，以获得25，572条推文的整体平衡数据集（一半是人类，一半是机器人生成的）。该数据集在Kaggle上公开提供。最后，我们评估了13种deepfake文本检测方法（基于各种最先进的方法），以展示Tweepfake带来的挑战，并创建检测技术的坚实基线。我们希望TweepFake也能提供解决社交媒体消息中的deepfake检测的机会。
英文摘要：
The recent advances in language modeling significantly improved the generative capabilities of deep neural models: in 2019 OpenAI released GPT-2, a pre-trained language model that can autonomously generate coherent, non-trivial and human-like text samples. Since then, ever more powerful text generative models have been developed. Adversaries can exploit these tremendous generative capabilities to enhance social bots that will have the ability to write plausible deepfake messages, hoping to contaminate public debate. To prevent this, it is crucial to develop deepfake social media messages detection systems. However, to the best of our knowledge no one has ever addressed the detection of machine-generated texts on social networks like Twitter or Facebook. With the aim of helping the research in this detection field, we collected the first dataset of \real deepfake tweets, TweepFake. It is real in the sense that each deepfake tweet was actually posted on Twitter. We collected tweets from a total of 23 bots, imitating 17 human accounts. The bots are based on various generation techniques, i.e., Markov Chains, RNN, RNN+Markov, LSTM, GPT-2. We also randomly selected tweets from the humans imitated by the bots to have an overall balanced dataset of 25,572 tweets (half human and half bots generated). The dataset is publicly available on Kaggle. Lastly, we evaluated 13 deepfake text detection methods (based on various state-of-the-art approaches) to both demonstrate the challenges that Tweepfake poses and create a solid baseline of detection techniques. We hope that TweepFake can offer the opportunity to tackle the deepfake detection on social media messages as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
