Detecting Adversarial Examples of Fake News via the Neurons Activation State

Fan Hsun Tseng, Jiang Yi Zeng, Hsin Hung Cho, Kuo Hui Yeh, Chi Yuan Chen

研究成果: Article同行評審

3 引文 斯高帕斯(Scopus)

摘要

Due to the development of technologies, such as the Internet and mobile communication, news production is increasing day by day. Proper news delivery can lead to a thriving economy and disseminate knowledge. However, in addition to disrupting the existing order, fake news may create incorrect values and even beliefs. Therefore, detecting the authenticity of news is an extremely important issue. At present, many scholars have used artificial intelligence (AI) to detect fake news, achieving excellent results. However, once humans become dependent on AI, adversarial examples (AEs) can deceive the AI model and allow humans to receive false information. We have discovered that samples from different categories result in distinct and independent activation state distributions for each neuron. Therefore, this study proposes a method that detects adversarial samples of fake news by observing the activation states of neurons and modeling them as a Poisson distribution. The results of the experiment showed that our method can effectively detect AEs mixed in normal data and remove them, thereby improving the classification accuracy of the model by about 17%. The experimental results show that the method proposed in this article can improve the detection accuracy of fake news AEs.

原文English
頁(從 - 到)5199-5209
頁數11
期刊IEEE Transactions on Computational Social Systems
11
發行號4
DOIs
出版狀態Published - 2024

All Science Journal Classification (ASJC) codes

  • 建模與模擬
  • 社會科學(雜項)
  • 人機介面

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