BeGuard: An LSTM–Fused Defense Model Against Deepfakes in Competitive Activities–Related Social Networks
Yujie Li, Guoxu Liu, Chunlei Chen, Sun-Kyoung Kang, Andia Foroughi · International Journal of Intelligent Systems · 2025
We propose a novel defense mechanism for protecting users from deepfakes by analyzing their behaviors in competitive activities and their social interactions. The model dynamically embeds user behaviors based on their participation in competitive activities, capturing these activities’ temporal dynamics through long short–term memory networks. This allows the model to effectively identify patterns and changes in user behaviors. BeGuard also considers users’ social relationships, embedding the behaviors of their social friends to account for the influence of these connections on their actions. This results in a richer and more contextually aware behavioral representation. To improve detection accuracy, the model uses an attention mechanism to evaluate abnormal values in user behaviors, particularly those indicating potential deepfake content. This attention‐based evaluation enhances the model’s capacity to detect subtle anomalies, providing a more effective defense against deepfakes in competitive activities–related social networks.