Securing Phygital Gameplay: Strategies for Video-Replay Spoofing Detection

Abstract

Physical Virtual Sports (PVS) utilize digital technologies for the analysis and evaluation of sports performances. This research article addresses the challenge of detecting video-replay spoofing in PVS, with a specific focus on a digital football sport aimed at assessing and improving a player’s football juggling skills. In the context of the growing presence of digital coaches as well as PVS, accurate assessment of player performance and identification of deceptive practices in these applications are paramount. The proliferation of sophisticated technologies, such as deepfake algorithms and computer vision techniques, has facilitated the manipulation of video replays, deceiving both viewers and officials. To tackle the challenges associated with video-replay spoofing, this article introduces a meticulously curated dataset comprising 600 players engaged in the digital football sport. Additionally, the dataset includes video-replay spoofing videos captured on a wide range of display devices. A deep learning-based model is developed and trained on this dataset, achieving an accuracy rate of approximately 95%. Generalization studies were also conducted to assess the model’s ability to generalize to unseen scenarios and datasets. The ROC-AUC score highlighted the model’s discriminative power across different threshold values, validating its effectiveness in distinguishing between genuine and spoofed video replays. The results demonstrate that our trained model exhibited consistent performance across multiple public face biometric spoofing datasets, underscoring its robustness against sophisticated video-replay attacks in various domains. Additionally, ablation studies were carried out by systematically removing or modifying the model’s backbone architectures to analyze their effects on detection accuracy and reliability. Furthermore, computational complexity analysis was presented to evaluate the model’s efficiency in terms of time and space requirements. The findings underscore the scientific significance and relevance of video replay spoof detection in PVS. By presenting a novel dataset (https://www.fiteq.org/research) and employing an advanced deep learning approach, this article contributes to the scientific community’s understanding and progress in combating fraudulent practices, ultimately preserving the integrity and fairness of digital sports applications.