Behavioral Signature Decoding: Facial Landmark-based Graph Learning for Cybernetic Avatar Authentication

Mohamad Ammar Alsherfawi Aljazaerly, Jianhang Zhou, Allam Shehata, Yasushi Yagi

Abstract: With the rapid advancement of AI-generated videos, distinguishing synthetic content from genuine human-driven content has become increasingly difficult, threatening the integrity of human authenticity and creative expression. In this context, Cybernetic Avatar (CA) is introduced as a digital entity that mirrors a remote operator’s facial expressions, gestures, and speech in virtual environments, posing new challenges for secure identity verification. A critical threat emerges when unauthorized users manipulate a CA, potentially deceiving both systems and human observers. This paper addresses the CA Authentication problem, which seeks to verify the true teleoperator behind a CA video despite the CA’s mutable appearance and expressive behaviors. % More specifically, we propose a robust CA authentication framework that leverages spatio-temporal facial behavior captured from the CA video to authenticate the legitimate teleoperator. To effectively learn identity-sensitive motion patterns (signature), we develop a Behavior Signature Decoder Graph Convolutional Network (BSDec-GCN) that constructs a constrained spatio-temporal graph to amplify identity-specific dynamics and suppress inter-user ambiguity. Furthermore, we introduce a dual landmark and graph-level losses that boost discrimination. The comprehensive experiments and the thorough ablation studies demonstrate the reliability of the proposed framework with a competitive performance against the existing baseline methods. To the best of our knowledge, this work presents the first graph-based learning approach tailored for Cybernetic Avatar authentication, opening a new direction for securing virtual identity in the era of AI-mediated communication.