Video Content Verification System

References

[1] Gao, et al. (2016). A Popularity-Driven Video Discovery Scheme for the Centralized P2P-VoD System. IEEE Transactions on Multimedia.

[2] G. Wu, Z. Liu, L. Yao, J. Deng, and J. Wang, “A Trust Routing for Multimedia Social Networks,” The Computer Journal, vol. 58, no. 4, pp. 688–699, Apr. 2015. This paper introduces a fuzzy-based trust model embedding both social trust and QoS metrics to improve routing in multimedia social networks.

[3] Z. Wang, “Data-driven Approaches for Social Video Distribution,” arXiv, Jun. 2015. This work presents frameworks for distributing video content via social networks by integrating data-driven insights from social propagation behaviors.

[4] Z. Wang, “Secure Delivery Method for Preserving Data Integrity of a Video Frame with Sensitive Objects,” Applied Sciences, 2023. Focuses on frame-level integrity, using logging, hashing, and signature-based verification to secure video content.

[5] Y. Sun, H. Cao, W. Qi, and J. Zhang, “Improving the security and quality of real-time multimedia transmission in cyber-physical-social systems,” J. Information Processing Systems, 2018. Addresses real-time video streaming in IoT-like networks using sliding-window retransmission and token-based tampering detection.

[6] E. Liu, Z. Liu, F. Shao, and Z. Zhang, “A Game-Theoretical Approach to Multimedia Social Networks Security,” Scientific World Journal, vol. 2014, Article ID 791690, 2014. Proposes a Nash-equilibrium-based trust-access control model in multimedia social networks.

[7] L. Masinde and K. Graffi, “Peer-to-Peer based Social Networks: A Comprehensive Survey,” arXiv preprint, Jan. 2020. Discusses decentralized, privacy-centered P2P social networks as trust-aware alternatives to centralized platforms.

[8] E. G. Virdi and N. S. Talwandi, “Visual Truth in the Digital Age: A Review of Image and Video Authentication and Verification Approaches,” J. Advanced Research in Computer Graphics and Multimedia Technology, 2021. Reviews multimedia forensics and video integrity verification—essential for combating fake or tampered content.

[9] Media forensics on social media platforms: a survey, EURASIP Journal on Information Security, 2021. Surveys multimedia source identification and forgery detection techniques especially tailored to social media contexts.

[10] M. Kosslyn and others, “Interpersonal Trust within Social Media Applications: A Conceptual Literature Review,” IntechOpen, 2020–2022. Discusses how multimodal content (e.g. video, voice) impacts trust in social media, including the challenges posed by deepfakes.

[11] E. Sun, R. Escriva, M. K. Goldberg, M. Hayvanovych, M. Magdon-Ismail, B. K. Szymanski, W. A. Wallace, and G. T. Williams, “Measuring behavioral trust in social networks,” in Proc. IEEE ISI, 2010. Investigates behavioral trust metrics that can be adapted for video-sharing platforms.

[12] CyVOD: a novel trinity multimedia social network scheme, Multimedia Tools and Applications, vol. 76, no. 18, 2016. Proposes a DRM-aware multimedia social network framework balancing content protection and usability.

[13] Thabit, “Trust management and data protection for online social networks,” IET Communications, 2022. Introduces group-key cryptography and differential privacy methods for enhancing trust and data protection in social networks.

[14] Named Data Networking security model, “Named Data Networking,” Wikipedia, 2025. Describes data-centric trust where each data packet is cryptographically signed, enabling integrity and provenance verification—a model that could inspire your framework.

@article{1710526,
      author = {Kuchannagari Sathwika, K. Balakrishna Maruthiram},
      title = {Video Content Verification System},
      journal = {Iconic Research And Engineering Journals},
      year = {2025},
      volume = {9},
      number = {3},
      pages = {256-261},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1710526.pdf},
      abstract = {The primary objective of this project is to design a framework that fosters collaboration between humans and intelligent systems to guarantee the secure delivery of trusted video content across Social Media Networks (SMNs). The framework is built upon three core principles: User Trust Assignment ? Each user is allocated a trust score based on their past behaviour and activity history. Intelligent Content Agent ? A decision-making agent determines whether content should be automatically published, flagged for human review, or rejected. Video Integrity Verification ? Continuous monitoring ensures the authenticity and integrity of video files throughout the streaming process. By integrating these mechanisms, the framework enhances the overall trustworthiness of SMNs while also contributing to optimized capital expenditure and resource utilization.},
      month = {September},
      doi = {https://doi.org/10.64388/IREV9I3-1710526-059}
  }