Blockchain-Assisted Video Integrity Verification UsingResNeXt and LSTM-Based Deepfake Detection

  • Authors

    • P.Senthil Pandian Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
    • S. Manikandan Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
    • Mrs.D. Ramya Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
    • Mrs.S. Rajeswari Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
    • J. Hemalatha Department of Computer Science and Engineering, AAA College of Engineering and Technology, Sivakasi, Tamil Nadu, India
    • R.Rubesh Selvakumar Department of Computer Science and Engineering, Sethu Institute of Technology, Viruthunagar, Tamil Nadu, India
    • S. Mothilal Professor & Principal, Department of Mechanical Engineering, Vagai College of Engineering, Madurai
    https://doi.org/10.14419/v12sar21

    Received date: July 2, 2025

    Accepted date: August 25, 2025

    Published date: August 31, 2025

  • Deepfake Video Detection, Facial Image Analysis, ResNext feature, Load Trained Model

  • Abstract

    With the rapid advancement of deep learning algorithms, synthetic media commonly known as deepfakes have reached a level of realism that makes them nearly indistinguishable from authentic human appearances. Such content poses serious threats, including misinformation, impersonation, and cybercrimes. In this paper, we propose a novel blockchain-assisted framework for the detection and validation of deep-fake videos. Our approach integrates deep learning and decentralized verification mechanisms to enhance multimedia integrity. Frame-level features are extracted using a ResNeXt Convolutional Neural Network, and these are further processed using a Recurrent Neural Network (RNN) equipped with Long Short-Term Memory (LSTM) units to analyze temporal patterns across video sequences. To ensure tamper-proof logging and traceability, detection results and video metadata are immutably stored and verified on a Blockchain ledger. This combination not only improves classification accuracy but also provides a transparent and secure method for verifying video authenticity. Experimental comparisons demonstrate that our Blockchain-integrated model outperforms existing methods in both accuracy and reliability, contributing to the state-of-the-art in secure multimedia authentication.

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  • How to Cite

    Pandian , P. ., Manikandan , S., Ramya, M., Rajeswari , M., Hemalatha , J., Selvakumar , R. ., & Mothilal , S. (2025). Blockchain-Assisted Video Integrity Verification UsingResNeXt and LSTM-Based Deepfake Detection. International Journal of Basic and Applied Sciences, 14(4), 802-807. https://doi.org/10.14419/v12sar21