Title: SMA-CLMPNet: spatial multiscale attention enabled convolutional distributed memory network for intra-frame video forgery detection

Authors: Neha Dhiman; Hakam Singh; Abhishek

Addresses: Department of Computer Science and Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India ' Department of Computer Science and Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India ' Department of Computer Science and Engineering, Chitkara University School of Engineering and Technology, Chitkara University, Baddi, Himachal Pradesh, India

Abstract: Video forgery detection presents a vital threat to digital media authenticity because it allows harmful modifications through deepfakes, splicing, and frame duplication methods. Earlier techniques lack sufficient capability to capture long-range temporal dependencies and to maintain cross-channel correlations which degrade their detection performance. Hence, this research proposes a spatial multiscale attention coupled convolutional distributed long short-term memory based modified pooling network (SMA-CLMPNet) for detecting intra-frame video forgeries. The framework processes spatial, channel, and multi-scale features to capture complex forged patterns. Additionally, the distributed LSTM and modified pooling techniques in CLMPNet address temporal inconsistencies by selectively aggregating the most informative features while reducing the loss of critical information. The experimental results are analysed using the Face Forensics++ dataset. At the training percentage of 90%, the framework attained an accuracy of 97.92%, sensitivity of 97.34%, and specificity of 98.20%, showcasing it as the prominent solution for detecting manipulations and forgeries in videos.

Keywords: video forgery detection; digital forensics; deep learning; digital video security; feature extraction; attention mechanism.

DOI: 10.1504/IJCSE.2026.155102

International Journal of Computational Science and Engineering, 2026 Vol.29 No.4, pp.344 - 371

Received: 07 Jun 2025
Accepted: 02 Dec 2025
Published online: 27 Jul 2026 *

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