Transformer-Based Temporal Learning for Robust Frame Deletion Detection in Videos

Video data plays an increasingly important role as digital evidence in areas such as surveillance systems, media verification, and forensic analysis. Nevertheless, the widespread availability of advanced video editing software has made it possible to perform complex temporal manipulations, including frame deletion, which can hide significant events and undermine the reliability of video evidence. Most existing frame deletion detection techniques are based on convolutional neural networks and handcrafted temporal descriptors. These approaches often exhibit limitations in modelling long-range temporal relationships and show reduced performance in low-motion scenes or under varying illumination conditions.To address these challenges, this paper proposes a Transformer-based temporal learning framework for reliable frame deletion detection in videos. The proposed method employs self-attention mechanisms to capture global temporal dependencies across video frames, allowing the identification of subtle temporal inconsistencies introduced by frame deletion. In contrast to conventional CNNbased techniques, the framework reduces dependence on frame differencing operations and manually defined statistical thresholds. By combining frame-level feature extraction with a temporal Transformer encoder, the proposed model enhances robustness across diverse motion patterns. This study demonstrates the potential of attention-driven temporal modelling in video forensics and establishes a scalable basis for future research in deep learning–based video manipulation detection.