Swin-3DART: An Efficient and Robust Lightweight Transformer for Video Anomaly Detection with TG-RGB+

Video anomaly detection is vital for public safety but remains challenging due to complex motion patterns, limited robustness to motion-related perturbations, and the heavy computation demands of modern transformers. To address these challenges, swin-3DART is introduced as a unified framework that improves both efficiency and resilience. First, the proposed TG-RGB(+) modality enhances RGB frames with temporal-gradient motion cues, improving motion sensitivity. Second, it designs T-GAP (temporal gradient adaptive perturbation), which generates worst-case perturbations to expose vulnerabilities and strengthen the model through adversarial training. Third, an adversarial defence mechanism is embedded to ensure robustness, achieving consistently low attack success rates (4%-7%) across datasets. Finally, the framework incorporates the 3DART (3D adaptive receive transformer), which reduces memory footprint by similar to 12% and FLOPs by similar to 11.9%, making it suitable for deployment in real-time surveillance or edge computing scenarios. Comprehensive evaluations show that swin-3DART achieves state-of-the-art AUCs of 95% on UBI fights, 86% on UCF-crime, and 99% on RLVS. These results highlight swin-3DART's potential as an efficient and robust solution for real-time, safety-critical video anomaly detection.