Abstract

The rapid growth of digital multimedia sharing platforms increases the demand for secure video copyright protection and unauthorized content tracking. Conventional video watermarking approaches often suffer from low robustness, limited embedding capacity, and poor resistance against geometric and signal-processing attacks. Existing methods also exhibit inadequate detection accuracy under compressed and noisy transmission environments. These limitations create significant challenges in multimedia authentication, copyright verification, and secure video communication applications. This study presents a Hybrid Deep Neural Video Watermarking Framework that integrates attention-driven watermark embedding with intelligent tamper detection mechanisms for robust multimedia security. The proposed method combines Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Transformer-based attention modules to achieve adaptive watermark insertion and reliable extraction. Initially, video frames are decomposed into spatial and temporal components through adaptive feature learning. The embedding model identifies perceptually significant regions using an attention-guided encoder, where encrypted watermark information is inserted with minimal visual distortion. Subsequently, a dual-stage decoder network performs watermark recovery and tamper localization through deep residual feature analysis. The framework also incorporates adversarial training and adaptive noise filtering to improve resilience against compression, frame dropping, Gaussian noise, rotation, and scaling attacks. Experimental evaluation demonstrates that the proposed framework achieves a PSNR of 48.7 dB, SSIM of 0.986, NC value of 0.994, tamper detection accuracy of 98.4%, and watermark recovery accuracy of 96.7% under diverse multimedia attack environments. The framework also maintains stable extraction performance under compression, scaling, Gaussian noise, rotation, and frame-dropping attacks. Comparative analysis indicates that the proposed model improves robustness by 19.8%, perceptual quality by 12.4 dB, and watermark reconstruction accuracy by 18.5% compared with conventional DWT-SVD and CNN-based watermarking approaches.

Authors

M. Ranjithkumar, R. Karthick, A. Vasanthkumar, S. Ashiq
Knowledge Institute of Technology, India

Keywords

Video Watermarking, Deep Learning, CNN-BiLSTM, Attention Mechanism, Tamper Detection