ArticleEntropy (Basel, Switzerland)2023
Efficient Video Watermarking Algorithm Based on Convolutional Neural Networks with Entropy-Based Information Mapper.
Article in Entropy (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Deep Learning for Image Watermarking: A Comprehensive Review and Analysis of Techniques, Challenges, and Applications.Sensors (Basel, Switzerland) · 2026Review
- Outdoor Microphone Range Tests and Spectral Analysis of UAV Acoustic Signatures for Array Development.Sensors (Basel, Switzerland) · 2025Article
- Optimization of Imaging Reconnaissance Systems Using Super-Resolution: Efficiency Analysis in Interference Conditions.Sensors (Basel, Switzerland) · 2024Article
- Separable Reversible Data Hiding in Encrypted Images for Remote Sensing Images.Entropy (Basel, Switzerland) · 2023Article
- A Dual Blind Watermarking Method for 3D Models Based on Normal Features.Entropy (Basel, Switzerland) · 2023Article
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Authors and funding
2 authors.
Funding
Abstract
This paper presents a method for the transparent, robust, and highly capacitive watermarking of video signals using an information mapper. The proposed architecture is based on the use of deep neural networks to embed the watermark in the luminance channel in the YUV color space. An information mapper was used to enable the transformation of a multi-bit binary signature of varying capacitance reflecting the entropy measure of the system into a watermark embedded in the signal frame. To confirm the effectiveness of the method, tests were carried out for video frames with a resolution of 256 × 256 pixels, with a watermark capacity of 4 to 16,384 bits. Transparency metrics (SSIM and PSNR) and a robustness metric-the bit error rate (BER)-were used to assess the performance of the algorithms.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.