Related Experiment Video
Updated: Jan 19, 2026

De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
Published on: February 18, 2022
Data-Loss resilience video steganography using frame reference and data ensemble reconstruction
Feng Yong Li1, Jiang Yu2, Yan Li Ren3
1College of Computer Science and Technology, Shanghai University of Electric Power, Shanghai, P.R.China.
Abstract:
In this paper, we propose a robust video steganographic method, which can efficiently hide confidential messages in video sequences, and ensure that these messages are perfectly reconstructed by recipient. To apply proposed scheme to video sequences, we must be faced with two nontrivial problems: (a) how to effectively minimize the total steganographic distortion for each video frame? (b) how to recover the hidden messages if some frames are lost or damaged? We tackle the first question by designing a new distortion function, which employs two continuous adjacent frames with the same scene as side-information. The second question is addressed by data sharing. In this mechanism, the original data is expanded and split into multiple shares by using multi-ary Vandermonde matrix. Since these shares contain a lot of data redundancy, the recipient can recover the hidden data even if some frames are damaged or lost during delivery. Extensive experiments show that proposed scheme outperforms the state-of-the-arts in terms of robustness and diverse attacks.
More Related Videos
07:59Author Spotlight: Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
Published on: June 9, 2023
10:51Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
Published on: January 15, 2018
Related Concept Videos
08:23De novo Identification of Actively Translated Open Reading Frames with Ribosome Profiling Data
Inertial Frames of Reference
Non-inertial Frames of Reference
07:59Alignment of Synchronized Time-Series Data Using the Characterizing Loss of Cell Cycle Synchrony Model for Cross-Experiment Comparisons
10:51Frame-by-Frame Video Analysis of Idiosyncratic Reach-to-Grasp Movements in Humans
08:25Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment