The audio-video synchronization of MKV Container Format is exploited to achieve data hiding

Overview

1.0 Data Hiding in MKV Container Format

1.1 Brief Description

The audio-video synchronization of MKV Container Format is exploited to achieve data hiding, where the hidden data can be utilized for various management purposes, including hyper-linking, annotation, and authentication

1.2 Video Demonstration @ YouTube

Data Hiding (Hidden Watermark) in MKV Container Format

1.3 Requirements

  • Linux (not tested anywhere else)
  • Python
  • .MKV reader (like VLC player)
  • All the files are required:
    • .MKV video (./VideoForTesting/2mb.mkv)
    • ./convert_xml2mkv.py
    • ./parse_and_convert_mkv2xml.py
    • ./find_data.py
    • ./hide_data.py
    • ./find
    • ./hide
  • Ensure that you have all the permission to access these files. Run the following command: chmod +x convert_xml2mkv.py && chmod +x find_data.py && chmod +x hide_data.py && chmod +x parse_and_convert_mkv2xml.py
  • If the command above doesn't work and Linux prevents your access you may use the following command on any of the affected files: chmod +x filename.extension

1.4 How To Run Data Embedding Process

Note: for screenshots refer to the end of the ./Maxim_Zaika_Data_Hiding_in_MKV_Container.pdf file

  1. Ensure 1.3 Requirements are fulfilled
  2. Run ./hide from your terminal within the folder where files are located.
  3. Enter the name of the .MKV container: 2mb.mkv.
  4. Enter the data that needs to be hidden: 'example'. Write it down!
  5. Enter the SECRET KEY that will be used to decrypt your data in the data detecting process: 'encryption key'. Write it down!
  6. Enter the timecode where data will be saved to: 10.523 or type 'help' to display all the available timecodes. Write it down!
  7. File modified_mkv.mkv should now be created that stores your hidden data.

Note: do not lose text of the hidden data, SECRET KEY, and the timecode. Otherwise, you won't be able to verify it later.

1.5 How To Run Data Detecting Process

  1. Ensure 1.3 Requirements are fulfilled
  2. Run ./find from your terminal within the folder where files are located.
  3. Enter the file name: modified_mkv.mkv.
  4. Enter the text of your hidden data: 'example'.
  5. Enter the SECRET KEY used: 'encryption key'.
  6. Enter the timecode used: 10.523.
  7. If the data is matching then it will show a success.

2.0 Data Embedding Process

2.1 Software Architecture of Data Embedding

DataEmbeddingDesign

2.2 Data Embedding Design

DataEmbeddingDesign

2.3 Data Embedding Pseudocode

Note: this is incomplete representation.

Function main {
  Set a_word -> “word that needs to be written in”
  Set encryption_key -> “key used for the encryption”
  If (length of encryption_key) < (length of a_word) {
	  Set encryption_key -> same length as a_word
  }
  Set a_word -> convert to ascii
  Set encryption_key -> convert to ascii
  Set ascii_a_word -> convert to hexadecimal
  Set ascii_encryption_key -> convert to hexadecimal
  If (length of ascii_encryption_key) < (length of ascii_a_word) { 
	  Set ascii_encryption_key = -> same length as ascii_a_word
  }
  Encrypt a_word(ascii_a_word, ascii_encryption_key, a_word) // encrypt ascii word
                                                             // using original word 
  Convert encrypted word to hexadecimal // because MKV parser accepts hexadecimals
                                        // inside the cluster’s timecode
  Timecodes = [] // read the XML file and identify the timecodes
  Set input_timecode -> “input timecode here”
  Call function embed data (filename, input_timecode, encrypted_word_in_hexadecimal_format)
}

Function embed data {
	Loop through the file {
		Identify the location of the timecode {
			Identify the location of the data inside the cluster’s timecode {
				Write-in the data
			}
		} else not found timecode {
			Try again
		}
	}
}

3.0 Data Detecting Process

3.1 Software Architecture of Data Detecting

DataEmbeddingDesign

3.2 Data Detecting Design

DataEmbeddingDesign

3.3 Data Embedding Pseudocode

Note: this is incomplete representation.

Function detect data {
	Set hexadecimal_word -> ‘the encrypted word’ \\ basically the identical process like in data 
						                                    \\ hiding process
	Loop through the file {
		Loop each line of the file {
			Identify the location of the timecode {
				Identify the data inside the cluster’s timecode {
					Read through the line ignoring first 6 characters // format
				}
				If there is at least 1 miss-match {
					Return error
				} else fully matched {
					Return success
				}
			}
		}
	}
}

4.0 Results

Description Explanation
Limited Number of Cluster's Timecodes Modifying more than two cluster’s timecodes cause slight video distortion; however, modifying even more timecodes causes both video and audio distortions.
Embedding Capacity Passed test of up to 2,500 characters. Assumption is that 2,500 characters should be more than enough for the user.
File Size Increment Original file: 2.1 MB (2,097,641 bytes) -> Modified File (2,500 characters): 2.1 MB (2,122,058 bytes). Increased by 23,417 bytes (1.00%).

5.0 Additional Information

For more information (like testing and background information), refer to the .PDF file attached to this repository: ./Maxim_Zaika_Data_Hiding_in_MKV_Container.pdf

6.0 Credits

It would not be possible to complete this project without MKV > XML > MKV parser created by Vitaly "_Vi" Shukela: https://github.com/vi/mkvparse.

Parser is rewritten for my own needs (for better understanding) and included in this repository to ensure that there is no mismatch with Vitaly's version. If you are interested in the parser, please, refer to his repository provided above. I do not take any credit for its creation.

Owner
Maxim Zaika
Maxim Zaika
Official Pytorch implementation of "Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes", CVPR 2022

Learning to Estimate Robust 3D Human Mesh from In-the-Wild Crowded Scenes / 3DCrowdNet News 💪 3DCrowdNet achieves the state-of-the-art accuracy on 3D

Hongsuk Choi 113 Dec 21, 2022
Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021]

Moiré Attack (MA): A New Potential Risk of Screen Photos [NeurIPS 2021] This repository is the official implementation of Moiré Attack (MA): A New Pot

Dantong Niu 22 Dec 24, 2022
Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System

Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System This repository contains code for the paper Schultheis,

2 Oct 28, 2022
Only valid pull requests will be allowed. Use python only and readme changes will not be accepted.

❌ This repo is excluded from hacktoberfest This repo is for python beginners and contains lot of beginner python projects for practice. You can also s

Prajjwal Pathak 50 Dec 28, 2022
Determined: Deep Learning Training Platform

Determined: Deep Learning Training Platform Determined is an open-source deep learning training platform that makes building models fast and easy. Det

Determined AI 2k Dec 31, 2022
The pure and clear PyTorch Distributed Training Framework.

The pure and clear PyTorch Distributed Training Framework. Introduction Requirements and Usage Dependency Dataset Basic Usage Slurm Cluster Usage Base

WILL LEE 208 Dec 20, 2022
"MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction" (CVPRW 2022) & (Winner of NTIRE 2022 Challenge on Spectral Reconstruction from RGB)

MST++: Multi-stage Spectral-wise Transformer for Efficient Spectral Reconstruction (CVPRW 2022) Yuanhao Cai, Jing Lin, Zudi Lin, Haoqian Wang, Yulun Z

Yuanhao Cai 274 Jan 05, 2023
Repository for the paper titled: "When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer"

When is BERT Multilingual? Isolating Crucial Ingredients for Cross-lingual Transfer This repository contains code for our paper titled "When is BERT M

Princeton Natural Language Processing 9 Dec 23, 2022
A template repository for submitting a job to the Slurm Cluster installed at the DISI - University of Bologna

Cluster di HPC con GPU per esperimenti di calcolo (draft version 1.0) Per poter utilizzare il cluster il primo passo è abilitare l'account istituziona

20 Dec 16, 2022
OpenDILab RL Kubernetes Custom Resource and Operator Lib

DI Orchestrator DI Orchestrator is designed to manage DI (Decision Intelligence) jobs using Kubernetes Custom Resource and Operator. Prerequisites A w

OpenDILab 205 Dec 29, 2022
A Graph Neural Network Tool for Recovering Dense Sub-graphs in Random Dense Graphs.

PYGON A Graph Neural Network Tool for Recovering Dense Sub-graphs in Random Dense Graphs. Installation This code requires to install and run the graph

Yoram Louzoun's Lab 0 Jun 25, 2021
Technical experimentations to beat the stock market using deep learning :chart_with_upwards_trend:

DeepStock Technical experimentations to beat the stock market using deep learning. Experimentations Deep Learning Stock Prediction with Daily News Hea

Keon 449 Dec 29, 2022
Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018)

CDAN Code release for "Conditional Adversarial Domain Adaptation" (NIPS 2018) New version: https://github.com/thuml/Transfer-Learning-Library Dataset

THUML @ Tsinghua University 363 Dec 20, 2022
A Marvelous ChatBot implement using PyTorch.

PyTorch Marvelous ChatBot [Update] it's 2019 now, previously model can not catch up state-of-art now. So we just move towards the future a transformer

JinTian 223 Oct 18, 2022
HyperaPy: An automatic hyperparameter optimization framework ⚡🚀

hyperpy HyperPy: An automatic hyperparameter optimization framework Description HyperPy: Library for automatic hyperparameter optimization. Build on t

Sergio Mora 7 Sep 06, 2022
Self-training with Weak Supervision (NAACL 2021)

This repo holds the code for our weak supervision framework, ASTRA, described in our NAACL 2021 paper: "Self-Training with Weak Supervision"

Microsoft 148 Nov 20, 2022
Pytorch reimplementation of PSM-Net: "Pyramid Stereo Matching Network"

This is a Pytorch Lightning version PSMNet which is based on JiaRenChang/PSMNet. use python main.py to start training. PSM-Net Pytorch reimplementatio

XIAOTIAN LIU 1 Nov 25, 2021
imbalanced-DL: Deep Imbalanced Learning in Python

imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc

NTUCSIE CLLab 19 Dec 28, 2022
QI-Q RoboMaster2022 CV Algorithm

QI-Q RoboMaster2022 CV Algorithm

2 Jan 10, 2022
Code for the paper "Graph Attention Tracking". (CVPR2021)

SiamGAT 1. Environment setup This code has been tested on Ubuntu 16.04, Python 3.5, Pytorch 1.2.0, CUDA 9.0. Please install related libraries before r

122 Dec 24, 2022