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
Semantic Image Synthesis with SPADE

Semantic Image Synthesis with SPADE New implementation available at imaginaire repository We have a reimplementation of the SPADE method that is more

NVIDIA Research Projects 7.3k Jan 07, 2023
AlphaNet Improved Training of Supernet with Alpha-Divergence

AlphaNet: Improved Training of Supernet with Alpha-Divergence This repository contains our PyTorch training code, evaluation code and pretrained model

Facebook Research 87 Oct 10, 2022
Pytorch implementation of "Geometrically Adaptive Dictionary Attack on Face Recognition" (WACV 2022)

Geometrically Adaptive Dictionary Attack on Face Recognition This is the Pytorch code of our paper "Geometrically Adaptive Dictionary Attack on Face R

6 Nov 21, 2022
This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationships.

Auto-Lambda This repository contains the source code of Auto-Lambda and baselines from the paper, Auto-Lambda: Disentangling Dynamic Task Relationship

Shikun Liu 76 Dec 20, 2022
Repo for CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

CReST in Tensorflow 2 Code for the paper: "CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning" by Chen Wei, Ki

Google Research 75 Nov 01, 2022
From a body shape, infer the anatomic skeleton.

OSSO: Obtaining Skeletal Shape from Outside (CVPR 2022) This repository contains the official implementation of the skeleton inference from: OSSO: Obt

Marilyn Keller 166 Dec 28, 2022
PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks

PyDEns PyDEns is a framework for solving Ordinary and Partial Differential Equations (ODEs & PDEs) using neural networks. With PyDEns one can solve PD

Data Analysis Center 220 Dec 26, 2022
Set of models for classifcation of 3D volumes

Classification models 3D Zoo - Keras and TF.Keras This repository contains 3D variants of popular CNN models for classification like ResNets, DenseNet

69 Dec 28, 2022
A repository for interferometer controller code.

dses-interferometer-controller A repository for interferometer controller code, hardware, and simulations. See dses.science for more information on th

Eli Reed 1 Jan 17, 2022
PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation.

ALiBi PyTorch implementation of Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation. Quickstart Clone this reposit

Jake Tae 4 Jul 27, 2022
Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

FPT_data_centric_competition - Team nan solution repository for FPT data-centric competition. Data augmentation, Albumentation, Mosaic, Visualization, KNN application

Pham Viet Hoang (Harry) 2 Oct 30, 2022
Code for EMNLP 2021 paper: "Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training"

SCAPT-ABSA Code for EMNLP2021 paper: "Learning Implicit Sentiment in Aspect-based Sentiment Analysis with Supervised Contrastive Pre-Training" Overvie

Zhengyan Li 66 Dec 04, 2022
NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

NVTabular is a feature engineering and preprocessing library for tabular data designed to quickly and easily manipulate terabyte scale datasets used to train deep learning based recommender systems.

880 Jan 07, 2023
Node Editor Plug for Blender

NodeEditor Blender的程序化建模插件 Show Current 基本框架:自定义的tree-node-socket、tree中的node与socket采用字典查询、基于socket入度的拓扑排序 数据传递和处理依靠Tree中的字典,socket传递字典key TODO 增加更多的节点

Cuimi 11 Dec 03, 2022
Code for Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights

Piggyback: https://arxiv.org/abs/1801.06519 Pretrained masks and backbones are available here: https://uofi.box.com/s/c5kixsvtrghu9yj51yb1oe853ltdfz4q

Arun Mallya 165 Nov 22, 2022
catch-22: CAnonical Time-series CHaracteristics

catch22 - CAnonical Time-series CHaracteristics About catch22 is a collection of 22 time-series features coded in C that can be run from Python, R, Ma

Carl H Lubba 229 Oct 21, 2022
ICON: Implicit Clothed humans Obtained from Normals

ICON: Implicit Clothed humans Obtained from Normals arXiv, December 2021. Yuliang Xiu · Jinlong Yang · Dimitrios Tzionas · Michael J. Black Table of C

Yuliang Xiu 1.1k Dec 30, 2022
Bayesian optimisation library developped by Huawei Noah's Ark Library

Bayesian Optimisation Research This directory contains official implementations for Bayesian optimisation works developped by Huawei R&D, Noah's Ark L

HUAWEI Noah's Ark Lab 395 Dec 30, 2022
QueryDet: Cascaded Sparse Query for Accelerating High-Resolution SmallObject Detection

QueryDet-PyTorch This repository is the official implementation of our paper: QueryDet: Cascaded Sparse Query for Accelerating High-Resolution Small O

Chenhongyi Yang 276 Dec 31, 2022
Official implement of "CAT: Cross Attention in Vision Transformer".

CAT: Cross Attention in Vision Transformer This is official implement of "CAT: Cross Attention in Vision Transformer". Abstract Since Transformer has

100 Dec 15, 2022