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
Some methods for comparing network representations in deep learning and neuroscience.

Generalized Shape Metrics on Neural Representations In neuroscience and in deep learning, quantifying the (dis)similarity of neural representations ac

Alex Williams 45 Dec 27, 2022
Code for Talk-to-Edit (ICCV2021). Paper: Talk-to-Edit: Fine-Grained Facial Editing via Dialog.

Talk-to-Edit (ICCV2021) This repository contains the implementation of the following paper: Talk-to-Edit: Fine-Grained Facial Editing via Dialog Yumin

Yuming Jiang 221 Jan 07, 2023
GluonMM is a library of transformer models for computer vision and multi-modality research

GluonMM is a library of transformer models for computer vision and multi-modality research. It contains reference implementations of widely adopted baseline models and also research work from Amazon

42 Dec 02, 2022
Aerial Imagery dataset for fire detection: classification and segmentation (Unmanned Aerial Vehicle (UAV))

Aerial Imagery dataset for fire detection: classification and segmentation using Unmanned Aerial Vehicle (UAV) Title FLAME (Fire Luminosity Airborne-b

79 Jan 06, 2023
A Tensorfflow implementation of Attend, Infer, Repeat

Attend, Infer, Repeat: Fast Scene Understanding with Generative Models This is an unofficial Tensorflow implementation of Attend, Infear, Repeat (AIR)

Adam Kosiorek 82 May 27, 2022
The project was to detect traffic signs, based on the Megengine framework.

trafficsign 赛题 旷视AI智慧交通开源赛道,初赛1/177,复赛1/12。 本赛题为复杂场景的交通标志检测,对五种交通标志进行识别。 框架 megengine 算法方案 网络框架 atss + resnext101_32x8d 训练阶段 图片尺寸 最终提交版本输入图片尺寸为(1500,2

20 Dec 02, 2022
Контрольная работа по математическим методам машинного обучения

ML-MathMethods-Test Контрольная работа по математическим методам машинного обучения. Вычисление основных статистик, диаграмм и графиков, проверка разл

Stas Ivanovskii 1 Jan 06, 2022
this is a lite easy to use virtual keyboard project for anyone to use

virtual_Keyboard this is a lite easy to use virtual keyboard project for anyone to use motivation I made this for this year's recruitment for RobEn AA

Mohamed Emad 3 Oct 23, 2021
Implementation of "Bidirectional Projection Network for Cross Dimension Scene Understanding" CVPR 2021 (Oral)

Bidirectional Projection Network for Cross Dimension Scene Understanding CVPR 2021 (Oral) [ Project Webpage ] [ arXiv ] [ Video ] Existing segmentatio

Hu Wenbo 135 Dec 26, 2022
Spatial Sparse Convolution Library

SpConv: Spatially Sparse Convolution Library PyPI Install Downloads CPU (Linux Only) pip install spconv CUDA 10.2 pip install spconv-cu102 CUDA 11.1 p

Yan Yan 1.2k Jan 07, 2023
Labels4Free: Unsupervised Segmentation using StyleGAN

Labels4Free: Unsupervised Segmentation using StyleGAN ICCV 2021 Figure: Some segmentation masks predicted by Labels4Free Framework on real and synthet

70 Dec 23, 2022
StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation

StyleSpace Analysis: Disentangled Controls for StyleGAN Image Generation Demo video: CVPR 2021 Oral: Single Channel Manipulation: Localized or attribu

Zongze Wu 267 Dec 30, 2022
A python package to perform same transformation to coco-annotation as performed on the image.

coco-transform-util A python package to perform same transformation to coco-annotation as performed on the image. Installation Way 1 $ git clone https

1 Jan 14, 2022
Implementation of Wasserstein adversarial attacks.

Stronger and Faster Wasserstein Adversarial Attacks Code for Stronger and Faster Wasserstein Adversarial Attacks, appeared in ICML 2020. This reposito

21 Oct 06, 2022
This repository contains the entire code for our work "Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding"

Two-Timescale-DNN Two-Timescale End-to-End Learning for Channel Acquisition and Hybrid Precoding This repository contains the entire code for our work

QiyuHu 3 Mar 07, 2022
PyTorch code for ICLR 2021 paper Unbiased Teacher for Semi-Supervised Object Detection

Unbiased Teacher for Semi-Supervised Object Detection This is the PyTorch implementation of our paper: Unbiased Teacher for Semi-Supervised Object Detection

Facebook Research 366 Dec 28, 2022
Implements Stacked-RNN in numpy and torch with manual forward and backward functions

Recurrent Neural Networks Implements simple recurrent network and a stacked recurrent network in numpy and torch respectively. Both flavours implement

Vishal R 1 Nov 16, 2021
Unofficial PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution

PyTorch reimplementation of the paper Swin Transformer V2: Scaling Up Capacity and Resolution [arXiv 2021].

Christoph Reich 122 Dec 12, 2022
Detection of PCBA defect

Detection_of_PCBA_defect Detection_of_PCBA_defect Use yolov5 to train. $pip install -r requirements.txt Detect.py will detect file(jpg,mp4...) in cu

6 Nov 28, 2022
An Implementation of Fully Convolutional Networks in Tensorflow.

Update An example on how to integrate this code into your own semantic segmentation pipeline can be found in my KittiSeg project repository. tensorflo

Marvin Teichmann 1.1k Dec 12, 2022