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
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
Deep Learning Visuals contains 215 unique images divided in 23 categories

Deep Learning Visuals contains 215 unique images divided in 23 categories (some images may appear in more than one category). All the images were originally published in my book "Deep Learning with P

Daniel Voigt Godoy 1.3k Dec 28, 2022
Prototype-based Incremental Few-Shot Semantic Segmentation

Prototype-based Incremental Few-Shot Semantic Segmentation Fabio Cermelli, Massimiliano Mancini, Yongqin Xian, Zeynep Akata, Barbara Caputo -- BMVC 20

Fabio Cermelli 21 Dec 29, 2022
Jittor Medical Segmentation Lib -- The assignment of Pattern Recognition course (2021 Spring) in Tsinghua University

THU模式识别2021春 -- Jittor 医学图像分割 模型列表 本仓库收录了课程作业中同学们采用jittor框架实现的如下模型: UNet SegNet DeepLab V2 DANet EANet HarDNet及其改动HarDNet_alter PSPNet OCNet OCRNet DL

48 Dec 26, 2022
This repository contains the map content ontology used in narrative cartography

Narrative-cartography-ontology This repository contains the map content ontology used in narrative cartography, which is associated with a submission

Weiming Huang 0 Oct 31, 2021
The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color

The World of an Octopus: How Reporting Bias Influences a Language Model's Perception of Color Overview Code and dataset for The World of an Octopus: H

1 Nov 13, 2021
tensorflow implementation of 'YOLO : Real-Time Object Detection'

YOLO_tensorflow (Version 0.3, Last updated :2017.02.21) 1.Introduction This is tensorflow implementation of the YOLO:Real-Time Object Detection It can

Jinyoung Choi 1.7k Nov 21, 2022
This repository lets you interact with Lean through a REPL.

lean-gym This repository lets you interact with Lean through a REPL. See Formal Mathematics Statement Curriculum Learning for a presentation of lean-g

OpenAI 87 Dec 28, 2022
This repository contains the source code for the paper "DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks",

DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks Project Page | Video | Presentation | Paper | Data L

Facebook Research 281 Dec 22, 2022
The second project in Python course on FCC

Assignment Write a function named add_time that takes in two required parameters and one optional parameter: a start time in the 12-hour clock format

Denise T 1 Dec 13, 2021
Release of SPLASH: Dataset for semantic parse correction with natural language feedback in the context of text-to-SQL parsing

SPLASH: Semantic Parsing with Language Assistance from Humans SPLASH is dataset for the task of semantic parse correction with natural language feedba

Microsoft Research - Language and Information Technologies (MSR LIT) 35 Oct 31, 2022
This tutorial repository is to introduce the functionality of KGTK to first-time users

Welcome to the KGTK notebook tutorial The goal of this tutorial repository is to introduce the functionality of KGTK to first-time users. The Knowledg

USC ISI I2 58 Dec 21, 2022
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement

Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement In this project, we proposed a Domain Disentanglement Faster-RCNN (DDF)

19 Nov 24, 2022
[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore

[AI6101] Introduction to AI & AI Ethics is a core course of MSAI, SCSE, NTU, Singapore. The repository corresponds to the AI6101 of Semester 1, AY2021-2022, starting from 08/2021. The instructors of

AccSrd 1 Sep 22, 2022
A curated list of awesome Machine Learning frameworks, libraries and software.

Awesome Machine Learning A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php. If you

Joseph Misiti 57.1k Jan 03, 2023
This is the repository for The Machine Learning Workshops, published by AI DOJO

This is the repository for The Machine Learning Workshops, published by AI DOJO. It contains all the workshop's code with supporting project files necessary to work through the code.

AI Dojo 12 May 06, 2022
EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

EquiBind: geometric deep learning for fast predictions of the 3D structure in which a small molecule binds to a protein

Hannes Stärk 355 Jan 03, 2023
Implement object segmentation on images using HOG algorithm proposed in CVPR 2005

HOG Algorithm Implementation Description HOG (Histograms of Oriented Gradients) Algorithm is an algorithm aiming to realize object segmentation (edge

Leo Hsieh 2 Mar 12, 2022
Shōgun

The SHOGUN machine learning toolbox Unified and efficient Machine Learning since 1999. Latest release: Cite Shogun: Develop branch build status: Donat

Shōgun ML 2.9k Jan 04, 2023
Adversarial Attacks on Probabilistic Autoregressive Forecasting Models.

Attack-Probabilistic-Models This is the source code for Adversarial Attacks on Probabilistic Autoregressive Forecasting Models. This repository contai

SRI Lab, ETH Zurich 25 Sep 14, 2022