forensic - GitLite

KO | EN

gitlite — search

#react #ai #llm #golang #android #linux #macos #java #kubernetes #vue #security #pytorch #ios #openai

forensic

(★ 145)

Copy-move image forgery detection library.

파일 탐색기

최종 버전 다운로드 (.zip)

- - build.yml
- FUNDING.yml
- modules.txt
.gitignore
build.sh
go.mod
go.sum
LICENSE
main.go
Makefile
README.md
stackblur.go
utils.go

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/esimov/forensic

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd forensic

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Go

보통 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Go Go 언어 컴파일러 및 도구 모음입니다.

go build ./...

Go 프로그램을 컴파일해 실행 파일을 만듭니다.

go run .

따로 빌드하지 않고 바로 Go 프로그램을 실행합니다.

✅ 빌드가 에러 없이 끝나면 성공입니다. go run . 을 썼다면 터미널 출력을 확인하세요.

3. Make

보통

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Make Linux/macOS는 보통 기본 설치되어 있습니다. Windows는 별도 설치(예: MSYS2, WSL)가 필요합니다.

make

생성된 빌드 설정을 바탕으로 실제 컴파일을 진행해 실행 파일을 만듭니다.

✅ 에러 없이 끝나면 성공입니다. 생성된 실행 파일을 직접 실행해보세요.

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# Forensic

[![build](https://github.com/esimov/forensic/actions/workflows/build.yml/badge.svg)](https://github.com/esimov/forensic/actions/workflows/build.yml)
[![license](https://img.shields.io/github/license/esimov/forensic)](https://raw.githubusercontent.com/esimov/forensic/master/LICENSE)

Forensic is an image processing library which aims to detect copy-move forgeries in digital images. The implementation is mainly based on this paper: https://arxiv.org/pdf/1308.5661.pdf

### Implementation details

* Convert the `RGB` image to `YUV` color space.
* Divide the `R`,`G`,`B`,`Y` components into fixed-sized blocks.
* Obtain each block `R`,`G`,`B` and `Y` components.
* Calculate each block `R`,`G`,`B` and `Y` components `DCT` (Discrete Cosine Transform) coefficients.
* Extract features from the obtained `DCT` coefficients and save it into a matrix. The matrix rows will contain the blocks top-left coordinate position plus the DCT coefficient. The matrix will have `(M − b + 1)(N − b + 1)x9` elements.
* Sort the features in lexicographic order.
* Search for similar pairs of blocks. Because identical blocks are most probably neighbors, after ordering them in lexicographic order we need to apply a specific threshold to filter out the false positive detections. If the distance between two neighboring blocks is smaller than a predefined threshold the blocks are considered as a pair of candidate for the forgery.
* For each pair of candidate compute the cumulative number of shift vectors (how many times the same block is detected). If that number is greater than a predefined threshold the corresponding regions are considered forged.

## Install
First install Go if you don't have already installed, set your `GOPATH`, and make sure `$GOPATH/bin` is in your `PATH` environment variable.

```bash
$ export GOPATH="$HOME/go"
$ export PATH="$PATH:$GOPATH/bin"
```
Next download the project and build the binary file.

```bash
$ go get -u -f github.com/esimov/forensic
$ go install
```

In case you do not want to build the binary file yourself you can obtain the prebuilt one from the [releases](https://github.com/esimov/forensic/releases) folder.

## Usage

```bash
$ forensic -in input.jpg -out output.jpg
```

### Supported commands:
```bash 
$ forensic --help

Image forgery detection library.
    Version:

-blur int
    	Blur radius (default 1)
  -bs int
    	Block size (default 4)
  -dt float
    	Distance threshold (default 0.4)
  -ft float
    	Forgery threshold (default 210)
  -in string
    	Input image
  -ot int
    	Offset threshold (default 72)
  -out string
    	Output image
```

## Results
| Original image | Forged image | Detection result |
| --- | --- | --- |
| ![dogs_original](https://user-images.githubusercontent.com/883386/39047347-3fee70cc-44a2-11e8-8729-c4312c631017.jpg) | ![dogs_forged](https://user-images.githubusercontent.com/883386/39047218-c1c8c530-44a1-11e8-8eb6-f9a8470848bd.jpg) | ![dogs_result](https://user-images.githubusercontent.com/883386/39047481-aec6f0f0-44a2-11e8-9f0f-041b9f2a0eb4.png) |

### Notice
Sometimes the library produces false positive results depending on the image content. For this reason I advise to adjust the settings. Also in some cases human judgement is required, but otherwise the library do a decent job in detecting forged images.

### How to interpret the results?
The more intensive the overlayed color is, the more certain is that the image is tampered.

## Author

* Endre Simo ([@simo_endre](https://twitter.com/simo_endre))

## License

Copyright © 2018 Endre Simo

This project is under the MIT License. See the LICENSE file for the full license text.