BusterNet implementation for TPU Training In Colab

Version: 0.0.2  
Python : 3.6.8
Researchers : Md. Nazmuddoha Ansary **
              Shakir Hossain
              Mohammad Bin Monjil  
              Habibur Rahman  
              Shahriar Prince  
              Md Aminul Islam  

Version and Requirements

Keras==2.2.5  
numpy==1.16.4  
opencv-python==4.1.1.26  
tensorflow==1.13.1 
termcolor==1.1.0  
Pillow==6.1.0
imageio==2.5.0

• Python == 3.6.8
• pip3 install -r requirements.txt

Preprocessing The Data

1. Download MICC-F2000 dataset

2. Unzip MICC-F2000.zip
   NOTE:The dataset contains a file named: nikon7_scale.jpg. It has to be renamed as nikon_7_scale.jpg.

3. Run preprocess.py in scripts folder

    usage: ./preprocess.py [-h] data_dir save_dir    
    MICC_F2000 Dataset preprocessing    
    positional arguments:    
        data_dir    /path/to/MICC-F2000 Folder    
        save_dir    /path/to/save/preprocessed/data    
    optional arguments:    
        -h, --help  show this help message and exit         
   

• The total number of tampered images in the dataset is 700. BUT for processing convenience images with identifiers 'P1000231' and 'DSCN47' are avoided for generalization of template matching procedure. The dataset is preprocessed such that there are 3 types of ground truths.For example: This to train by the three stage strategy BusterNet.Sample Ground Truths for manipulation, similiarity and fusion branch respectively:
  

BusterNet

The model implementation is based on BusterNet: Detecting Copy-Move Image Forgery with Source/Target Localization

Authors and Researchers: Yue Wu,Wael Abd-Almageed,Prem Natarajan

Colab and TPU(Tensor Processing Unit)

TPU’s have been recently added to the Google Colab portfolio making it even more attractive for quick-and-dirty machine learning projects when your own local processing units are just not fast enough. While the Tesla K80 available in Google Colab delivers respectable 1.87 TFlops and has 12GB RAM, the TPUv2 available from within Google Colab comes with a whopping 180 TFlops, give or take. It also comes with 64 GB High Bandwidth Memory (HBM). Visit This For More Info
For this model the approx time/epoch=11s

Manipulation Region Detection (man-net)

A sample result from the manipulation detection branch:

Similiar Region Detection (sim-net)

A sample result from the similiarity detection branch:

Localization of source and forgery (fusion-net)

Note: Green indicates source region and Red indicates forged Regions

Publications:

A research paper relating to the work is currently being communicated by the researchers. Once published, the publication link will be added here