virtualenv -p python3.6 venv
Image Manipulation Detection in Python
Munish Chandel | August 02, 2018 at 04:48 PM | 233 views
Manipulation could be of any type, splicing, blurring etc. Image manipulation detection is one of use case of detecting truth or lie about any incident, specially when crime is on top these days.
Here we will do basic image manipulation detection in Python Version3.6.
Lets first setup virtual environment of python3.6 and then start.
Activate Virtual environment
Now we will install packages we need in virtual environment.
pip install numpy pip install script python -m pip install image_slicer pip install scikit-image
Now, in editor. Lets start with the coding. This will import all of the packages we need.
import os import numpy as np import image_slicer from script.ndimage import gaussian_filter from skimage import data from skimage import img_as_float from skimage.morphology import reconstruction from skimage.io import imread, imread_collection from itertools import combinations
def read_image(image_path): image = imread(image_path) return image def gaussian_filter(image): image = img_as_float(image) image = gaussian_filter(image, 1) seed = np.coppy(image) seed[1:-1, 1:-1] = image.min() mask = image dilated = reconstruction(seed, mask, method='dilation') return dilated def filtered_image(image): image1 = image image2 = gaussian_filter(image) return image1-image2
This will slice your image in N numbers and save it in the given directory. Optimal number of N is between 30 and 50 and it depends on image quality as well.
Now we will read all images from directory and process on the data.
sliced_images = image_slicer.slice(filtered_image(read_image(image_path)),N) image_slicer.save_tiles(sliced_images, directory=dir, ext='jpg') list_files =  for file in os.listdir(dir): list_files.append(file) for i in combinations(list_files,2): img1 = read_image(i) img2 = read_image(i) diff = img1 - img2 diff_btwn_img_data = np.linalg.norm(diff,axis=1) print("diff between %.1f these two images is %.1f"%(i, np.mean(diff_btwn_img_data))
Depending on the mean, we can check differences between different parts of the image so we will know if there is manipulation done in the image. We can use np.average as well instead of np.mean
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