Neural Style Transfer

This an demonstration of how neural style transfer can be implemented with the help of Fast arbitrary file transfer from TensorFlow .

It can be used in document & forgery analysis (Forensic domain) in the analysis of forged documents, counterfeit currency, or altered images. By applying the style of genuine or known samples, the technique can help identify inconsistencies, detect tampering, or reveal hidden alterations.

Attaching my code to this article for educational purposes.

import tensorflow as tf
import tensorflow_hub as hub
import matplotlib.pyplot as plt
import numpy as np
import PIL.Image


def tensor_to_image(tensor):
    tensor = tensor * 255
    tensor = np.array(tensor, dtype=np.uint8)
    if np.ndim(tensor) > 3:
        assert tensor.shape[0] == 1
        tensor = tensor[0]
    return PIL.Image.fromarray(tensor)


def load_image(image_path, image_size=(256, 256)):
    image = tf.io.read_file(image_path)
    image = tf.image.decode_image(image, channels=3)
    image = tf.image.convert_image_dtype(image, tf.float32)
    image = tf.image.resize(image, image_size)
    image = image[tf.newaxis, :]
    return image


def stylize_image(content_image, style_image):
    hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
    stylized_image = hub_module(tf.constant(content_image), tf.constant(style_image))[0]
    return stylized_image


content_path = '/content/IMG_20230408_163146.jpg'
style_path = '/content/abstract-art.jpeg'


content_image = load_image(content_path)
style_image = load_image(style_path)


stylized_image = stylize_image(content_image, style_image)
stylized_image = tensor_to_image(stylized_image)


plt.subplot(1, 3, 1)
plt.imshow(load_image(content_path)[0])
plt.title('Content Image')


plt.subplot(1, 3, 2)
plt.imshow(load_image(style_path)[0])
plt.title('Style Image')


plt.subplot(1, 3, 3)
plt.imshow(stylized_image)
plt.title('Stylized Image')


plt.show()