all.py

import io
import requests
from PIL import Image

# own imports
from src.model import load_model, preprocess, predict, inverse_preprocess
from src.fgsm import iterative_fast_gradient_sign_
from src.viz import plot

# ------------------------------------------------------------------------------
# 1. PARAMETERS
# load image from a given url
url = 'https://github.com/Paulescu/adversarial-machine-learning/blob/main/images/dog.jpg?raw=true'

# FGSM parameters
epsilon = 0.09
n_steps = 9
alpha = 0.025
# ------------------------------------------------------------------------------

# ------------------------------------------------------------------------------
# 2. PROCESSING
# load image from the given url
response = requests.get(url)
img = Image.open(io.BytesIO(response.content))

# load model. This is a time-consuming operation
model = load_model()

# make sure the image from URL is clean, and the model correctly classifies it.
# preprocess original image
x_original = inverse_preprocess(preprocess(img))
# and make sure the model predicts the correct label
x_predictions = predict(model, x_original)
print(x_predictions)

# returns a pair (x_adv, grad) after each FGSM step.
iterator = iterative_fast_gradient_sign_(
    model,
    preprocess(img),
    epsilon,
    n_steps=n_steps,
    alpha=alpha
)
# ------------------------------------------------------------------------------

# ------------------------------------------------------------------------------
# 3. PLOT RESULTS
for x_adv, grad in iterator:

    # get model predictions
    x_adv_predictions = predict(model, x_adv)
    
    plot(x_original, x_adv, grad, epsilon,
         x_label=x_predictions['label'],
         x_prob=x_predictions['confidence'],
         x_adv_label=x_adv_predictions['label'],
         x_adv_prob=x_adv_predictions['confidence'])
    
    # starting image new iteration
    x_original = x_adv
    x_predictions = predict(model, x_original)
# ------------------------------------------------------------------------------