Adversarial Exampleで作る"エロ画像" ソースコード（ライセンスはMIT-0です）

adversarial.py

import copy
import i2v
import numpy as np
from PIL import Image
from skimage.transform import resize

image_size = 224
channel_size = 3
noise_size = 56

illust2vec = i2v.make_i2v_with_onnx(
    "illust2vec_tag_ver200.onnx", "tag_list.json")
img_org = Image.open("/home/kivantium/initial.jpg")
img_org_resized = img_org.resize((image_size, image_size))
img_org_resized_np = np.array(img_org_resized, np.float32) / 255.0


def order_rating(rating):
    for r in rating:
        if r[0] == 'safe':
            safe = r[1]
        elif r[0] == 'questionable':
            questionable = r[1]
        elif r[0] == 'explicit':
            explicit = r[1]
    return safe, questionable, explicit


# image: numpy array (float)
def target_function(image_np):
    c = 1.0
    k = 20
    image_pil = Image.fromarray(np.uint8(image_np * 255))
    rating = illust2vec.estimate_plausible_tags([image_pil])[0]['rating']
    safe, questionable, explicit = order_rating(rating)
    f = max(np.log(safe + questionable) - np.log(explicit), -k)
    return np.linalg.norm(image_np - img_org_resized_np) + c * f


# image: numpy array
# coordinate: 3-membered tuple
def grad_and_hessian(image, coordinate):
    x, y, c = coordinate
    h = 0.01
    f = target_function(image)
    perturbation = np.zeros((noise_size, noise_size, channel_size))
    perturbation[y][x][c] = h
    perturbation_resized = resize(perturbation, (image_size, image_size))
    f1 = target_function(image + perturbation_resized)
    f2 = target_function(image - perturbation_resized)
    grad = (f1 - f2) / (2 * h)
    hessian = (f1 - 2 * f + f2) / (h ** 2)
    return grad, hessian


image = copy.deepcopy(img_org_resized_np)
eta = 0.001
it = 0
while True:
    x, y = np.random.randint(noise_size, size=2)
    c = np.random.randint(channel_size)
    grad, hessian = grad_and_hessian(image, (x, y, c))
    noise = np.zeros((noise_size, noise_size, channel_size))
    if hessian <= 0.0:
        noise[y, x, c] = -eta * grad
    else:
        noise[y, x, c] = -eta * grad / hessian
    noise_resized = resize(noise, (image_size, image_size))
    image += noise_resized
    image_pil = Image.fromarray(np.uint8(image * 255))
    rating = illust2vec.estimate_plausible_tags([image_pil])[0]['rating']
    print("rating:", rating, "func value:", target_function(image))
    if it % 100 == 0:
        image_pil.save(f"adversarial{it}.jpg")
    it += 1