关于对于旋转攻击鲁棒性的疑问

from blind_watermark import WaterMark

bwm1 = WaterMark(password_img=1, password_wm=1)
bwm1.read_img('cat.png')
bwm1.read_wm([True, False, True, True, True, False], mode='bit')
bwm1.embed('cat-w.png')
len_wm = len(bwm1.wm_bit)
print('Put down the length of wm_bit {len_wm}'.format(len_wm=len_wm))

import blind_watermark
from blind_watermark import WaterMark
import PIL
import utils_img_k
import torchvision
import matplotlib.pyplot as plt
import torch.nn.functional as F
import torch

blind_watermark.bw_notes.close()
device = 'cuda' if torch.cuda.is_available() else 'cpu'

attacks = {
    'none': lambda x: x,
    # 'crop_01': lambda x: utils_img_k.center_crop(x, 0.1),
    # 'crop_09': lambda x: utils_img_k.center_crop(x, 0.9),
    # 'resize_03': lambda x: utils_img_k.resize(x, 0.3),
    # 'resize_05': lambda x: utils_img_k.resize(x, 0.5),
    'rot_45': lambda x: utils_img_k.rotate(x, 45),
    # 'rot_180': lambda x: utils_img_k.rotate(x, 180),
    # 'blur': lambda x: utils_img_k.gaussian_blur(x, sigma=4.0, kernel_size=3),
    # 'brightness_3': lambda x: utils_img_k.adjust_brightness(x, 3),
    # 'jpeg_30': lambda x: utils_img_k.jpeg_compress(x, 30),
    # 'gaussian_noise': lambda x: utils_img_k.gaussian_noise(x, std=0.1),
}

for name, attack in attacks.items():
    print(name)
    # distortion
    file_path = f"cat-w.png"
    image_w = PIL.Image.open(file_path).convert('RGB')
    to_tensor = torchvision.transforms.ToTensor()
    image_w = to_tensor(image_w).unsqueeze(0).to(torch.float32).to(device)
    image_w_distortion = attack(image_w)
    if image_w_distortion.shape != image_w.shape:
        image_w_distortion = F.interpolate(image_w_distortion, size=512, mode='bilinear')

    to_pil = torchvision.transforms.ToPILImage()
    pil_image = to_pil(image_w_distortion.squeeze(0))

    # 显示图片
    plt.imshow(pil_image)
    plt.axis("off")  # 关闭坐标轴
    plt.show()

    temp_path = f"cat-d.png"
    pil_image.save(temp_path)

    bwm1 = WaterMark(password_img=1, password_wm=1)
    wm_extract = bwm1.extract('cat-d.png', mode='bit',wm_shape=6)
    print(wm_extract)

none
[ True False  True  True  True False]
crop_01
[ True  True  True False False False]
crop_09
[ True  True False False False  True]
resize_03
[ True False  True  True  True False]
resize_05
[ True False  True  True  True False]
rot_45
[ True False False False  True  True]
rot_180
[ True  True  True False False  True]
blur
[ True False  True  True  True False]
brightness_3
[False  True False False False  True]
jpeg_30
[ True False  True  True  True False]
gaussian_noise
[ True False  True  True  True False]