给我一段伪造cookie的Java代码

String cookieString = "CookieName=CookieValue; Domain=example.com; Path=/; Expires=Sun, 06 Nov 2022 08:49:37 GMT";Cookie cookie = new Cookie("CookieName","CookieValue");
cookie.setDomain("example.com");
cookie.setPath("/");
cookie.setMaxAge(2147483647);String cookieString = cookie.toString();

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给我一段基于ObjectFormer进行传统伪造检测的代码

以下是基于ObjectFormer进行传统伪造检测的代码：

import cv2
import numpy as np
import tensorflow as tf
from object_detection.builders import model_builder
from object_detection.utils import visualization_utils as viz_utils

# Load the saved model
PATH_TO_SAVED_MODEL = "path/to/saved/model"
detect_fn = tf.saved_model.load(PATH_TO_SAVED_MODEL)

# Load an image
image_path = "path/to/image"
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

# Detect objects in the image using the ObjectFormer model
input_tensor = tf.convert_to_tensor(image)
input_tensor = input_tensor[tf.newaxis, ...]
detections = detect_fn(input_tensor)

# Extract the bounding boxes and class labels
boxes = detections['detection_boxes'][0].numpy()
scores = detections['detection_scores'][0].numpy()
classes = detections['detection_classes'][0].numpy().astype(np.uint32)

# Visualize the detections
viz_utils.visualize_boxes_and_labels_on_image_array(
    image,
    boxes,
    classes,
    scores,
    category_index,
    use_normalized_coordinates=True,
    max_boxes_to_draw=200,
    min_score_thresh=.30,
    agnostic_mode=False)

# Display the image
cv2.imshow('Object Detection', cv2.cvtColor(image, cv2.COLOR_RGB2BGR))
cv2.waitKey(0)
cv2.destroyAllWindows()

这段代码可以加载已保存的ObjectFormer模型，并使用它来检测输入图像中的对象。代码会提取检测到的边界框、分数和类标签，并使用可视化工具将它们绘制在原始图像上。你可以通过调整参数来更改检测结果的质量和可视化效果。

生成一段python基于objectformer进行传统伪造检测的代码

以下是一段基于 ObjectFormer 进行传统伪造检测的 Python 代码：

import torch
import torch.nn as nn
import torch.nn.functional as F
from transformers import ObjectFormerModel, ObjectFormerTokenizer

# 加载 ObjectFormer 模型和分词器
model = ObjectFormerModel.from_pretrained('patrickvonplaten/object-former-pegasus-finetuned-imdb')
tokenizer = ObjectFormerTokenizer.from_pretrained('patrickvonplaten/object-former-pegasus-finetuned-imdb')

# 自定义传统伪造检测网络
class FakeDetectionNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 64, 3, padding=1)
        self.conv2 = nn.Conv2d(64, 128, 3, padding=1)
        self.conv3 = nn.Conv2d(128, 256, 3, padding=1)
        self.fc1 = nn.Linear(256 * 4 * 4, 512)
        self.fc2 = nn.Linear(512, 64)
        self.fc3 = nn.Linear(64, 2)

    def forward(self, x):
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv2(x)), (2, 2))
        x = F.max_pool2d(F.relu(self.conv3(x)), (2, 2))
        x = x.view(-1, 256 * 4 * 4)
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return F.softmax(x, dim=1)

# 加载传统伪造检测网络
fake_detection_net = FakeDetectionNet()
fake_detection_net.load_state_dict(torch.load('fake_detection_net.pth'))

# 输入一段文本并进行预处理
text = "这是一段可疑的文本"
inputs = tokenizer(text, return_tensors='pt')
outputs = model(**inputs)

# 获取 ObjectFormer 提取的对象特征
object_features = outputs.last_hidden_state[:, 0, :]

# 将对象特征变形成图片格式
object_images = object_features.view(-1, 1, 4, 4)

# 使用传统伪造检测网络进行检测
with torch.no_grad():
    pred = fake_detection_net(object_images)
    if pred.argmax() == 0:
        print("这是真实的对象特征")
    else:
        print("这是伪造的对象特征")

以上代码中，我们首先加载了 ObjectFormer 模型和分词器，并定义了一个自定义的传统伪造检测网络。接着，我们输入一段文本并使用 ObjectFormer 模型提取出其中的对象特征，将对象特征变形成图片格式，并使用传统伪造检测网络进行检测。最后根据检测结果输出相应的信息。