[GHCTF 2025]Disappeared Logo WP

Problem: [GHCTF 2025]Disappeared Logo

思路

• 这是原论文
  https://www.anquanke.com/post/id/259060#h3-4
• 这是我出题的wp
  https://mp.weixin.qq.com/s/H3l54bICAkvLTFNuPTinKg
  

总结

• 对该题的考点总结

1. 时间戳//10000后代表当天
2. 根据参数shape来恢复模型结构
3. 考察选手电脑性能，为了改模型而改模型，见到了 \(262144 *64\) 的全连接层，华硕轻薄本已经死了
4. 考察选手对于出过已有题目的认知，我已经见过不少于7个版本的这篇论文了，不愧是CTF的好论文
5. 信春哥，得永生

EXP

• 具体攻击代码

from PIL import Image
import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import transforms
import time
import pickle

class Lemodel(nn.Module):
    def __init__(self):
        super(Lemodel, self).__init__()
        act = nn.Sigmoid
        self.body = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1, stride=1),
            act(),
            nn.Conv2d(32, 32, kernel_size=3, padding=1, stride=1),
            act(),
            nn.Conv2d(32, 64, kernel_size=3, padding=1, stride=1),
            act(),
        )
        self.fc = nn.Sequential(
            nn.Linear(262144, 64),
        )
        
    def forward(self, x):
        out = self.body(x)
        out = out.view(-1)
        out = self.fc(out)
        return out

def cross_entropy_for_onehot(pred, target):
    return torch.mean(torch.sum(- target * F.log_softmax(pred, dim=-1), 1))

def weights_init(m):
    if hasattr(m, "weight"):
        m.weight.data.uniform_(-0.5, 0.5)
    if hasattr(m, "bias"):
        m.bias.data.uniform_(-0.5, 0.5)

def label_to_onehot(target):
    target = torch.unsqueeze(target, 1)
    onehot_target = torch.zeros(target.size(0), 64, device=target.device)
    onehot_target.scatter_(1, target, 1)
    return onehot_target


tt = transforms.Compose([
        transforms.Resize((64, 64)),
        transforms.ToTensor()
    ])

original_dy_dx = pickle.load(open('model/logo.pkl', 'rb'))
device = "cpu"
criterion = cross_entropy_for_onehot
tt = transforms.Compose([
    transforms.Resize((64, 64)),
    transforms.ToTensor()
])
tp = transforms.ToPILImage()
seed = 17402
torch.manual_seed(seed)
model = Lemodel()
model.to(device)
model.apply(weights_init)

# for gx, gy in zip(model.parameters(), original_dy_dx):
#     print(gx.shape, gy.shape)
# exit()



dummy_data = torch.randn(1,3,64,64).to('cpu').requires_grad_(True)

dummy_onehot_label = label_to_onehot(torch.Tensor([int(0)]).long().to(device))

optimizer = torch.optim.LBFGS([dummy_data])
history = []
for iters in range(300):
    def closure():
        optimizer.zero_grad()
        pred = model(dummy_data)
        dummy_loss = criterion(pred,dummy_onehot_label)

        dummy_dy_dx = torch.autograd.grad(dummy_loss, model.parameters(), create_graph=True)
        grad_diff = 0
        grad_count = 0
        for gx, gy in zip(dummy_dy_dx, original_dy_dx):
            grad_diff += ((gx - gy) ** 2).sum()
            grad_count += gx.nelement()
        grad_diff.backward()
        return grad_diff

    optimizer.step(closure)
    if iters % 10 == 0:
        current_loss = closure()
        print(iters, "%.4f" % current_loss.item())
        tp(dummy_data[0].cpu()).save(f"{iters//10}.png")