N1CTF20221 Collision exp解析write up
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N1CTF2021 Collision exp解析write up
Collision
本人并没有在n1ctf2021中做出这道题，本文的最终exp是根据出题人提供的exp修改得到的。我将在本文中分享我的做这道题的经历以及解题exp的分析
原始exp链接：
https://github.com/Nu1LCTF/n1ctf-2021/blob/main/Misc/collision/exp.py
题面描述如下：
附件链接如下：
链接：https://pan.baidu.com/s/1tYfzP7YOiB8UEGwDYdMNbg
提取码：7zgc
里面包含了一个代码和一个模型的参数，代码如下
import base64
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
def load_model(path):
model = Net()
model.load_state_dict(torch.load(path, map_location="cpu"))
return model.eval()
model = load_model("convNet.pt")
def hex_hash(hash_bits):
x = hash_bits.detach().numpy()
res = [str(int(i > 0)) for i in x[0]]
return hex(int(''.join(res), 2))
L0_THRES = 54.1
L2_THRES = 6.45
src = np.frombuffer(base64.b64decode(
'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5eTkPgAAgD/l5GQ+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOPiYj8AAIA/5eRkPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOno6D0AAIA/5eTkPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADl5OQ+AACAP62srD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAx8ZGPwAAgD/p6Og9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6ejoPQAAgD/j4mI/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI6NDT8AAIA/jo0NPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOXkZD6trKw+raysPo6NDT/l5GQ+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrqio/AACAP62srD4AAAAAAAAAAAAAAAAAAAAAAAAAAOXkZD7j4mI/AACAPwAAgD8AAIA/AACAP+XkZD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq6oqPwAAgD+trKw+AAAAAAAAAAAAAAAAAAAAAAAAAACrqio/AACAPwAAgD/l5OQ+5eRkPuPiYj/HxkY/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKuqKj8AAIA/raysPgAAAAAAAAAAAAAAAAAAAADl5GQ+AACAPwAAgD+trKw+AAAAAAAAAACrqio/AACAPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrqio/AACAP62srD4AAAAAAAAAAAAAAAAAAAAAq6oqPwAAgD+trKw+AAAAAAAAAAAAAAAAq6oqPwAAgD+trKw+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAjo0NPwAAgD/l5OQ+AAAAAAAAAAAAAAAA6ejoPQAAgD/HxkY/AAAAAAAAAAAAAAAAAAAAAKuqKj8AAIA/raysPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOno6D0AAIA/AACAPwAAAAAAAAAAAAAAAKuqKj8AAIA/5eTkPgAAAAAAAAAAAAAAAAAAAADHxkY/4+JiPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAx8ZGPwAAgD/l5OQ+AAAAAAAAAACrqio/AACAP62srD4AAAAAAAAAAAAAAACtrKw+AACAP+Xk5D4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK2srD4AAIA/AACAP6uqKj/l5GQ+AACAPwAAgD/p6Og9AAAAAAAAAACtrKw+4+JiP+PiYj/p6Og9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5eTkPuPiYj8AAIA/AACAPwAAgD8AAIA/raysPq2srD6rqio/AACAPwAAgD/l5GQ+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADp6Og9jo0NPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP8fGRj/p6Og9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4+JiPwAAgD8AAIA/jo0NP62srD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI6NDT8AAIA/AACAP6uqKj+rqio/5eRkPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAjo0NP+PiYj+rqio/5eTkPuno6D0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=='),
dtype="float32")
def hash_buf(buf):
global src
arr = np.frombuffer(buf, dtype="float32")
if arr.shape[0] != 28 * 28:
raise BufferError("hacker")
arr = np.clip(arr, 0, 1)
diff = arr - src
#print(np.linalg.norm(diff, 0), np.linalg.norm(diff, 2))
if np.linalg.norm(diff, 0) > L0_THRES or np.linalg.norm(diff, 2) > L2_THRES:
raise BufferError("attack failed")
arr = torch.FloatTensor(arr).reshape(1, 1, 28, 28)
# need=0x1046822029745355ca048937f624fb87
# hash(src)=0x91460a702d62d166a9942cb7fc10d7a2
return hex_hash(model(arr))
让我们分析代码，首先给出了一个CNN模型Net
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
然后load_model函数负责载入已有的convNet.pt模型参数
def load_model(path):
model = Net()
model.load_state_dict(torch.load(path, map_location="cpu"))
return model.eval()
model = load_model("convNet.pt")
hex_hash函数用于生成hash值，其具体方法是将给定的numpy数组的值映射为0和1，当数组元素小于等于0则这一位对应的hash值为0反之为1。
def hex_hash(hash_bits):
x = hash_bits.detach().numpy()
res = [str(int(i > 0)) for i in x[0]]
return hex(int(''.join(res), 2))
接下来给定了图像攻击题目中常见的l0和l2范数约束，以及作为对照的图片的base64的值
L0_THRES = 54.1
L2_THRES = 6.45
src = np.frombuffer(base64.b64decode(
'AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5eTkPgAAgD/l5GQ+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOPiYj8AAIA/5eRkPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOno6D0AAIA/5eTkPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADl5OQ+AACAP62srD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAx8ZGPwAAgD/p6Og9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6ejoPQAAgD/j4mI/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI6NDT8AAIA/jo0NPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOXkZD6trKw+raysPo6NDT/l5GQ+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrqio/AACAP62srD4AAAAAAAAAAAAAAAAAAAAAAAAAAOXkZD7j4mI/AACAPwAAgD8AAIA/AACAP+XkZD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAq6oqPwAAgD+trKw+AAAAAAAAAAAAAAAAAAAAAAAAAACrqio/AACAPwAAgD/l5OQ+5eRkPuPiYj/HxkY/AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAKuqKj8AAIA/raysPgAAAAAAAAAAAAAAAAAAAADl5GQ+AACAPwAAgD+trKw+AAAAAAAAAACrqio/AACAPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACrqio/AACAP62srD4AAAAAAAAAAAAAAAAAAAAAq6oqPwAAgD+trKw+AAAAAAAAAAAAAAAAq6oqPwAAgD+trKw+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAjo0NPwAAgD/l5OQ+AAAAAAAAAAAAAAAA6ejoPQAAgD/HxkY/AAAAAAAAAAAAAAAAAAAAAKuqKj8AAIA/raysPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAOno6D0AAIA/AACAPwAAAAAAAAAAAAAAAKuqKj8AAIA/5eTkPgAAAAAAAAAAAAAAAAAAAADHxkY/4+JiPwAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAx8ZGPwAAgD/l5OQ+AAAAAAAAAACrqio/AACAP62srD4AAAAAAAAAAAAAAACtrKw+AACAP+Xk5D4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAK2srD4AAIA/AACAP6uqKj/l5GQ+AACAPwAAgD/p6Og9AAAAAAAAAACtrKw+4+JiP+PiYj/p6Og9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA5eTkPuPiYj8AAIA/AACAPwAAgD8AAIA/raysPq2srD6rqio/AACAPwAAgD/l5GQ+AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADp6Og9jo0NPwAAgD8AAIA/AACAPwAAgD8AAIA/AACAP8fGRj/p6Og9AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA4+JiPwAAgD8AAIA/jo0NP62srD4AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI6NDT8AAIA/AACAP6uqKj+rqio/5eRkPgAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAjo0NP+PiYj+rqio/5eTkPuno6D0AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA=='),
dtype="float32")
这张图dump出来我们可以发现是下图mnist数据集中的4，这提示了我们训练模型所使用的数据集，这张图在之后的训练中还会用到
同时也解释了为什么模型最后输出层单元数为10，因为这出题时是一个十分类任务
最后则是本题的核心函数hash_buf，这个函数做了这些事情：首先它会接收来自选手发送的图片的base64解码后的比特流，然后将其转换为numpy float32数组，然后判断大小是否为28*28(mnist数据集每张图片都是单通道28x28大小)，然后将图像像素值强制约束到[0,1]，也就是如果像素值小于0则会变成0，大于1变成1，其他不变。
global src
arr = np.frombuffer(buf, dtype="float32")
if arr.shape[0] != 28 * 28:
raise BufferError("hacker")
arr = np.clip(arr, 0, 1)
然后求出选手发送的图像与src的差值，求出l0和l2，如果不满足要求则失败，若满足要求，将发送的图像送入网络，取出倒数第二层的特征值数组转换为numpy后求hash值，如果为need的值，也就是need=0x1046822029745355ca048937f624fb87，则可以拿到flag
diff = arr - src
#print(np.linalg.norm(diff, 0), np.linalg.norm(diff, 2))
if np.linalg.norm(diff, 0) > L0_THRES or np.linalg.norm(diff, 2) > L2_THRES:
raise BufferError("attack failed")
arr = torch.FloatTensor(arr).reshape(1, 1, 28, 28)
# need=0x1046822029745355ca048937f624fb87
# hash(src)=0x91460a702d62d166a9942cb7fc10d7a2
return hex_hash(model(arr))
总的来说，为了解开这题，我们需要生成一张hash值(卷积得到的特征值)为0x1046822029745355ca048937f624fb87且和原图差别不大（满足给定的l0和l2约束）的攻击图片
首先给出完整的exp的攻击部分，来自出题人
model = load_model("./model/convNet.pt")
target_image=torch.FloatTensor(np.load("mnist.npz")['test_images'][8583]).reshape(1,1,28,28)
target_out = model(target_image).detach()
target_nsgn=-torch.sign(target_out).detach()
image = torch.FloatTensor(np.load("mnist.npz")['test_images'][22]).reshape(1,1,28,28)#这个就是src
adv=Variable(image,requires_grad=True)
loss_f=nn.L1Loss()
loss_l2=nn.MSELoss()
def attack():
global adv
max_sim = 0
cnt = 0
loss_cnt = 0
losses = []
lr=0.1
RATIO=10
itercnt=0
best = 9999
best_l0 = 9999
best_l2 = 9999
save_image(image.squeeze().detach().numpy(), 'origion.tiff')
save_image(target_image.squeeze().detach().numpy(), 'target.tiff')
for i in range(10000):
adv_out=model(adv)
l1l=loss_f(adv,image)*RATIO
hashl=torch.sum(F.relu(target_nsgn*adv_out))
l2l=loss_l2(adv,image)
loss=l1l+hashl
loss.backward()
adv.requires_grad=False
if hex_hash(adv_out)==hex_hash(target_out) and torch.sum(adv!=image).numpy() == 54 and l2l < 0.053:
break
adv=adv-adv.grad*lr
adv=adv.clamp(0,1)
if hex_hash(adv_out)==hex_hash(target_out):
best=min(best,torch.sum(torch.abs(adv-image)>0.005).numpy())
best_l0=min(best_l0,torch.sum(adv!=image).numpy())
best_l2 = min(best_l2, l2l)
itercnt+=1
mask=(torch.abs(adv-image)<np.clip(0.02+0.04*itercnt,0.02,0.4)).type(torch.FloatTensor)
adv=adv*(1-mask)+image*mask
adv.requires_grad = True
save_image(adv.squeeze().detach().numpy(), 'collision_pic.tiff')
首先可以看出这个攻击代码的主体算法是FGSM
FGSM在这里起到使得虚假图像的hash和目标hash更加相似的作用：这是因为我们的图像梯度是通过计算target_nsgn(目标特征层，这里将其设置为小于等于0时为-1和大于0为1)和我们的攻击图像的adv_out得到的，使用fgsm就可以使得图像在梯度上沿着使得俩特征层hash值相同的方向移动，因此最终可以使得攻击图像的hash值和目标hash相同。以上就是FGSM在本题中work的原因
我认为exp中对于hash值的损失函数的设计是本exp的一大亮点，不过需要提醒的是，这种hash_loss不是万能的，使用不同网络可能需要重新设计loss。在这里做着将俩hash相乘，其中一个hash意见事先乘以-1，在使用激活函数relu筛去符号相同的位后求和。因为损失函数为0是，攻击图像的hash值和目标hash值每一位正负号完全相同，所以这个损失函数可以有效地使得攻击图像的hash值朝着目标hash移动
解决了hash相同的问题，接下来让我们再看一看如何解决l0和l2的问题。我们可以这样来理解l0和l2：l0起到约束我们改变多少像素的作用而l2则起到约束我们两张图相似性的作用。因此我们可以使用l1损失函数nn.L1Loss()来对l0进行约束（这是因为numpy计算l0范数是sum(x!=0)）,使用nn.MSEloss()对l2进行约束。
实际调参时发现只需要关注l1损失，再添加l2损失反而l0和l2都无法达到要求。然后为了更加方便地满足l0和l2约束，我们将初始攻击图像设置为src，也就是之前被base64编码的图片
当然仅仅有损失函数的约束还不够，损失函数是一种偏软的约束，它虽然可以达到hash值相同的结果，但是由于其作用范围和FGSM都是在整个图像上进行的，则往往生成的攻击图像与对比图像在很多像素值上有细小的区别，这将导致难以通过较硬的l0约束
因此出题人使用了一种启发式的硬mask来解决这个问题，具体mask生成方式如下：
在hash值可以相等的前提下，这个mask将会筛去攻击图像中和对比图像差值小于阈值sita=np.clip(0.02+0.04*itercnt,0.02,0.4))的像素并将其替代为对比图像的像素值以便减小l0的值。启发式体现在，mask的sita阈值随着迭代次数的增加不断变大，最终不大于0.4，这将鼓励攻击图像生成数量更少但是对于hash值影响更大的像素，从而降低l0和l2
最终生成的攻击图像如下：
得到flag如下：
完整攻击脚本如下：
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from pwn import *
from PIL import Image
import hashlib
import string
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 32, 3, 1)
self.conv2 = nn.Conv2d(32, 64, 3, 1)
self.dropout1 = nn.Dropout(0.25)
self.dropout2 = nn.Dropout(0.5)
self.fc1 = nn.Linear(9216, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.conv1(x)
x = F.relu(x)
x = self.conv2(x)
x = F.relu(x)
x = F.max_pool2d(x, 2)
x = self.dropout1(x)
x = torch.flatten(x, 1)
x = self.fc1(x)
return x
def load_model(path):
model = Net()
model.load_state_dict(torch.load(path, map_location="cpu"))
return model.eval()
def save_image(arr, path):
im = Image.fromarray(arr)
im.save(path)
def get_hash_sim(adv_hash, std_hash):
cnt = 0
for i in range(len(adv_hash[0])):
tmp1 = adv_hash[0][i] > 0
tmp2 = std_hash[0][i] > 0
if tmp1 != tmp2:
cnt += 1
return 1 - (cnt / len(adv_hash[0]))
def cal_hash_bits(out):
return out
def hex_hash(hash_bits):
x = hash_bits.detach().numpy()
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