Logo: Signin
logo = """
####################################################################################################
############################ # #####################################################################
#### ## ########### ## ## ########### ######## ######
#### ## ######### ########## ###### ###
#### ######## ######## ########################## ############# ############ ##
#### ########### ###### ########### ############## ############ ###################
### ############# ##### ########### ############# ############ ###################
### ############## #### ############# ############# ############# ###################
### ############## ### ############# ############ ############## #################
### ############## ### ############### ############ ############### ###############
### ############## #### ############### ############ ################# #############
### ############# #### ############### ########### #################### ##########
### ############ ##### ############### ########### ###################### #########
### #### ###### ############## ########### ######################## #######
### #### ######## ############# ########### ########################## ######
### ######### ######### ############# ############ ########################## ######
### ########## ######## ########### ############# ########################## ######
### ########### ######## ######### ############# ############# ######### ######
### ############ ######### ###### ############ ############### #### #######
### ############## ########### ############ ######### #########
#### ############################### ############################################## ############
####################################################################################################
"""Logo: 2024
有长度限制，考虑对logo进行压缩。
首先把第一行和最后一行换成#*100
中间的内容通过相同字符长度进行压缩。正好是‘#’和’ ’间隔。
这里用39以后的字符防止出现单引号/双引号。
from RestrictedPython import compile_restricted, safe_builtins
from RestrictedPython.Eval import default_guarded_getitem
from RestrictedPython.Guards import full_write_guard
ROIS_LOGO = """
####################################################################################################
############################ # #####################################################################
#### ## ########### ## ## ########### ######## ######
#### ## ######### ########## ###### ###
#### ######## ######## ########################## ############# ############ ##
#### ########### ###### ########### ############## ############ ###################
### ############# ##### ########### ############# ############ ###################
### ############## #### ############# ############# ############# ###################
### ############## ### ############# ############ ############## #################
### ############## ### ############### ############ ############### ###############
### ############## #### ############### ############ ################# #############
### ############# #### ############### ########### #################### ##########
### ############ ##### ############### ########### ###################### #########
### #### ###### ############## ########### ######################## #######
### #### ######## ############# ########### ########################## ######
### ######### ######### ############# ############ ########################## ######
### ########## ######## ########### ############# ########################## ######
### ########### ######## ######### ############# ############# ######### ######
### ############ ######### ###### ############ ############### #### #######
### ############## ########### ############ ######### #########
#### ############################### ############################################## ############
####################################################################################################
"""
logo = "".join(ROIS_LOGO.strip().split("\n")[1:-1])
from string import printable
arr = []
lastchr = logo[0]
count = 1
for i in range(1,len(logo)):
if logo[i] == lastchr:
count += 1
else:
arr.append(count)
lastchr = logo[i]
count = 1
arr.append(count)
payload = bytearray([i+39 for i in arr]).decode();
print(len(payload))
loc = {}
cmdline = f"""t=100
c=f"{payload[::-1]}"
i=229
s="#"*t
while i:
i=i-1
s=s+"# "[i&1]*(ord(c[i])-39)
s=s+'#'*t
r=''
while s:
r=r+s[:t]+'\\n';s=s[t:]
logo=r"""
exec(compile_restricted(cmdline,"<inline>","exec"),{
"__builtins__": safe_builtins,
"_getitem_": default_guarded_getitem,
"_write_": full_write_guard,
},loc)
assert ROIS_LOGO.strip() == loc["logo"].strip()
assert len(cmdline) < len(ROIS_LOGO) * .2024
print("[+] Payload Generated: ",cmdline)
print("[+] Length:",len(cmdline))sec-image
import os
import shutil
from PIL import Image
import numpy as np
for i in range(10):
if os.path.exists(f'flag{i}'):
shutil.rmtree(f'flag{i}')
os.mkdir(f'flag{i}')
p=np.array(Image.open(f'flag{i}.png'))
Image.fromarray(p[::2,::2]).save(f'flag{i}/0.png')
Image.fromarray(p[::2,1::2]).save(f'flag{i}/1.png')
Image.fromarray(p[1::2,::2]).save(f'flag{i}/2.png')
Image.fromarray(p[1::2,1::2]).save(f'flag{i}/3.png')ezlogin
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
import os
from sklearn.manifold import TSNE
from torch.utils.data import DataLoader
import matplotlib.pyplot as plt
import numpy as np
import imageio as iio
import warnings
import functools
import signal
import hashlib
import time
import torchattacks
from PIL import Image
from torchvision import transforms
warnings.filterwarnings("ignore")
device = torch.device("cuda")
num_epochs = 50
batch_size = 512
learning_rate = 1e-4
# train_data = dataset = torchvision.datasets.EMNIST(root='data/',
# download=True,
# transform=transforms.ToTensor(),
# train=True,
# split='balanced')
# test_data = dataset = torchvision.datasets. EMNIST(root='data/',
# download=True,
# train=False,
# transform=transforms.ToTensor(),split='balanced')
# train_loader = DataLoader(train_data, batch_size=batch_size, shuffle=True)
# test_loader = DataLoader(test_data, 1, shuffle=False)
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 2, 3, padding=1)
self.conv2 = nn.Conv2d(2, 8, 3, padding=1)
self.conv3 = nn.Conv2d(8, 32, 3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(32 * 3 * 3, 1024)
self.fc2 = nn.Linear(1024, 512)
self.fc3 = nn.Linear(512, 256)
self.fc4 = nn.Linear(256, 128)
self.fc5 = nn.Linear(128, 47)
def forward(self, x):
x = self.pool(self.conv1(x))
x = self.pool(self.conv2(x))
x = self.pool(self.conv3(x))
x = x.view(-1, 32 * 3 * 3)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
x = F.relu(self.fc4(x))
x = self.fc5(x)
return x
if __name__ == "a":
try:
loaded = False
try:
model = CNN().to(device)
model.load_state_dict(torch.load('model.pt.state'))
loaded = True
except:
model = CNN().to(device)
try:
feature = torch.tensor([[-6.19499969e+01, -1.56200895e+01, -3.52624054e+01, -1.34233132e-01,
-6.48261490e+01, -1.47979248e+02, -5.15059547e+01, -1.14444227e+01,
4.33434563e+01, -3.69645386e+01, 2.00579977e+00, 4.74611549e+01,
-6.33986130e+01, -1.57887411e+01, -2.87570419e+01, -5.35021248e+01,
-1.73028266e+00, -3.61370316e+01, -7.58331375e+01, -7.46535110e+01,
-7.24118347e+01, -4.76773834e+01, 6.51892662e+00, -5.07196846e+01,
-1.03041328e+02, 4.72574463e+01, 9.03826065e+01, 5.30947495e+01,
-5.03226738e+01, -1.50200531e+02, -3.46447792e+01, -4.23207245e+01,
6.44030609e+01, -5.05351334e+01, -4.11206970e+01, -2.18300457e+01,
2.70750694e+01, -1.00022865e+02, 3.77698517e+01, -3.60703392e+01,
-6.88536682e+01, 1.16945248e+01, -4.62400284e+01, -4.79546585e+01,
6.10636101e+01, -1.12650543e+02, -1.34837357e+02,]], dtype=torch.float32).numpy()
print("input image bytes(28 * 28):")
# readres = readNumpy(28 * 28, np.uint8)
readres=np.array([[154,254, 0,102,232,137, 76,112, 68, 52,137, 60,108,239, 46, 72,170, 226,248,107,115,194, 55,195,223,150, 48,237], [162, 81,129, 26, 48,211, 61,196, 14,142,181, 20, 32,150,222,144, 43, 120, 63, 1,118, 99, 54, 0, 29,173,174,104], [ 0, 37, 98,113,151,219,115, 68, 75, 41, 11, 97,177,103,160,157, 71, 136,156,124,142,142,152, 3, 20, 15, 59,150], [ 35,156, 66, 77,111, 12, 0,154,112,187,138,222,127,171, 85, 98,125, 105, 50, 69,112, 96,143, 69, 70,165, 26,154], [ 90,168,120,114, 95, 4, 61, 80,215,213,162, 61,219, 0, 92, 99, 42, 142,122,185,154,195,172,164,145,111, 11, 53], [119, 54, 89,211,131, 28,242,210,242, 69, 36,249, 86,165,218,203, 97, 232, 52,136, 66,110,136,106,190, 1,176, 89], [219, 55,170, 34,206,174,115,252,226, 55, 94,158, 37, 62,100,153, 0, 179,254,254, 33,249,204,244,205,186, 78,197], [254, 54,186,238,214,128,147, 39,168,112,138,143,162, 75,120, 42,199, 235,116, 18,184,251, 25,132,252,229,202,251], [ 23,237,199,116,125, 41,105,205, 55,111,165, 13,197,254, 7, 37,144, 235,183, 0,188, 96, 8, 1,193,124, 26,220], [193,156,152,195,254,125,231, 23,217,219,220, 0,183,193,222,250, 52, 187,213, 97, 96, 18, 24, 0,223, 41, 67,139], [112,254, 76,236,219,173,133,129, 86,171,150, 80,210, 74,189,190,160, 98,130,254,227, 19, 88, 81,225,187, 8,113], [212,205,221, 46, 0,210,111, 21,114,193, 48,214,188,158, 41, 27,238, 222,234,106,192, 91, 25,131,150, 75,159, 31], [ 16,150,140,183, 7, 89,151, 11, 75, 2, 60,166, 14,140, 36, 50,150, 186, 17,107,184, 61,201, 52, 31,176, 38, 20], [ 0,158,140,121, 37,155, 4,182,137,167,141, 61, 93,145, 15,156, 30, 221,109,164,167, 34,232,122, 52,167,217, 75], [243, 69,130,189,105,167,195,115, 31, 35, 58,170, 84,197, 85,225,216, 167,119, 32, 26,208, 19,123, 98,130,194, 57], [203, 50,225,242,228,153, 39,231, 75, 79,143, 16,246, 13, 5, 83,229, 59, 12,212,254, 39, 90,246,135,133,110,158], [ 63,146, 40,131,238,136,137, 45, 95,149, 11,119, 55,218,104,254,199, 101, 83,220, 20,124,243,114, 50,219, 20, 63], [ 32,103,114,188,230, 39, 0,132, 97,254,254,165,175,144, 0,207,198, 13,211,189,100,157,145,161,117, 90,120,137], [254, 60, 92, 5,202,159,167, 66, 75,165,252, 56,115, 8,233,240,180, 56, 65,179, 87, 5,107,162,151,155,222, 44], [133, 73,223,189,205, 0, 7, 59, 52, 0, 76,249,120, 0, 35, 94,161, 160, 0,167, 69,225, 0, 71,199,199,250,153], [ 38,154, 84,214, 0,162,193,238,158,159, 65,170, 23, 32, 80,186, 80, 90,163,165,175,212, 57,204,174, 81,184,183], [203, 27, 84, 65, 66,119,221,215, 60, 55, 3, 39,166, 33, 75,142, 96, 76,179, 94,181, 15,220,107,122,130, 37, 51], [ 99,247,113,161, 77,188,136, 74, 0, 81, 94, 6, 72, 51, 81, 89, 84, 55,169, 97, 42,183,209,112,229, 2,157, 49], [199,181, 46,111,115, 51,151,150, 65,103, 86,167, 79,130,205, 47,110, 73, 95,177,221, 19,110, 86,142, 63, 85,140], [174,189,224,133,251, 68, 0, 0, 0, 0,113, 24,145, 25,153,124,163, 106, 93,250, 60,117,253, 88, 58, 54,160,163], [ 95, 36,111,253,199,192,138,209, 54, 7, 47,174, 67,188, 77,103,254, 223, 39, 55,196, 12,127,139,143,132, 67,233], [ 64, 26,125,228,129,254,127,254,147,119,114, 85, 89, 6, 67,209,149, 254,144,236,254,205, 93, 20, 27,120,159,149], [143,109, 32,250,190,121, 85,254,113,253, 66, 20, 74,106,189,121,117, 215, 43,254,190, 76,239,193,100,131,147, 32]])
if isinstance(readres, None.__class__):
print("Error!")
os._exit(0)
img_new_tensor = torch.tensor(readres.reshape(28, 28), dtype=torch.float32) / 255.0
img_new_tensor = img_new_tensor.unsqueeze(0)
model.eval()
feature_new = model(img_new_tensor.to(device)).detach().cpu().numpy()
dist = np.sqrt(np.sum((feature - feature_new)**2))
print("distance:", dist)
if dist < 6:
print("flag{123213123232131231232131}")
except:
__import__("traceback").print_exc()
print("exit!")
os._exit(0)
except:
print("exit!")
os._exit(0)
def crack():
try:
model = CNN().to(device)
model.load_state_dict(torch.load('model.pt.state'))
loaded = True
except:
model = CNN().to(device)
model.eval()
attack=torchattacks.FGSM(model,eps=1/255.0)
attack.set_mode_targeted_by_label()
dist=1e300
# img=Image.new('L',(25,25),128)
# img=transforms.ToTensor()(img)
feature=feature = torch.tensor([[-6.19499969e+01, -1.56200895e+01, -3.52624054e+01, -1.34233132e-01,
-6.48261490e+01, -1.47979248e+02, -5.15059547e+01, -1.14444227e+01,
4.33434563e+01, -3.69645386e+01, 2.00579977e+00, 4.74611549e+01,
-6.33986130e+01, -1.57887411e+01, -2.87570419e+01, -5.35021248e+01,
-1.73028266e+00, -3.61370316e+01, -7.58331375e+01, -7.46535110e+01,
-7.24118347e+01, -4.76773834e+01, 6.51892662e+00, -5.07196846e+01,
-1.03041328e+02, 4.72574463e+01, 9.03826065e+01, 5.30947495e+01,
-5.03226738e+01, -1.50200531e+02, -3.46447792e+01, -4.23207245e+01,
6.44030609e+01, -5.05351334e+01, -4.11206970e+01, -2.18300457e+01,
2.70750694e+01, -1.00022865e+02, 3.77698517e+01, -3.60703392e+01,
-6.88536682e+01, 1.16945248e+01, -4.62400284e+01, -4.79546585e+01,
6.10636101e+01, -1.12650543e+02, -1.34837357e+02,]], dtype=torch.float32).to(device)
tensored = torch.rand(1, 1, 28, 28).to(device)
tensored=torch.tensor([[[[ 6.0288e-01, 1.1362e+00, 5.9489e-05, 4.2852e-01, 9.1239e-01,
5.7314e-01, 3.1315e-01, 4.4792e-01, 2.8514e-01, 2.0634e-01,
5.4083e-01, 2.2748e-01, 4.1847e-01, 9.3224e-01, 1.8290e-01,
2.8779e-01, 6.7119e-01, 8.8639e-01, 9.6913e-01, 4.1018e-01,
4.4693e-01, 7.5400e-01, 2.2920e-01, 7.9548e-01, 9.1583e-01,
5.9450e-01, 1.9110e-01, 9.1565e-01],
[ 6.1580e-01, 3.3205e-01, 5.0228e-01, 1.0933e-01, 2.0404e-01,
8.3139e-01, 2.2867e-01, 8.0309e-01, 7.0727e-02, 6.0615e-01,
7.1967e-01, 8.6963e-02, 1.2258e-01, 5.8602e-01, 8.7928e-01,
5.6752e-01, 1.7299e-01, 4.7335e-01, 2.8381e-01, -7.0812e-02,
5.0558e-01, 3.9065e-01, 2.2163e-01, -7.6157e-02, 1.5868e-01,
7.4678e-01, 7.0252e-01, 4.0050e-01],
[-7.1028e-02, 1.1317e-01, 3.6623e-01, 4.1133e-01, 5.8208e-01,
8.3339e-01, 4.8500e-01, 3.0252e-01, 3.1078e-01, 1.8317e-01,
5.6958e-02, 3.8725e-01, 7.0658e-01, 3.9187e-01, 6.2303e-01,
6.2260e-01, 2.7760e-01, 5.3419e-01, 6.4757e-01, 5.1142e-01,
5.9661e-01, 5.4655e-01, 6.1045e-01, -4.4963e-03, 1.2481e-01,
9.2860e-02, 2.6914e-01, 5.8491e-01],
[ 1.4626e-01, 6.0557e-01, 2.5293e-01, 3.1025e-01, 4.7795e-01,
5.3919e-02, -6.1463e-03, 6.0654e-01, 4.2319e-01, 7.4492e-01,
5.5084e-01, 8.6132e-01, 4.9652e-01, 6.6520e-01, 3.3005e-01,
3.7835e-01, 5.1184e-01, 4.1918e-01, 1.5690e-01, 2.5480e-01,
4.5393e-01, 3.7624e-01, 5.4659e-01, 2.7696e-01, 2.4687e-01,
7.1665e-01, 1.2837e-01, 6.1163e-01],
[ 3.4478e-01, 6.6759e-01, 4.8163e-01, 4.8742e-01, 3.8671e-01,
4.2637e-02, 2.8213e-01, 3.2243e-01, 8.7444e-01, 8.3748e-01,
6.3151e-01, 2.3735e-01, 8.7876e-01, -3.9210e-02, 3.9525e-01,
3.7733e-01, 1.6370e-01, 5.4120e-01, 4.7166e-01, 7.5188e-01,
6.1869e-01, 8.4919e-01, 6.7780e-01, 6.4300e-01, 5.7504e-01,
4.7048e-01, 5.7314e-02, 2.1222e-01],
[ 4.7097e-01, 2.0323e-01, 3.3424e-01, 8.1913e-01, 5.3727e-01,
9.9891e-02, 1.0234e+00, 8.4038e-01, 1.1350e+00, 2.5937e-01,
1.4993e-01, 1.0317e+00, 3.5118e-01, 6.5605e-01, 8.7078e-01,
7.9255e-01, 3.8220e-01, 8.6914e-01, 2.2715e-01, 5.4343e-01,
2.7454e-01, 4.7493e-01, 5.3477e-01, 4.1217e-01, 7.1557e-01,
-1.9818e-02, 6.7947e-01, 3.4895e-01],
[ 8.1369e-01, 2.2739e-01, 6.2416e-01, 1.3195e-01, 8.0578e-01,
6.8169e-01, 4.5694e-01, 9.9322e-01, 9.0871e-01, 2.2177e-01,
3.5772e-01, 6.1249e-01, 1.4451e-01, 2.1517e-01, 3.8865e-01,
6.2094e-01, -1.3825e-01, 7.2339e-01, 1.0589e+00, 1.2942e+00,
9.6191e-02, 1.0701e+00, 8.1473e-01, 1.0005e+00, 8.0526e-01,
7.1779e-01, 3.1163e-01, 7.7659e-01],
[ 9.6146e-01, 1.6313e-01, 6.8609e-01, 9.1936e-01, 8.2348e-01,
4.9940e-01, 5.5067e-01, 1.3772e-01, 6.5934e-01, 4.4348e-01,
5.5429e-01, 5.5286e-01, 6.4212e-01, 2.9482e-01, 4.9009e-01,
1.5313e-01, 7.6001e-01, 9.5773e-01, 4.4870e-01, 7.0587e-02,
6.3051e-01, 9.7914e-01, 1.0483e-01, 5.0774e-01, 1.0615e+00,
9.0353e-01, 7.8602e-01, 1.0277e+00],
[ 1.1235e-01, 9.0992e-01, 7.2119e-01, 4.3361e-01, 4.7386e-01,
1.4313e-01, 4.0376e-01, 8.1287e-01, 2.3066e-01, 4.5863e-01,
6.8672e-01, 5.9185e-02, 7.6155e-01, 1.0895e+00, 1.0696e-02,
1.1955e-01, 5.9255e-01, 9.0098e-01, 7.3014e-01, -2.2989e-01,
7.0312e-01, 3.3254e-01, -3.0847e-02, 1.3195e-02, 7.2566e-01,
4.7231e-01, 1.1454e-01, 8.7590e-01],
[ 7.7808e-01, 6.2572e-01, 6.0420e-01, 7.3975e-01, 9.9742e-01,
4.9199e-01, 8.9071e-01, 1.0273e-01, 8.1529e-01, 8.7398e-01,
8.5837e-01, -9.3574e-02, 7.4139e-01, 7.6849e-01, 8.9423e-01,
1.0978e+00, 2.1246e-01, 7.1140e-01, 7.9466e-01, 3.9209e-01,
3.7501e-01, 7.2755e-02, 9.0850e-02, 2.2957e-02, 8.6281e-01,
1.4462e-01, 2.7115e-01, 5.4973e-01],
[ 4.4107e-01, 1.0529e+00, 2.9880e-01, 9.2260e-01, 8.4810e-01,
6.4860e-01, 4.9903e-01, 4.9807e-01, 3.0239e-01, 6.5316e-01,
5.8924e-01, 3.5929e-01, 8.7163e-01, 3.3495e-01, 7.8563e-01,
7.7249e-01, 6.3549e-01, 3.7659e-01, 4.8119e-01, 1.1958e+00,
8.3194e-01, 6.4911e-02, 3.4109e-01, 3.0952e-01, 8.7574e-01,
7.5492e-01, 4.5016e-02, 4.5166e-01],
[ 7.9193e-01, 8.0962e-01, 8.3640e-01, 1.7199e-01, 8.5984e-03,
8.0833e-01, 4.3951e-01, 1.0346e-01, 4.5846e-01, 7.6686e-01,
1.8354e-01, 8.5022e-01, 7.3330e-01, 5.7344e-01, 1.2528e-01,
1.0215e-01, 9.3043e-01, 8.7269e-01, 9.3331e-01, 3.8962e-01,
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4.0771e-01, 6.3883e-01, 5.0394e-01, 6.4061e-01, 4.4024e-01,
3.4866e-01, 4.6830e-01, 5.3808e-01],
[ 1.0570e+00, 2.3428e-01, 3.2460e-01, -7.7438e-02, 8.1746e-01,
6.5163e-01, 6.8952e-01, 2.5616e-01, 2.8494e-01, 6.2445e-01,
1.0361e+00, 2.0589e-01, 4.7993e-01, 5.7002e-02, 8.7431e-01,
9.3684e-01, 7.1176e-01, 2.3189e-01, 2.5901e-01, 7.3493e-01,
3.4834e-01, 2.9198e-02, 4.1804e-01, 5.6902e-01, 5.4621e-01,
5.9270e-01, 8.5254e-01, 1.8216e-01],
[ 5.0931e-01, 2.6628e-01, 8.5866e-01, 7.5221e-01, 8.4197e-01,
-5.4657e-02, 2.3840e-02, 2.4812e-01, 2.1561e-01, -9.8964e-03,
3.4617e-01, 1.1136e+00, 4.6051e-01, -4.9158e-03, 1.4681e-01,
3.6429e-01, 6.5646e-01, 6.4155e-01, 2.6962e-03, 6.3122e-01,
2.7927e-01, 9.0141e-01, 1.5441e-02, 3.0391e-01, 7.3944e-01,
8.2543e-01, 9.7200e-01, 6.1424e-01],
[ 1.6109e-01, 6.1200e-01, 3.3933e-01, 8.9053e-01, 1.2685e-02,
6.3852e-01, 7.7605e-01, 8.9602e-01, 6.2727e-01, 5.3226e-01,
2.6726e-01, 6.6007e-01, 1.1367e-01, 1.7282e-01, 3.0375e-01,
7.3988e-01, 3.2879e-01, 3.7334e-01, 6.7422e-01, 6.4509e-01,
6.8196e-01, 8.1858e-01, 2.3444e-01, 8.1845e-01, 6.8228e-01,
3.6365e-01, 7.3153e-01, 7.2874e-01],
[ 8.2190e-01, 1.1075e-01, 3.2542e-01, 2.3088e-01, 2.3919e-01,
4.7973e-01, 8.8109e-01, 8.5061e-01, 2.3921e-01, 1.8530e-01,
-4.5518e-02, 1.7482e-01, 6.2579e-01, 1.3013e-01, 2.8357e-01,
5.7316e-01, 4.1975e-01, 3.1367e-01, 7.2057e-01, 3.7517e-01,
6.7830e-01, 5.4520e-02, 8.7112e-01, 4.1475e-01, 5.3532e-01,
5.3351e-01, 1.5299e-01, 2.1696e-01],
[ 3.8619e-01, 1.0056e+00, 4.5137e-01, 6.5311e-01, 2.9081e-01,
7.6705e-01, 5.2778e-01, 3.1267e-01, -1.2548e-01, 3.2731e-01,
3.7135e-01, 2.2293e-02, 2.5009e-01, 2.1048e-01, 3.4622e-01,
3.5983e-01, 3.5104e-01, 2.1220e-01, 6.7556e-01, 3.7475e-01,
1.6431e-01, 7.0422e-01, 8.0087e-01, 4.7100e-01, 9.7766e-01,
1.8277e-02, 6.1969e-01, 1.9711e-01],
[ 7.8155e-01, 7.1101e-01, 2.0455e-01, 4.4153e-01, 4.8890e-01,
2.1791e-01, 6.4668e-01, 5.9280e-01, 2.9211e-01, 4.1581e-01,
3.8185e-01, 6.4024e-01, 3.1393e-01, 5.0632e-01, 8.2283e-01,
1.8128e-01, 4.4095e-01, 2.9088e-01, 3.6859e-01, 7.1484e-01,
8.6203e-01, 7.5778e-02, 3.8975e-01, 3.4041e-01, 5.6930e-01,
2.7090e-01, 3.4177e-01, 5.4593e-01],
[ 6.9933e-01, 7.6190e-01, 9.1418e-01, 5.1421e-01, 1.0412e+00,
2.7945e-01, -1.5315e-01, 3.3666e-02, 2.4592e-02, -4.8662e-02,
4.8521e-01, 6.3433e-02, 5.8517e-01, 8.8535e-02, 5.8993e-01,
4.9659e-01, 6.3918e-01, 4.2742e-01, 3.5838e-01, 9.8733e-01,
2.2435e-01, 4.5459e-01, 1.0969e+00, 3.6596e-01, 2.4139e-01,
2.5908e-01, 6.2124e-01, 6.3509e-01],
[ 3.7773e-01, 1.5590e-01, 4.6437e-01, 1.0532e+00, 7.7038e-01,
7.7253e-01, 5.5581e-01, 8.5207e-01, 1.9952e-01, 4.3578e-04,
1.8705e-01, 7.2782e-01, 2.8181e-01, 7.5687e-01, 3.1991e-01,
3.8863e-01, 1.0320e+00, 8.8327e-01, 1.5132e-01, 2.2798e-01,
7.8556e-01, 7.4501e-02, 5.0239e-01, 5.6149e-01, 5.7039e-01,
5.4560e-01, 2.5559e-01, 9.1737e-01],
[ 2.4790e-01, 9.1259e-02, 4.8480e-01, 8.7040e-01, 5.0994e-01,
1.2352e+00, 4.3721e-01, 1.1540e+00, 5.7661e-01, 4.4002e-01,
4.3529e-01, 3.4350e-01, 3.6314e-01, 3.2342e-02, 2.7325e-01,
8.1995e-01, 5.7767e-01, 1.1382e+00, 5.6145e-01, 9.2122e-01,
1.0223e+00, 8.3465e-01, 3.4931e-01, 6.5662e-02, 9.8764e-02,
4.7991e-01, 6.3983e-01, 6.0949e-01],
[ 5.7333e-01, 4.3587e-01, 1.1823e-01, 9.6195e-01, 7.3666e-01,
4.4254e-01, 3.1766e-01, 1.0803e+00, 4.1708e-01, 9.6695e-01,
2.6936e-01, 8.2981e-02, 2.9549e-01, 4.1854e-01, 7.3636e-01,
4.8149e-01, 4.6612e-01, 8.4265e-01, 1.7358e-01, 1.0392e+00,
7.4474e-01, 2.9865e-01, 9.7939e-01, 7.7768e-01, 4.0470e-01,
5.2455e-01, 5.9163e-01, 1.4044e-01]]]], device='cuda:0',
requires_grad=True)
# tensored = torch.rand(1, 1, 28, 28).to(device)
# tensored=torch.clamp(tensored, min=0,max=1)
# img_save=transforms.ToPILImage()(tensored[0])
# img_save.save('out.png')
while True:
tensored.requires_grad_()
embedding = model(tensored)[0]
dist = torch.sqrt(torch.sum((feature - embedding)**2))
if dist <= 6:
tensor1 = tensored * 255
array = tensor1.detach().cpu().numpy()
array = array.astype(np.uint8)
array1=torch.tensor(array).to("cuda:0")
img_new_tensor = torch.tensor(array1.reshape(28, 28), dtype=torch.float32) / 255.0
img_new_tensor = img_new_tensor.unsqueeze(0)
feature_new = model(img_new_tensor.to(device)).detach().cpu().numpy()
ddd = np.sqrt(np.sum((feature.cpu().numpy() - feature_new)**2))
if ddd<6:
print(array)
exit(0)
dist.backward()
tensored = tensored.detach() - tensored.grad**3 *0.0000001
tensored=torch.clamp(tensored, min=0,max=1)
print(dist)
crack()s1ayth3sp1re
反编译搜索3000找到
两坨数组拿出来异或即可。
arr1 = [164, 158, 95, 107, 4, 215, 108, 115, 5, 8, 25, 57, 41, 236, 231, 17, 85]
arr2 = [246,221,11,45,127,148,45,36,70,73,78,8,98,141,140,112,40]
for i in range(len(arr2)):
print(chr(arr1[i] ^ arr2[i]),end="")