2024 数字中国创新大赛数字安全赛道 部分题目wp

base_addr = 0x562CF20A8000
start_addr = 0x562CF2CA07A0 - base_addr
end_addr = 0xBF7D80  # 0x074EBD15位置-0x10

dump_file = open('HardTree_heap.dmp', 'rb')
file = dump_file.read()

def bytes_to_addr(byte_str):
    num = 0
    for ch in byte_str[::-1]:
        num *= 256
        num += ch
    return num

have_node = []
node_path = []

def read_node(node):
    if node not in have_node:
        if node == end_addr:
            print('flag{'+''.join(node_path)+'}')
            exit()
        have_node.append(node)
        # print(node)
        r_node = bytes_to_addr(file[node:node + 8]) - base_addr
        l_node = bytes_to_addr(file[node + 8:node + 16]) - base_addr
        if r_node != 0:
            node_path.append('r')
            read_node(r_node)
            node_path.pop()
        if l_node != 0:
            node_path.append('l')
            read_node(l_node)
            node_path.pop()
        have_node.pop()

read_node(start_addr)
# flag{llllllllllllllrlrllrllllrrlrlrlrlllrllrlllrllllllrr}

import cv2
import numpy as np

def arnold_transform(image):
    """Perform one iteration of the Arnold transform on the image."""
    N = image.shape[0]
    new_image = np.zeros_like(image)
    for x in range(N):
        for y in range(N):
            new_x = (x + y) % N
            new_y = (x + 2 * y) % N
            new_image[new_x, new_y] = image[x, y]
    return new_image

def save_images(image, max_iterations, output_prefix):
    """Save images after each iteration of the Arnold transform."""
    transformed_image = np.copy(image)
    for i in range(1, max_iterations + 1):
        transformed_image = arnold_transform(transformed_image)
        output_filename = f"compute/{i}.png"
        cv2.imwrite(output_filename, transformed_image)
        print(f"Saved {output_filename}")

# Load the image
input_image_path = 'img/5.png'  # Replace with your input image path
image = cv2.imread(input_image_path)

if image is None:
    raise ValueError("Image not found or unable to load image.")

# Number of iterations and output prefix
max_iterations = 256
output_prefix = ''

# Save transformed images
save_images(image, max_iterations, output_prefix)

from PIL import Image
import numpy as np

imgs = []
for i in range(256):
    img = Image.open(f"compute/{i}.png")
    img = np.array(img)
    imgs.append(img)

write_byte = []
for i in range(802):
    img = Image.open(f"img/{i}.png")
    img = np.array(img)
    for j in range(256):
        if np.array_equal(img, imgs[j]):
            write_byte.append(j)
            break

with open("flag", "wb") as f:
    f.write(bytes(write_byte))