# Labyrinth

• Category: Dev
• 150 points
• Solved by JCTF Team

## Description  ## Solution

As you can guess, it is a development challenge.
In this challenge we get five links for images of Where's Wally? and should reply with the coordinates of wally in the image within seconds.
Obviously we don't have time to find him by eyes, so let's throw ML on him!
LMGTFY - we found HereIsWally on GitHub.
After some configuration and downloads, we managed to run find_wally.py on one of the images and get the corret coordiates of Wally. Good work tadejmagajna!

Now we just had to add a little automation to get the images, run the detection function, normalize the coordinates and convert them to int, and send them back to the server.
This is the full script:

``````import numpy as np
import sys
import tensorflow as tf
from PIL import Image
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util

model_path = './trained_model/frozen_inference_graph.pb'

from pwn import *
import requests # to get image from the web
import shutil # to save it locally

conn = remote('labyrinth.ctf.bsidestlv.com',5000)
for i in range(5):
conn.recvuntil('https', drop=True)
image_url  = 'https'+conn.recvuntil('jpg')
image_path = image_url.split("/")[-1]
print(image_path)
r = requests.get(image_url, stream = True)

# Check if the image was retrieved successfully
if r.status_code == 200:
# Set decode_content value to True, otherwise the downloaded image file's size will be zero.
r.raw.decode_content = True

# Open a local file with wb ( write binary ) permission.
with open(image_path,'wb') as f:
shutil.copyfileobj(r.raw, f)

else:
print('Image Couldn\'t be retreived')

detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(model_path, 'rb') as fid:
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')

(im_width, im_height) = image.size
return np.array(image.getdata()).reshape(
(im_height, im_width, 3)).astype(np.uint8)

categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=1, use_display_name=True)
category_index = label_map_util.create_category_index(categories)

with detection_graph.as_default():
with tf.Session(graph=detection_graph) as sess:
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: np.expand_dims(image_np, axis=0)})

if scores < 0.1:

width, height = Image.open(image_path).size
print('Wally found')
vis_util.visualize_boxes_and_labels_on_image_array(
image_np,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index,
use_normalized_coordinates=True,
line_thickness=8)
resp = '{},{}'.format(int(round(np.squeeze(boxes)*width)), int(round(np.squeeze(boxes)*height)))

print(conn.recvline())
conn.sendline(bytearray(resp, 'utf8'))
print(conn.recvrepeat(timeout=2))``````

After five images we got:

flag: BSidesTLV2021{Here_1s_wally_lets_throw_ML_on_him}

#### P.S.

It was also possible to solve it manually. We know of another team who downloaded all the images, and solved them "by hand" (well, by eye).