TrendMicroCTF - 500 Captchas! (Misc 300)

Welcome to Captcha Challenge!

The challenge description was a website, upon entering the website, you are allowed to register/sign in.


After registering and logging in, you are shown a captcha, and it seems that the challenge was to solve 500 consecutive captchas without any mistakes. However, it seems that you are able to skip captchas by refreshing the page (this turned out to be very useful).

Captchas looks like this:

To automatically solve all 500 captchas, we wrote a python script using the Python Imaging Library. The trick to solving the captchas was to do some pre-processing and to build a dataset of good characters which we will be using to solve new captchas.

Processing

Turn background/noise to white, and text to black Scan the entire picture and rank the colours by frequency, the background colour would have the highest frequency followed by the text

Dynamically slice the image by characters For each X coordinate, if the Y coordinates along the X coordinate has a black pixel:
set inLetter = true
Keep going along X until you reach a X where there is no black pixel along it, then you have your starting and ending x coordinate for your character!

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for y in range(im2.size[0]): # slice across
  for x in range(im2.size[1]): # slice down
    pix = im2.getpixel((y,x))
    if pix != 255:
      inletter = True
      last = y
  if foundletter == False and inletter == True:
    foundletter = True
    start = y

  if foundletter == True and inletter == False:
    end = y
    if end-last > 3 and end-start > 20:
      foundletter=False
      letters.append((start-3,end))
  inletter=False

With that, you can build a dataset of characters to use for comparison:

So now, we process new captchas similarly, and slice them up into characters for comparison with our dataset.
The code for comparison is as follows:

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class VectorCompare:
  def magnitude(self,concordance):
    total = 0
    for word,count in concordance.iteritems():
      total += count ** 2
    return math.sqrt(total)

  def relation(self,concordance1, concordance2):
    relevance = 0
    topvalue = 0
    for word, count in concordance1.iteritems():
      if concordance2.has_key(word):
        topvalue += count * concordance2[word]
    return topvalue / (self.magnitude(concordance1) * self.magnitude(concordance2))

def buildvector(im):
  d1 = {}

  count = 0
  for i in im.getdata():
    d1[count] = i
    count += 1

  return d1

def crack(img):
  im = img
  im = im.convert("RGBA")
  his = im.load()
  v = VectorCompare()

  imageset = []

  for img in os.listdir('./iconset4/'):
    temp = []
    if img != "Thumbs.db" and img != ".DS_Store": # windows check...
      imgs = Image.open("./iconset4/%s"%(img))
      imgs = imgs.convert("P")
      temp.append(buildvector(imgs))
    imageset.append({img[0:2]:temp})


  values = {}

  for y in xrange(im.size[1]):
      for x in xrange(im.size[0]):
        values[x,y] = his[x,y]

  count = {}
  for j,k in sorted(values.items(), key=itemgetter(1), reverse=True):
    if (k > 0 ):
      if k in count:
        count[k] = count[k]+1
      else:
        count[k] = 1

  a = sorted(count.items(), key=itemgetter(1), reverse=True)
  text = a[1][0]
  im2 = Image.new("P",im.size,255)


  for x in range(im.size[1]):
    for y in range(im.size[0]):
      pix = im.getpixel((y,x))
      if pix == text:
        pix = (0,0,0,0)
      else:
        pix = (255,255,255,255)
      im2.putpixel((y,x),pix[2])

  guessword = ""
  letters =[]
  inletter = False
  foundletter=False
  start = 0
  end = 0
  last = 0
  for y in range(im2.size[0]): # slice across
    for x in range(im2.size[1]): # slice down
      pix = im2.getpixel((y,x))
      if pix != 255:
        inletter = True
        last = y
    if foundletter == False and inletter == True:
      foundletter = True
      start = y

    if foundletter == True and inletter == False:
      end = y
      if end-last > 3 and end-start > 20:
        foundletter=False
        letters.append((start-3,end))
    inletter=False

  results = []
  for letter in letters:
    m = hashlib.md5()
    img4 = im2.crop(( letter[0] , 0, letter[1],im2.size[1] ))
    guess = []
    for image in imageset:
      for x,y in image.iteritems():
        if len(y) != 0:
          guess.append( ( v.relation(y[0],buildvector(img4)),x.decode('hex')) )
    guess.sort(reverse=True)
    results.append(guess[0])

  return results

def main():
  img= Image.open("image.png")
  print crack(img)
  
if __name__ == "__main__":
    main()

To achieve a 100% accuracy, we need to reject certain solutions that contains risk, to do this, we reject a solution if any of the characters has a similarity score that is less that 0.99. With this, we were able to achieve a 100% accuracy with a rejection rate of ~1 per captcha.

</br> After about ~2 hours, our script finally solve all 500 captchas.


And we have our flag: TMCTF{217dae3fd34cee799658d4552e37827f}

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