image_encryption_decryption.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tifffile import imsave\n",
    "from NtruEncrypt import *\n",
    "from Polynomial import Zx\n",
    "from num_to_polynomial import *\n",
    "import matplotlib.pyplot as plt \n",
    "import imageio\n",
    "from numpy import asarray\n",
    "from PIL import Image\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scale_array(dat, out_range=(0, 1)):\n",
    "    domain = [np.min(dat, axis=0), np.max(dat, axis=0)]\n",
    "    def interp(x):\n",
    "        return out_range[0] * (1.0 - x) + out_range[1] * x\n",
    "    def uninterp(x):\n",
    "        b = 0\n",
    "        if (domain[1] - domain[0]) != 0:\n",
    "            b = domain[1] - domain[0]\n",
    "        else:\n",
    "            b =  1.0 / domain[1]\n",
    "        return (x - domain[0]) / b\n",
    "    return interp(uninterp(dat))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def revert_dimension(lst,a,b):\n",
    "    rd = []\n",
    "    for i in range(a):\n",
    "        t = []\n",
    "        for j in range(b):\n",
    "            t.append(lst[j+i*b])\n",
    "        rd.append(t)\n",
    "    return rd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def flatten_list(lst):\n",
    "    return [val for l in lst for val in l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter Message: Awnon\n",
      "Curve Parameters\n",
      "Enter A: 9\n",
      "Enter B: 7\n",
      "\n",
      "Public Key = (-52x^14) + (38x^13) + (-1x^12) + (-25x^11) + (10x^10) + (8x^9) + (-20x^8) + (39x^7) + (-32x^6) + (34x^5) + (-64x^4) + (-51x^3) + (10x^2) + (52x) + (29)\n",
      "\n",
      "Encrypted = \n",
      "[[62, 2, 14, 44, -16, 36, -35, -57, -62, -29, -47, 33, 6, 48, 25], [-23, 4, -42, 54, -60, 23, -50, 17, 36, -9, 2, 41, 26, 34, -52], [54, -51, -18, 23, -26, 2, -38, 32, 13, -19, 40, -15, 56, -43, -36], [39, -57, 29, 22, -41, 43, 29, 8, -17, 5, -61, 20, 53, 60, -56], [-49, 62, 6, 2, -52, 39, -49, 6, -37, 46, -4, 1, 26, -38, -35]]\n"
     ]
    }
   ],
   "source": [
    "d = 5\n",
    "p = 3\n",
    "q = 128\n",
    "\n",
    "message = input(\"Enter Message: \")\n",
    "\n",
    "print('Curve Parameters')\n",
    "elliptic_a = int(input(\"Enter A: \"))\n",
    "elliptic_b = int(input(\"Enter B: \"))\n",
    "\n",
    "character_polynomials,N = koblitz_encoder(message,elliptic_a,elliptic_b) \n",
    "\n",
    "public_key,private_key = generate_keypair(p,q,d,N)\n",
    "\n",
    "print('\\nPublic Key = ',end='')\n",
    "print(public_key.print_polynomial())\n",
    "\n",
    "print('\\nEncrypted = ')\n",
    "cipher_polys = []\n",
    "coefficients = []\n",
    "for element in character_polynomials:\n",
    "    cipher_text = encrypt(element,public_key,d,N,q)\n",
    "    cipher_text.coeffs = list(map(int, cipher_text.coeffs))\n",
    "    cipher_polys.append(cipher_text)\n",
    "    coefficients.append(cipher_text.coeffs)\n",
    "print(coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Convert this to image:\n",
      "\n",
      "[[1.0, 0.5161290322580645, 0.6129032258064516, 0.8548387096774194, 0.3709677419354839, 0.7903225806451613, 0.21774193548387097, 0.04032258064516129, 0.0, 0.2661290322580645, 0.12096774193548387, 0.7661290322580645, 0.5483870967741935, 0.8870967741935484, 0.7016129032258065], [0.31451612903225806, 0.532258064516129, 0.16129032258064516, 0.9354838709677419, 0.016129032258064516, 0.6854838709677419, 0.0967741935483871, 0.6370967741935484, 0.7903225806451613, 0.4274193548387097, 0.5161290322580645, 0.8306451612903226, 0.7096774193548387, 0.7741935483870968, 0.08064516129032258], [0.9354838709677419, 0.08870967741935484, 0.3548387096774194, 0.6854838709677419, 0.2903225806451613, 0.5161290322580645, 0.1935483870967742, 0.7580645161290323, 0.6048387096774194, 0.3467741935483871, 0.8225806451612904, 0.3790322580645161, 0.9516129032258065, 0.1532258064516129, 0.20967741935483872], [0.8145161290322581, 0.04032258064516129, 0.7338709677419355, 0.6774193548387096, 0.1693548387096774, 0.8467741935483871, 0.7338709677419355, 0.5645161290322581, 0.3629032258064516, 0.5403225806451613, 0.008064516129032258, 0.6612903225806451, 0.9274193548387096, 0.9838709677419355, 0.04838709677419355], [0.10483870967741936, 1.0, 0.5483870967741935, 0.5161290322580645, 0.08064516129032258, 0.8145161290322581, 0.10483870967741936, 0.5483870967741935, 0.20161290322580644, 0.8709677419354839, 0.46774193548387094, 0.5080645161290323, 0.7096774193548387, 0.1935483870967742, 0.21774193548387097]]\n",
      "\n",
      "Generating Image...\n"
     ]
    }
   ],
   "source": [
    "x_trans = scale_array(flatten_list(coefficients))\n",
    "\n",
    "print('Convert this to image:\\n')\n",
    "to_img = revert_dimension(x_trans,len(coefficients),N)\n",
    "print(to_img)\n",
    "\n",
    "to_img = np.asarray(to_img)\n",
    "\n",
    "print('\\nGenerating Image...')\n",
    "\n",
    "imsave('encrypted_image.tiff', to_img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(to_img, cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data Type: float64\n",
      "Min: 0.000, Max: 1.000\n",
      "[[1.         0.51612903 0.61290323 0.85483871 0.37096774 0.79032258\n",
      "  0.21774194 0.04032258 0.         0.26612903 0.12096774 0.76612903\n",
      "  0.5483871  0.88709677 0.7016129 ]\n",
      " [0.31451613 0.53225806 0.16129032 0.93548387 0.01612903 0.68548387\n",
      "  0.09677419 0.63709677 0.79032258 0.42741935 0.51612903 0.83064516\n",
      "  0.70967742 0.77419355 0.08064516]\n",
      " [0.93548387 0.08870968 0.35483871 0.68548387 0.29032258 0.51612903\n",
      "  0.19354839 0.75806452 0.60483871 0.34677419 0.82258065 0.37903226\n",
      "  0.9516129  0.15322581 0.20967742]\n",
      " [0.81451613 0.04032258 0.73387097 0.67741935 0.16935484 0.84677419\n",
      "  0.73387097 0.56451613 0.36290323 0.54032258 0.00806452 0.66129032\n",
      "  0.92741935 0.98387097 0.0483871 ]\n",
      " [0.10483871 1.         0.5483871  0.51612903 0.08064516 0.81451613\n",
      "  0.10483871 0.5483871  0.2016129  0.87096774 0.46774194 0.50806452\n",
      "  0.70967742 0.19354839 0.21774194]]\n"
     ]
    }
   ],
   "source": [
    "image = imageio.imread('encrypted_image.tiff')\n",
    "\n",
    "pixels = asarray(image)\n",
    "\n",
    "print('Data Type: %s' % pixels.dtype)\n",
    "print('Min: %.3f, Max: %.3f' % (pixels.min(), pixels.max()))\n",
    "# convert from integers to floats\n",
    "pixels = pixels.astype('float64')\n",
    "\n",
    "print(pixels)\n",
    "decoded_coefficients = []\n",
    "for p in pixels:\n",
    "    for j in p:\n",
    "        decoded_coefficients.append(j)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "x_inv = scale_array(decoded_coefficients,(np.min(flatten_list(coefficients), axis=0), np.max(flatten_list(coefficients), axis=0)))\n",
    "x_inv = [round(i) for i in x_inv]\n",
    "x_inv = list(map(int, x_inv))\n",
    "decoded_coefficients = revert_dimension(x_inv,len(coefficients),N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[62, 2, 14, 44, -16, 36, -35, -57, -62, -29, -47, 33, 6, 48, 25], [-23, 4, -42, 54, -60, 23, -50, 17, 36, -9, 2, 41, 26, 34, -52], [54, -51, -18, 23, -26, 2, -38, 32, 13, -19, 40, -15, 56, -43, -36], [39, -57, 29, 22, -41, 43, 29, 8, -17, 5, -61, 20, 53, 60, -56], [-49, 62, 6, 2, -52, 39, -49, 6, -37, 46, -4, 1, 26, -38, -35]]\n"
     ]
    }
   ],
   "source": [
    "print(decoded_coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[62, 2, 14, 44, -16, 36, -35, -57, -62, -29, -47, 33, 6, 48, 25], [-23, 4, -42, 54, -60, 23, -50, 17, 36, -9, 2, 41, 26, 34, -52], [54, -51, -18, 23, -26, 2, -38, 32, 13, -19, 40, -15, 56, -43, -36], [39, -57, 29, 22, -41, 43, 29, 8, -17, 5, -61, 20, 53, 60, -56], [-49, 62, 6, 2, -52, 39, -49, 6, -37, 46, -4, 1, 26, -38, -35]]\n"
     ]
    }
   ],
   "source": [
    "print(coefficients)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Complete Match!\n"
     ]
    }
   ],
   "source": [
    "if coefficients == decoded_coefficients:\n",
    "    print('Complete Match!')\n",
    "else:\n",
    "    print('Not Equal!')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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 },
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