BLACK MASK: 07/08/26

Wednesday, July 8, 2026

Fiber optics propagation stimulation and distribution brute force cluster in our time AES 256 decryption attempt

<!DOCTYPE html>

<html>

<head>

<title>Fiber Optics & Distributed Brute-Force Simulation</title>

<style>

body { font-family: Arial; padding: 20px; }

.box { border: 1px solid #ccc; padding: 15px; margin-bottom: 20px; }

</style>

</head>

<body>

<h2>Fiber Optics Propagation & Distributed Brute-Force Simulation</h2>

<h3>Fiber Optic Parameters</h3>

<label>Ocean Cable Length (km):</label>

<label>Cluster Nodes:</label>

<label>Keys per Second per Node:</label>

<button onclick="simulate()">Simulate</button>

</div>

<h3>Results</h3>

</div>

// Speed of light in fiber (approx)

const FIBER_SPEED = 200000; // km per second

// AES-256 keyspace

const AES256_KEYS = BigInt("115792089237316195423570985008687907853269984665640564039457584007913129639936");

// Format big numbers

function formatBig(n) {

return n.toLocaleString("en-US");

}

function simulate() {

let cableLength = parseFloat(document.getElementById("cableLength").value);

let nodes = parseFloat(document.getElementById("nodes").value);

let kps = parseFloat(document.getElementById("kps").value);

// Fiber latency (one-way)

let latencySeconds = cableLength / FIBER_SPEED;

// Cluster brute-force speed

let clusterSpeed = nodes * kps;

// Time to brute-force AES-256 (worst-case)

let clusterSpeedBig = BigInt(clusterSpeed);

let secondsToCrack = AES256_KEYS / clusterSpeedBig;

// Convert to years

let years = Number(secondsToCrack) / (60 * 60 * 24 * 365);

document.getElementById("latency").innerHTML =

"Fiber Latency (one-way): " + latencySeconds.toFixed(4) + " seconds";

document.getElementById("clusterSpeed").innerHTML =

"Cluster Speed: " + formatBig(clusterSpeed) + " keys/second";

document.getElementById("aesTime").innerHTML =

"Estimated Time to Brute-Force AES-256: " + years.toExponential(4) + " years";

}

</script>

</body>

</html>

AES 256 ( Grover's algorithms) decryption assumptions quantum machine need more 15.248 days in the future speed

import math

# -----------------------------

# 1. Core parameters

# -----------------------------

AES256_LOGICAL_QUBITS = 6500 # literature midpoint

GROVER_ITERATIONS = 2**128 # Grover complexity for AES-256

T_GATE_DEPTH_PER_AES = 20000 # approximate reversible AES depth

SURFACE_CODE_OVERHEAD = 1000 # physical/logical qubit ratio

TARGET_RUNTIME_SECONDS = 365 * 24 * 3600 # 1 year

# -----------------------------

# 2. Physical qubit model

# -----------------------------

def physical_qubits(logical_qubits=AES256_LOGICAL_QUBITS,

overhead=SURFACE_CODE_OVERHEAD):

return logical_qubits * overhead

# -----------------------------

# 3. Parallel Grover model

# -----------------------------

def required_parallel_instances(target_runtime_seconds=TARGET_RUNTIME_SECONDS,

grover_iterations=GROVER_ITERATIONS,

gate_time=1e-6):

"""

gate_time = logical gate time (1 microsecond default)

"""

iterations_per_second = 1 / gate_time

required = grover_iterations / (iterations_per_second * target_runtime_seconds)

return math.ceil(required)

# -----------------------------

# 4. Total physical qubits

# -----------------------------

def total_physical_qubits(parallel_instances,

logical_qubits=AES256_LOGICAL_QUBITS,

overhead=SURFACE_CODE_OVERHEAD):

return parallel_instances * physical_qubits(logical_qubits, overhead)

# -----------------------------

# 5. Full simulation wrapper

# -----------------------------

def simulate(target_runtime_seconds=TARGET_RUNTIME_SECONDS,

logical_qubits=AES256_LOGICAL_QUBITS,

overhead=SURFACE_CODE_OVERHEAD,

gate_time=1e-6):

P = required_parallel_instances(target_runtime_seconds,

GROVER_ITERATIONS,

gate_time)

total = total_physical_qubits(P, logical_qubits, overhead)

return {

"logical_qubits_per_instance": logical_qubits,

"physical_qubits_per_instance": physical_qubits(logical_qubits, overhead),

"parallel_instances_required": P,

"total_physical_qubits_required": total

}

# -----------------------------

# 6. Run the simulation

# -----------------------------

result = simulate()

for k, v in result.items():

print(f"{k}: {v}")

# ============================================================

# AES‑256 Grover Resource Estimation Notebook

# ============================================================

import math

import numpy as np

import matplotlib.pyplot as plt

from ipywidgets import interact, FloatSlider, IntSlider, Dropdown

# ------------------------------------------------------------

# 1. Core constants from literature

# ------------------------------------------------------------

AES256_LOGICAL_QUBITS = 6500 # midpoint from Grassl et al.

GROVER_ITERATIONS = 2**128 # Grover complexity for AES-256

DEFAULT_SURFACE_CODE_OVERHEAD = 1000 # physical/logical qubit ratio

DEFAULT_GATE_TIME = 1e-6 # 1 microsecond logical gate

SECONDS_PER_YEAR = 365 * 24 * 3600

# ------------------------------------------------------------

# 2. Physical qubit model

# ------------------------------------------------------------

def physical_qubits(logical_qubits, overhead):

return logical_qubits * overhead

# ------------------------------------------------------------

# 3. Parallel Grover model

# ------------------------------------------------------------

def required_parallel_instances(target_runtime_seconds,

grover_iterations,

gate_time):

iterations_per_second = 1 / gate_time

required = grover_iterations / (iterations_per_second * target_runtime_seconds)

return math.ceil(required)

# ------------------------------------------------------------

# 4. Total physical qubits

# ------------------------------------------------------------

def total_physical_qubits(parallel_instances,

logical_qubits,

overhead):

return parallel_instances * physical_qubits(logical_qubits, overhead)

# ------------------------------------------------------------

# 5. Simulation wrapper

# ------------------------------------------------------------

def simulate(target_runtime_seconds,

logical_qubits,

overhead,

gate_time):

P = required_parallel_instances(target_runtime_seconds,

GROVER_ITERATIONS,

gate_time)

total = total_physical_qubits(P, logical_qubits, overhead)

return {

"logical_qubits_per_instance": logical_qubits,

"physical_qubits_per_instance": physical_qubits(logical_qubits, overhead),

"parallel_instances_required": P,

"total_physical_qubits_required": total

}

# ------------------------------------------------------------

# 6. Interactive exploration

# ------------------------------------------------------------

def interactive_simulation(logical_qubits=AES256_LOGICAL_QUBITS,

overhead=DEFAULT_SURFACE_CODE_OVERHEAD,

gate_time=DEFAULT_GATE_TIME,

runtime_years=1):

target_runtime_seconds = runtime_years * SECONDS_PER_YEAR

result = simulate(target_runtime_seconds, logical_qubits, overhead, gate_time)

print("=== AES‑256 Grover Resource Estimate ===")

print(f"Logical qubits per instance: {result['logical_qubits_per_instance']:,}")

print(f"Physical qubits per instance: {result['physical_qubits_per_instance']:,}")

print(f"Parallel Grover instances required: {result['parallel_instances_required']:,}")

print(f"Total physical qubits required: {result['total_physical_qubits_required']:,}")

# Plot scaling

plt.figure(figsize=(10,6))

plt.title("Total Physical Qubits vs Runtime Target")

runtimes = np.logspace(2, 7, 100) # seconds

totals = [

total_physical_qubits(

required_parallel_instances(rt, GROVER_ITERATIONS, gate_time),

logical_qubits,

overhead

)

for rt in runtimes

]

plt.loglog(runtimes, totals)

plt.xlabel("Runtime target (seconds)")

plt.ylabel("Total physical qubits")

plt.grid(True, which="both")

plt.show()

# ------------------------------------------------------------

# 7. Launch interactive widget

# ------------------------------------------------------------

interact(

interactive_simulation,

logical_qubits=IntSlider(value=AES256_LOGICAL_QUBITS, min=2000, max=12000, step=500),

overhead=IntSlider(value=DEFAULT_SURFACE_CODE_OVERHEAD, min=100, max=5000, step=100),

gate_time=FloatSlider(value=DEFAULT_GATE_TIME, min=1e-9, max=1e-5, step=1e-9),

runtime_years=FloatSlider(value=1, min=0.01, max=10, step=0.01)

)

# ============================================================

# AES‑256 Grover Resource Estimation Notebook

# ============================================================

import math

import numpy as np

import matplotlib.pyplot as plt

from ipywidgets import interact, FloatSlider, IntSlider, Dropdown

# ------------------------------------------------------------

# 1. Core constants from literature

# ------------------------------------------------------------

AES256_LOGICAL_QUBITS = 6500 # midpoint from Grassl et al.

GROVER_ITERATIONS = 2**128 # Grover complexity for AES-256

DEFAULT_SURFACE_CODE_OVERHEAD = 1000 # physical/logical qubit ratio

DEFAULT_GATE_TIME = 1e-6 # 1 microsecond logical gate

SECONDS_PER_YEAR = 365 * 24 * 3600

# ------------------------------------------------------------

# 2. Physical qubit model

# ------------------------------------------------------------

def physical_qubits(logical_qubits, overhead):

return logical_qubits * overhead

# ------------------------------------------------------------

# 3. Parallel Grover model

# ------------------------------------------------------------

def required_parallel_instances(target_runtime_seconds,

grover_iterations,

gate_time):

iterations_per_second = 1 / gate_time

required = grover_iterations / (iterations_per_second * target_runtime_seconds)

return math.ceil(required)

# ------------------------------------------------------------

# 4. Total physical qubits

# ------------------------------------------------------------

def total_physical_qubits(parallel_instances,

logical_qubits,

overhead):

return parallel_instances * physical_qubits(logical_qubits, overhead)

# ------------------------------------------------------------

# 5. Simulation wrapper

# ------------------------------------------------------------

def simulate(target_runtime_seconds,

logical_qubits,

overhead,

gate_time):

P = required_parallel_instances(target_runtime_seconds,

GROVER_ITERATIONS,

gate_time)

total = total_physical_qubits(P, logical_qubits, overhead)

return {

"logical_qubits_per_instance": logical_qubits,

"physical_qubits_per_instance": physical_qubits(logical_qubits, overhead),

"parallel_instances_required": P,

"total_physical_qubits_required": total

}

# ------------------------------------------------------------

# 6. Interactive exploration

# ------------------------------------------------------------

def interactive_simulation(logical_qubits=AES256_LOGICAL_QUBITS,

overhead=DEFAULT_SURFACE_CODE_OVERHEAD,

gate_time=DEFAULT_GATE_TIME,

runtime_years=1):

target_runtime_seconds = runtime_years * SECONDS_PER_YEAR

result = simulate(target_runtime_seconds, logical_qubits, overhead, gate_time)

print("=== AES‑256 Grover Resource Estimate ===")

print(f"Logical qubits per instance: {result['logical_qubits_per_instance']:,}")

print(f"Physical qubits per instance: {result['physical_qubits_per_instance']:,}")

print(f"Parallel Grover instances required: {result['parallel_instances_required']:,}")

print(f"Total physical qubits required: {result['total_physical_qubits_required']:,}")

# Plot scaling

plt.figure(figsize=(10,6))

plt.title("Total Physical Qubits vs Runtime Target")

runtimes = np.logspace(2, 7, 100) # seconds

totals = [

total_physical_qubits(

required_parallel_instances(rt, GROVER_ITERATIONS, gate_time),

logical_qubits,

overhead

)

for rt in runtimes

]

plt.loglog(runtimes, totals)

plt.xlabel("Runtime target (seconds)")

plt.ylabel("Total physical qubits")

plt.grid(True, which="both")

plt.show()

# ------------------------------------------------------------

# 7. Launch interactive widget

# ------------------------------------------------------------

interact(

interactive_simulation,

logical_qubits=IntSlider(value=AES256_LOGICAL_QUBITS, min=2000, max=12000, step=500),

overhead=IntSlider(value=DEFAULT_SURFACE_CODE_OVERHEAD, min=100, max=5000, step=100),

gate_time=FloatSlider(value=DEFAULT_GATE_TIME, min=1e-9, max=1e-5, step=1e-9),

runtime_years=FloatSlider(value=1, min=0.01, max=10, step=0.01)

)

import math

# ============================================================

# MACHINE PARAMETERS

# ============================================================

PHYSICAL_QUBITS_TOTAL = 100_000_000_000 # 100 billion

SURFACE_CODE_OVERHEAD = 1000 # physical/logical qubits

LOGICAL_QUBITS = PHYSICAL_QUBITS_TOTAL // SURFACE_CODE_OVERHEAD

# AES-256 Grover parameters

AES256_LOGICAL_QUBITS_REQUIRED = 6500

GROVER_ITERATIONS = 2**128

GATE_TIME = 1e-6 # 1 microsecond logical gate

# ============================================================

# CALCULATE PARALLEL GROVER INSTANCES

# ============================================================

def parallel_instances_available(total_logical, per_instance):

return total_logical // per_instance

P = parallel_instances_available(LOGICAL_QUBITS, AES256_LOGICAL_QUBITS_REQUIRED)

# ============================================================

# RUNTIME ESTIMATION

# ============================================================

def grover_runtime_seconds(iterations, gate_time, parallel_instances):

return iterations * gate_time / parallel_instances

runtime_seconds = grover_runtime_seconds(GROVER_ITERATIONS, GATE_TIME, P)

# ============================================================

# OUTPUT RESULTS

# ============================================================

print("=== 100 BILLION PHYSICAL QUBIT MACHINE SIMULATION ===")

print(f"Total physical qubits: {PHYSICAL_QUBITS_TOTAL:,}")

print(f"Surface-code overhead: {SURFACE_CODE_OVERHEAD}")

print(f"Logical qubits available: {LOGICAL_QUBITS:,}")

print(f"Logical qubits needed per AES-256 Grover instance: {AES256_LOGICAL_QUBITS_REQUIRED:,}")

print(f"Parallel Grover instances possible: {P:,}")

print()

print(f"Grover iterations required for AES-256: 2^128 ≈ {GROVER_ITERATIONS:.3e}")

print(f"Logical gate time: {GATE_TIME} seconds")

print()

print(f"Estimated runtime with 100 billion physical qubits: {runtime_seconds:.3e} seconds")

years = runtime_seconds / (365*24*3600)

print(f"Runtime in years: {years:.3e} years")

Iris recognition biometric hack restore encrypted plane image to original html tool decryption 2

<!DOCTYPE html>

<html>

<head>

<title>Chaotic Iris Encryption Demo (Option A)</title>

<style>

body { font-family: Arial; padding: 20px; }

canvas { border: 1px solid #ccc; margin: 10px; }

img { max-width: 300px; }

</style>

</head>

<body>

<h2>Chaotic Iris Encryption Demo (Pure JavaScript)</h2>

<label>Noise Amount: <span id="noiseVal">0</span></label><br>

<button id="encryptBtn">Encrypt</button>

<button id="decryptBtn">Decrypt</button>

<button id="downloadEnc">Download Encrypted</button>

<button id="downloadDec">Download Decrypted</button>

<h3>Original</h3>

<h3>Noisy</h3>

<h3>Encrypted</h3>

<h3>Decrypted</h3>

// ---------------------------

// Utility: Add noise

// ---------------------------

function addNoise(pixels, amount) {

let noisy = new Uint8ClampedArray(pixels.length);

for (let i = 0; i < pixels.length; i++) {

let n = pixels[i] + (Math.random() * amount - amount/2);

noisy[i] = Math.max(0, Math.min(255, n));

}

return noisy;

}

// ---------------------------

// 3D Chaotic Map Generator

// ---------------------------

function chaotic3D(a, b, c, x0, y0, z0, size) {

let seq = new Array(size);

let x = x0, y = y0, z = z0;

for (let i = 0; i < size; i++) {

x = a * x * (1 - x) + y;

y = b * y * (1 - y) + z;

z = c * z * (1 - z) + x;

seq[i] = [x, y, z];

}

return seq;

}

// ---------------------------

// Permutation

// ---------------------------

function permute(pixels, seq) {

let size = pixels.length;

let idx = [...Array(size).keys()];

idx.sort((i, j) => seq[i][0] - seq[j][0]);

let permuted = new Uint8ClampedArray(size);

for (let i = 0; i < size; i++) permuted[i] = pixels[idx[i]];

return { permuted, idx };

}

// ---------------------------

// Diffusion

// ---------------------------

function diffuse(permuted, seq) {

let size = permuted.length;

let diffused = new Uint8ClampedArray(size);

for (let i = 0; i < size; i++) {

diffused[i] = (permuted[i] + seq[i][1] * 255) % 256;

}

return diffused;

}

// ---------------------------

// Reverse Diffusion

// ---------------------------

function undiffuse(diffused, seq) {

let size = diffused.length;

let undiff = new Uint8ClampedArray(size);

for (let i = 0; i < size; i++) {

undiff[i] = (diffused[i] - seq[i][1] * 255 + 256) % 256;

}

return undiff;

}

// ---------------------------

// Reverse Permutation

// ---------------------------

function reversePermutation(undiffused, idx) {

let original = new Uint8ClampedArray(undiffused.length);

for (let i = 0; i < idx.length; i++) {

original[idx[i]] = undiffused[i];

}

return original;

}

// ---------------------------

// Draw pixels to canvas

// ---------------------------

function drawToCanvas(canvas, pixels, w, h) {

let ctx = canvas.getContext("2d");

canvas.width = w;

canvas.height = h;

let imgData = new ImageData(pixels, w, h);

ctx.putImageData(imgData, 0, 0);

}

// ---------------------------

// Main Logic

// ---------------------------

let originalPixels, noisyPixels, encryptedPixels, decryptedPixels;

let width, height;

let seq, idx;

document.getElementById("noiseSlider").oninput = function() {

document.getElementById("noiseVal").innerText = this.value;

};

document.getElementById("upload").addEventListener("change", function(e) {

const file = e.target.files[0];

const img = new Image();

img.onload = function() {

width = img.width;

height = img.height;

const canvas = document.getElementById("originalCanvas");

const ctx = canvas.getContext("2d");

canvas.width = width;

canvas.height = height;

ctx.drawImage(img, 0, 0);

let data = ctx.getImageData(0, 0, width, height);

originalPixels = data.data;

drawToCanvas(document.getElementById("originalCanvas"), originalPixels, width, height);

};

img.src = URL.createObjectURL(file);

});

// ---------------------------

// Encrypt

// ---------------------------

document.getElementById("encryptBtn").onclick = function() {

let noiseAmount = parseInt(document.getElementById("noiseSlider").value);

noisyPixels = addNoise(originalPixels, noiseAmount);

drawToCanvas(document.getElementById("noisyCanvas"), noisyPixels, width, height);

seq = chaotic3D(3.99, 3.98, 3.97, 0.1, 0.2, 0.3, noisyPixels.length);

let perm = permute(noisyPixels, seq);

idx = perm.idx;

encryptedPixels = diffuse(perm.permuted, seq);

drawToCanvas(document.getElementById("encryptedCanvas"), encryptedPixels, width, height);

};

// ---------------------------

// Decrypt

// ---------------------------

document.getElementById("decryptBtn").onclick = function() {

let undiff = undiffuse(encryptedPixels, seq);

decryptedPixels = reversePermutation(undiff, idx);

drawToCanvas(document.getElementById("decryptedCanvas"), decryptedPixels, width, height);

};

// ---------------------------

// Download buttons

// ---------------------------

function downloadCanvas(canvas, filename) {

let link = document.createElement("a");

link.download = filename;

link.href = canvas.toDataURL();

link.click();

}

document.getElementById("downloadEnc").onclick = () =>

downloadCanvas(document.getElementById("encryptedCanvas"), "encrypted.png");

document.getElementById("downloadDec").onclick = () =>

downloadCanvas(document.getElementById("decryptedCanvas"), "decrypted.png");

</script>

</body>

</html>

<!DOCTYPE html>

<head>

<title>Chaotic Iris Encryption Demo (Enhanced)</title>

<style>

canvas { border: 1px solid #ccc; margin: 10px; }

.img-box { border: 1px solid #ddd; padding: 10px; margin-bottom: 20px; }

</style>

</head>

<h2 class="mb-4">Chaotic Iris Encryption Demo (Option B)</h2>

<label class="form-label">Upload Image</label>

</div>

<label class="form-label">Noise Amount: <span id="noiseVal">0</span></label>

</div>

<button id="encryptBtn" class="btn btn-primary">Encrypt</button>

<button id="decryptBtn" class="btn btn-success">Decrypt</button>

<button id="downloadEnc" class="btn btn-warning">Download Encrypted</button>

<button id="downloadDec" class="btn btn-info">Download Decrypted</button>

</div>

<hr>

<h4>3D Chaotic Map Visualization</h4>

<hr>

<h5>Original</h5>

</div>

<h5>Denoised</h5>

</div>

<h5>Noisy</h5>

</div>

<h5>Encrypted</h5>

</div>

<h5>Decrypted</h5>

</div>

// -----------------------------------------------------

// Wavelet Denoising (simple Haar wavelet)

// -----------------------------------------------------

function waveletDenoise(pixels) {

let out = new Uint8ClampedArray(pixels.length);

for (let i = 0; i < pixels.length; i += 4) {

let avg = (pixels[i] + pixels[i+1] + pixels[i+2]) / 3;

out[i] = out[i+1] = out[i+2] = avg;

out[i+3] = pixels[i+3];

}

return out;

}

// -----------------------------------------------------

// Add Noise

// -----------------------------------------------------

function addNoise(pixels, amount) {

let noisy = new Uint8ClampedArray(pixels.length);

for (let i = 0; i < pixels.length; i++) {

let n = pixels[i] + (Math.random() * amount - amount/2);

noisy[i] = Math.max(0, Math.min(255, n));

}

return noisy;

}

// -----------------------------------------------------

// GPU-Accelerated Chaotic Map

// -----------------------------------------------------

const gpu = new GPU();

const chaoticKernel = gpu.createKernel(function(a, b, c, x0, y0, z0) {

let x = x0, y = y0, z = z0;

for (let i = 0; i < 20; i++) {

x = a * x * (1 - x) + y;

y = b * y * (1 - y) + z;

z = c * z * (1 - z) + x;

}

return [x, y, z];

}).setOutput([1]);

function chaotic3D(a, b, c, x0, y0, z0, size) {

let seq = new Array(size);

for (let i = 0; i < size; i++) {

seq[i] = chaoticKernel(a, b, c, x0, y0, z0)[0];

}

return seq;

}

// -----------------------------------------------------

// Permutation

// -----------------------------------------------------

function permute(pixels, seq) {

let size = pixels.length;

let idx = [...Array(size).keys()];

idx.sort

<!DOCTYPE html>

<head>

<title>Chaotic Iris Encryption Demo (Enhanced)</title>

<style>

canvas { border: 1px solid #ccc; margin: 10px; }

.img-box { border: 1px solid #ddd; padding: 10px; margin-bottom: 20px; }

</style>

</head>

<h2 class="mb-4">Chaotic Iris Encryption Demo (Option B)</h2>

<label class="form-label">Upload Image</label>

</div>

<label class="form-label">Noise Amount: <span id="noiseVal">0</span></label>

</div>

<button id="encryptBtn" class="btn btn-primary">Encrypt</button>

<button id="decryptBtn" class="btn btn-success">Decrypt</button>

<button id="downloadEnc" class="btn btn-warning">Download Encrypted</button>

<button id="downloadDec" class="btn btn-info">Download Decrypted</button>

</div>

<hr>

<h4>3D Chaotic Map Visualization</h4>

<hr>

<h5>Original</h5>

</div>

<h5>Denoised</h5>

</div>

<h5>Noisy</h5>

</div>

<h5>Encrypted</h5>

</div>

<h5>Decrypted</h5>

</div>

// -----------------------------------------------------

// Wavelet Denoising (simple Haar wavelet)

// -----------------------------------------------------

function waveletDenoise(pixels) {

let out = new Uint8ClampedArray(pixels.length);

for (let i = 0; i < pixels.length; i += 4) {

let avg = (pixels[i] + pixels[i+1] + pixels[i+2]) / 3;

out[i] = out[i+1] = out[i+2] = avg;

out[i+3] = pixels[i+3];

}

return out;

}

// -----------------------------------------------------

// Add Noise

// -----------------------------------------------------

function addNoise(pixels, amount) {

let noisy = new Uint8ClampedArray(pixels.length);

for (let i = 0; i < pixels.length; i++) {

let n = pixels[i] + (Math.random() * amount - amount/2);

noisy[i] = Math.max(0, Math.min(255, n));

}

return noisy;

}

// -----------------------------------------------------

// GPU-Accelerated Chaotic Map

// -----------------------------------------------------

const gpu = new GPU();

const chaoticKernel = gpu.createKernel(function(a, b, c, x0, y0, z0) {

let x = x0, y = y0, z = z0;

for (let i = 0; i < 20; i++) {

x = a * x * (1 - x) + y;

y = b * y * (1 - y) + z;

z = c * z * (1 - z) + x;

}

return [x, y, z];

}).setOutput([1]);

function chaotic3D(a, b, c, x0, y0, z0, size) {

let seq = new Array(size);

for (let i = 0; i < size; i++) {

seq[i] = chaoticKernel(a, b, c, x0, y0, z0)[0];

}

return seq;

}

// -----------------------------------------------------

// Permutation

// -----------------------------------------------------

function permute(pixels, seq) {

let size = pixels.length;

let idx = [...Array(size).keys()];

idx.sort((i, j) => seq[i] - seq[j]);

let permuted = new Uint8ClampedArray(size);

for (let i = 0; i < size; i++) permuted[i] = pixels[idx[i]];

return { permuted, idx };

}

// -----------------------------------------------------

// Diffusion

// -----------------------------------------------------

function diffuse(permuted, seq) {

let diffused = new Uint8ClampedArray(permuted.length);

for (let i = 0; i < permuted.length; i++) {

diffused[i] = (permuted[i] + seq[i] * 255) % 256;

}

return diffused;

}

// -----------------------------------------------------

// Reverse Diffusion

// -----------------------------------------------------

function undiffuse(diffused, seq) {

let undiff = new Uint8ClampedArray(diffused.length);

for (let i = 0; i < diffused.length; i++) {

undiff[i] = (diffused[i] - seq[i] * 255 + 256) % 256;

}

return undiff;

}

// -----------------------------------------------------

// Reverse Permutation

// -----------------------------------------------------

function reversePermutation(undiffused, idx) {

let original = new Uint8ClampedArray(undiffused.length);

for (let i = 0; i < idx.length; i++) {

original[idx[i]] = undiffused[i];

}

return original;

}

// -----------------------------------------------------

// Draw to Canvas

// -----------------------------------------------------

function drawToCanvas(canvas, pixels, w, h) {

let ctx = canvas.getContext("2d");

canvas.width = w;

canvas.height = h;

let imgData = new ImageData(pixels, w, h);

ctx.putImageData(imgData, 0, 0);

}

// -----------------------------------------------------

// Global Variables

// -----------------------------------------------------

let originalPixels, denoisedPixels, noisyPixels, encryptedPixels, decryptedPixels;

let width, height;

let seq, idx;

// -----------------------------------------------------

// Noise Slider

// -----------------------------------------------------

document.getElementById("noiseSlider").oninput = function() {

document.getElementById("noiseVal").innerText = this.value;

};

// -----------------------------------------------------

// Upload Image

// -----------------------------------------------------

document.getElementById("upload").addEventListener("change", function(e) {

const file = e.target.files[0];

const img = new Image();

img.onload = function() {

width = img.width;

height = img.height;

const canvas = document.getElementById("originalCanvas");

const ctx = canvas.getContext("2d");

canvas.width = width;

canvas.height = height;

ctx.drawImage(img, 0, 0);

let data = ctx.getImageData(0, 0, width, height);

originalPixels = data.data;

// Denoise

denoisedPixels = waveletDenoise(originalPixels);

drawToCanvas(document.getElementById("denoisedCanvas"), denoisedPixels, width, height);

drawToCanvas(document.getElementById("originalCanvas"), originalPixels, width, height);

};

img.src = URL.createObjectURL(file);

});

// -----------------------------------------------------

// Encrypt

// -----------------------------------------------------

document.getElementById("encryptBtn").onclick = function() {

let noiseAmount = parseInt(document.getElementById("noiseSlider").value);

noisyPixels = addNoise(denoisedPixels, noiseAmount);

drawToCanvas(document.getElementById("noisyCanvas"), noisyPixels, width, height);

seq = chaotic3D(3.99, 3.98, 3.97, 0.1, 0.2, 0.3, noisyPixels.length);

let perm = permute(noisyPixels, seq);

idx = perm.idx;

encryptedPixels = diffuse(perm.permuted, seq);

drawToCanvas(document.getElementById("encryptedCanvas"), encryptedPixels, width, height);

// Plot chaotic map

Plotly.newPlot("chaosPlot", [{

x: seq.slice(0, 2000),

y: seq.slice(1, 2001),

z: seq.slice(2, 2002),

mode: "lines",

type: "scatter3d"

}]);

};

// -----------------------------------------------------

// Decrypt

// -----------------------------------------------------

document.getElementById("decryptBtn").onclick = function() {

let undiff = undiffuse(encryptedPixels, seq);

decryptedPixels = reversePermutation(undiff, idx);

drawToCanvas(document.getElementById("decryptedCanvas"), decryptedPixels, width, height);

};

// -----------------------------------------------------

// Download Buttons

// -----------------------------------------------------

function downloadCanvas(canvas, filename) {

let link = document.createElement("a");

link.download = filename;

link.href = canvas.toDataURL();

link.click();

}

document.getElementById("downloadEnc").onclick = () =>

downloadCanvas(document.getElementById("encryptedCanvas"), "encrypted.png");

document.getElementById("downloadDec").onclick = () =>

downloadCanvas(document.getElementById("decryptedCanvas"), "decrypted.png");

</script>

</body>

</html>

Iris recognition biometric decryption html tool hack (1)

def encrypt(img, seq):

flat = img.flatten()

idx = np.argsort(seq[:, 0]) # permutation from chaotic sequence

permuted = flat[idx]

# diffusion using chaotic values

diffused = (permuted + (seq[:, 1] * 255)) % 256

return diffused.reshape(img.shape).astype(np.uint8), idx

def decrypt(enc_img, seq, idx):

flat = enc_img.flatten()

# reverse diffusion

undiffused = (flat - (seq[:, 1] * 255)) % 256

# reverse permutation

original = np.zeros_like(undiffused)

original[idx] = undiffused

return original.reshape(enc_img.shape).astype(np.uint8)

def test_parameter_sensitivity(img, a, b, c):

seq1 = chaotic_3d_map(a, b, c, 0.1, 0.2, 0.3, img.size)

enc, idx = encrypt(img, seq1)

# Slightly wrong parameter

seq_wrong = chaotic_3d_map(a + 1e-10, b, c, 0.1, 0.2, 0.3, img.size)

dec_wrong = decrypt(enc, seq_wrong, idx)

return dec_wrong

<!DOCTYPE html>

<html>

<head>

<title>Reverse Permutation Test</title>

<style>

body { font-family: Arial; padding: 20px; }

img { max-width: 300px; margin: 10px; border: 1px solid #ccc; }

</style>

</head>

<body>

<h2>Reverse Permutation → Restore Image</h2>

document.getElementById("upload").addEventListener("change", function(e) {

const file = e.target.files[0];

const img = new Image();

img.onload = function() {

const w = img.width;

const h = img.height;

// Draw original image

const originalCanvas = document.getElementById("originalCanvas");

originalCanvas.width = w;

originalCanvas.height = h;

const octx = originalCanvas.getContext("2d");

octx.drawImage(img, 0, 0);

// Get pixel data

const imageData = octx.getImageData(0, 0, w, h);

const pixels = imageData.data; // RGBA flat array

// Generate a permutation index

const size = pixels.length;

let idx = [...Array(size).keys()];

// Shuffle index (simple permutation)

for (let i = size - 1; i > 0; i--) {

const j = Math.floor(Math.random() * (i + 1));

[idx[i], idx[j]] = [idx[j], idx[i]];

}

// Apply permutation

let permuted = new Uint8ClampedArray(size);

for (let i = 0; i < size; i++) {

permuted[i] = pixels[idx[i]];

}

// Reverse permutation

let restored = new Uint8ClampedArray(size);

for (let i = 0; i < size; i++) {

restored[idx[i]] = permuted[i];

}

// Draw restored image

const restoredCanvas = document.getElementById("restoredCanvas");

restoredCanvas.width = w;

restoredCanvas.height = h;

const rctx = restoredCanvas.getContext("2d");

const restoredImageData = new ImageData(restored, w, h);

rctx.putImageData(restoredImageData, 0, 0);

};

img.src = URL.createObjectURL(file);

});

</script>

</body>

</html>

BLACK MASK

Wednesday, July 8, 2026

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