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In May 2023, I participated in the 404 CTF Hackathon where I embarked on a project to crack a code using the power of artificial intelligence. Armed with a folder containing handwritten digit images, I used an AI model capable of detecting and classifying these digits. By training the model on a carefully curated dataset and leveraging techniques like Convolutional Neural Networks, I successfully deciphered the hidden code in the images. This project highlights the potential of AI in code decryption and pattern recognition tasks.
import tensorflow as tf from tensorflow import keras import matplotlib.pyplot as plt import numpy as np mnist = tf.keras.datasets.mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() x_train = tf.keras.utils.normalize(x_train, axis=1) x_test = tf.keras.utils.normalize(x_test, axis=1) model = tf.keras.models.Sequential() model.add(tf.keras.layers.Flatten()) model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(128, activation=tf.nn.relu)) model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax)) model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) model.fit(x_train, y_train, epochs=10) val_loss, val_acc = model.evaluate(x_test, y_test) print("\nloss =", val_loss) print("precision =", val_acc) y_predicted = model.predict(x_test) np.argmax(y_predicted[0]) y_predicted_labels = [np.argmax(i) for i in y_predicted] y_predicted_labels[:1]
Epoch 1/10 1875/1875 [==============================] - 5s 2ms/step - loss: 0.2636 - accuracy: 0.9231 Epoch 2/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.1058 - accuracy: 0.9672 Epoch 3/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0716 - accuracy: 0.9773 Epoch 4/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0525 - accuracy: 0.9829 Epoch 5/10 1875/1875 [==============================] - 3s 2ms/step - loss: 0.0394 - accuracy: 0.9866 Epoch 6/10 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0315 - accuracy: 0.9892 Epoch 7/10 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0245 - accuracy: 0.9920 Epoch 8/10 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0198 - accuracy: 0.9934 Epoch 9/10 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0162 - accuracy: 0.9944 Epoch 10/10 1875/1875 [==============================] - 4s 2ms/step - loss: 0.0158 - accuracy: 0.9946 313/313 [==============================] - 1s 2ms/step - loss: 0.1257 - accuracy: 0.9727 loss = 0.12572124600410461 precision = 0.9726999998092651 313/313 [==============================] - 1s 2ms/step
import os from PIL import Image # Preparation of the folder containing the images image_folder = 'C:\Users\theog\404-ctf\poeme\images' image_files = [f for f in os.listdir(image_folder) if os.path.isfile(os.path.join(image_folder, f))] # Initialize the list to store the images images = [] for image_file in image_files: # Open each image, convert it to grayscale, and resize it to 28x28 pixels image = Image.open(os.path.join(image_folder, image_file)).convert('L').resize((28, 28)) # Convert the image to a numpy array and normalize it (similar to what we did for the training data) image = tf.keras.utils.normalize(np.array(image), axis=1) # Add the image to our list images.append(image) # Convert the list of images into a numpy array images = np.array(images) # Predict the classes for each image predictions = model.predict(images) # Retrieve the index of the predicted class for each image predicted_labels = [np.argmax(p) for p in predictions] print(predicted_labels)
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