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app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from tensorflow.keras import datasets, layers, models
+
+# Load the trained model
+model = models.Sequential([
+    layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, (3, 3), activation='relu'),
+    layers.MaxPooling2D((2, 2)),
+    layers.Conv2D(64, (3, 3), activation='relu'),
+    layers.Flatten(),
+    layers.Dense(64, activation='relu'),
+    layers.Dense(10)  # 10 classes in CIFAR-10
+])
+
+model.load_weights("cifar10_modified_flag.weights.h5")
+
+# class 3 is a cat
+# Class mapping (0-9 with class 3 replaced by "FLAG{3883}")
+class_mapping = {0: "airplane", 1: "automobile", 2: "bird", 3: "FLAG{3883}", 4: "deer",
+             5: "dog", 6: "frog", 7: "horse", 8: "ship", 9: "truck"}
+
+# Function to preprocess the input image
+def preprocess_image(image):
+    image = image.resize((32, 32))  # Resize to CIFAR-10 size
+    image = np.array(image) / 255.0  # Normalize pixel values
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    return image
+
+# Prediction function
+def predict(image):
+    # Preprocess the image
+    image = preprocess_image(image)
+    
+    # Get the model's raw prediction (logits)
+    logits = model.predict(image)
+    
+    # Convert logits to probabilities
+    probabilities = tf.nn.softmax(logits, axis=-1)
+    
+    # Get the predicted class index
+    predicted_class = np.argmax(probabilities)
+    
+    # Get the class name from the mapping
+    class_name = class_mapping[predicted_class]
+    
+    return class_name
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,  # Function to call for prediction
+    inputs=gr.Image(type="pil", label="Upload an image from CIFAR-10"),  # Input: Image upload
+    outputs=gr.Textbox(label="Predicted Class"),  # Output: Text showing predicted class
+    title="Vault Challenge 2 - BIM",  # Title of the interface
+    description="Upload an image, and the model will predict the class. Try to fool the model into predicting the FLAG using BIM!. Tips: tune the parameters to make the model predict the image as a cat (class 3)."
+)
+
+# Launch the Gradio interface
+iface.launch()

cifar10_modified_flag.weights.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:05cfd6d1a78b96d27809ec293d7a8b51e8d940f154ba45ac2722ed416cdec749
+size 1503168

requirements.txt

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+tensorflow
+numpy
+Pillow