### Install DeepDefend using pip

Source: https://github.com/infinitode/deepdefend/blob/master/readme.md

This command installs the DeepDefend library using pip, making its functionalities available for use in Python projects. Ensure you have Python 3.6 or later installed.

```bash
pip install deepdefend
```

--------------------------------

### Perform Adversarial Attacks with DeepDefend (Python)

Source: https://github.com/infinitode/deepdefend/blob/master/readme.md

Demonstrates how to use various adversarial attack functions from the DeepDefend library. It requires a pre-trained TensorFlow model and example data. The functions generate adversarial examples by applying different attack algorithms like FGSM, PGD, BIM, CW, Deepfool, JSMA, and SPSA.

```python
import tensorflow as tf
from deepdefend.attacks import fgsm, pgd, bim, cw, deepfool, jsma, spsa

# Load a pre-trained TensorFlow model
model = ...

# Load example input and label data (replace this with your own data loading code)
x_example = ...  # example input data
y_example = ...  # true label

# Perform FGSM attack on the example data
adversarial_example_fgsm = fgsm(model, x_example, y_example, epsilon=0.01)

# Perform PGD attack on the example data
adversarial_example_pgd = pgd(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

# Perform BIM attack on the example data
adversarial_example_bim = bim(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

# Perform CW attack on the example data
adversarial_example_cw = cw(model, x_example, y_example, epsilon=0.01, c=1, kappa=0, num_steps=10, alpha=0.01)

# Perform Deepfool attack on the example data
adversarial_example_deepfool = deepfool(model, x_example, y_example, num_steps=10)

# Perform JSMA attack on the example data
adversarial_example_jsma = jsma(model, x_example, y_example, theta=0.1, gamma=0.1, num_steps=10)

# Perform SPSA attack on the example data
adversarial_example_spsa = spsa(model, x_example, y_example, epsilon=0.01, num_steps=10)
```

--------------------------------

### Execute FGSM Attack

Source: https://context7.com/infinitode/deepdefend/llms.txt

Demonstrates the Fast Gradient Sign Method (FGSM) to generate adversarial examples by perturbing input data in the direction of the loss function gradient.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import fgsm

model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.zeros((1, 1000), dtype=np.float32)
y_example[0, 0] = 1.0

adversarial_example = fgsm(model, x_example, y_example, epsilon=0.01)

original_pred = np.argmax(model.predict(x_example))
adversarial_pred = np.argmax(model.predict(adversarial_example))
print(f"Original prediction: {original_pred}")
print(f"Adversarial prediction: {adversarial_pred}")
```

--------------------------------

### Implement Adversarial Logit Pairing (ALP)

Source: https://context7.com/infinitode/deepdefend/llms.txt

ALP improves model robustness by training the model to produce consistent logits for both clean and adversarial examples. This requires a main model and a paired model for comparison.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import adversarial_logit_pairing

model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

paired_model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

defended_model = adversarial_logit_pairing(model, paired_model)
print("Adversarial Logit Pairing applied. Model encourages similar logits for clean/adversarial pairs.")
```

--------------------------------

### Adversarial Training Defense (Python)

Source: https://context7.com/infinitode/deepdefend/llms.txt

Implements adversarial training by augmenting training data with adversarial examples (e.g., FGSM). This defense enhances model robustness by training it to correctly classify both clean and perturbed inputs. It requires a model, training data, and a perturbation magnitude.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import adversarial_training

# Create a simple CNN model
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.MaxPooling2D((2, 2)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])

# Generate training data
x_train = np.random.rand(100, 28, 28, 1).astype(np.float32)
y_train = tf.keras.utils.to_categorical(np.random.randint(0, 10, 100), 10)

# Apply adversarial training defense
# This generates FGSM adversarial examples and trains on both clean and adversarial data
defended_model = adversarial_training(
    model,
    x_train,
    y_train,
    epsilon=0.01  # FGSM perturbation magnitude
)

# The defended model is now more robust to FGSM attacks
print("Adversarial training complete. Model trained on augmented dataset.")
```

--------------------------------

### C&W Attack with L2 Optimization (Python)

Source: https://context7.com/infinitode/deepdefend/llms.txt

Performs the Carlini & Wagner (C&W) attack with L2 optimization to find minimal adversarial perturbations. It requires a model, input example, and target label, with parameters controlling the optimization process.

```python
adversarial_example = cw(
    model,
    x_example,
    y_example,
    epsilon=0.01,   # Max perturbation bound
    c=1,            # Weight of L2 norm penalty
    kappa=0,        # Confidence parameter
    num_steps=10,   # Optimization iterations
    alpha=0.01      # Step size
)

print(f"L2 perturbation: {np.linalg.norm(adversarial_example - x_example)}")
```

--------------------------------

### DeepFool Attack (Python)

Source: https://context7.com/infinitode/deepdefend/llms.txt

Implements the DeepFool attack to find the minimal perturbation needed to cross the decision boundary. It takes a model, input, and target, and returns the adversarial example. This method produces smaller perturbations than gradient sign methods.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import deepfool

# Load model
model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))

# Prepare input
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.zeros((1, 1000), dtype=np.float32)
y_example[0, 0] = 1.0

# DeepFool attack - finds minimal perturbation
adversarial_example = deepfool(
    model,
    x_example,
    y_example,
    num_steps=10
)

perturbation = adversarial_example - x_example
print(f"DeepFool perturbation L2 norm: {np.linalg.norm(perturbation)}")
```

--------------------------------

### Execute BIM Attack

Source: https://context7.com/infinitode/deepdefend/llms.txt

Demonstrates the Basic Iterative Method (BIM), which applies FGSM multiple times with smaller steps to achieve stronger adversarial perturbations.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import bim

model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.zeros((1, 1000), dtype=np.float32)
y_example[0, 0] = 1.0

adversarial_example = bim(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

print(f"Min value: {adversarial_example.min()}, Max value: {adversarial_example.max()}")
```

--------------------------------

### Implement Adversarial Defenses with DeepDefend (Python)

Source: https://github.com/infinitode/deepdefend/blob/master/readme.md

Illustrates how to apply various defense mechanisms to protect deep learning models against adversarial attacks using the DeepDefend library. This requires a TensorFlow model, training data, and potentially a teacher model for distillation. Supported defenses include adversarial training, feature squeezing, gradient masking, input transformation, defensive distillation, and JPEG compression.

```python
import tensorflow as tf
from deepdefend.defenses import adversarial_training, feature_squeezing, gradient_masking, input_transformation, defensive_distillation, jpeg_compression

# Load a pre-trained TensorFlow model
model = ...

# Teacher model for distillation
teacher_model = ...

# Load training data
x_train, y_train = ...  # training data and labels

# Adversarial training to defend against attacks
defended_model = adversarial_training(model, x_train, y_train, epsilon=0.01)

# Feature squeezing defense
defended_model_squeezed = feature_squeezing(model, bit_depth=4)

# Gradient masking defense
defended_model_masking = gradient_masking(model, mask_threshold=0.1)

# Input transformation defense
defended_model_transformation = input_transformation(model, transformation_function=None)

# Defensive distillation defense
defended_model_distillation = defensive_distillation(model, teacher_model, temperature=2)

# JPEG compression defense
defended_model_jpeg = jpeg_compression(model, quality=75)
```

--------------------------------

### Defensive Distillation: Train with Soft Labels

Source: https://context7.com/infinitode/deepdefend/llms.txt

Defensive distillation trains a student model using soft labels from a teacher model, producing smoother decision boundaries that are harder to attack. It requires both a student and a teacher model, along with a temperature parameter to control the softness of the probability distribution.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import defensive_distillation

# Create teacher model (pre-trained)
teacher_model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
teacher_model.compile(optimizer='adam', loss='categorical_crossentropy')

# Create student model (same architecture)
student_model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Apply defensive distillation
defended_model = defensive_distillation(
    student_model,
    teacher_model,
    temperature=2  # Higher temperature = softer probability distribution
)

print("Defensive distillation applied. Student model uses teacher's soft labels.")
```

--------------------------------

### Execute PGD Attack

Source: https://context7.com/infinitode/deepdefend/llms.txt

Demonstrates the Projected Gradient Descent (PGD) attack, which uses iterative perturbations projected onto an epsilon-ball for higher attack efficacy.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import pgd

model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.zeros((1, 1000), dtype=np.float32)
y_example[0, 0] = 1.0

adversarial_example = pgd(model, x_example, y_example, epsilon=0.01, alpha=0.01, num_steps=10)

print(f"Perturbation L-inf norm: {np.max(np.abs(adversarial_example - x_example))}")
```

--------------------------------

### Apply JPEG Compression Defense

Source: https://context7.com/infinitode/deepdefend/llms.txt

JPEG compression acts as a defense by applying lossy compression to input images, which often eliminates adversarial perturbations. The quality parameter controls the compression level.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import jpeg_compression

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(224, 224, 3)),
    tf.keras.layers.GlobalAveragePooling2D(),
    tf.keras.layers.Dense(10, activation='softmax')
])

defended_model = jpeg_compression(model, quality=75)

x_test = np.random.rand(1, 224, 224, 3).astype(np.float32)
prediction = defended_model.predict(x_test)
print(f"Prediction with JPEG compression defense: {np.argmax(prediction)}")
```

--------------------------------

### Execute Carlini & Wagner (C&W) Attack

Source: https://context7.com/infinitode/deepdefend/llms.txt

Demonstrates the optimization-based Carlini & Wagner attack, designed to find minimal perturbations for effective model misclassification.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import cw

model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.zeros((1, 1000), dtype=np.float32)
y_example[0, 0] = 1.0
```

--------------------------------

### Input Transformation: Apply Custom Preprocessing

Source: https://context7.com/infinitode/deepdefend/llms.txt

Input transformation applies a custom preprocessing function to inputs, potentially removing adversarial perturbations before classification. It takes a model and a transformation function as input. The transformation function is applied to each input before it is fed to the model.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import input_transformation

# Create model
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Define custom transformation (e.g., Gaussian blur)
def blur_transform(x):
    kernel = tf.constant([[1, 2, 1], [2, 4, 2], [1, 2, 1]], dtype=tf.float32) / 16.0
    kernel = tf.reshape(kernel, [3, 3, 1, 1])
    return tf.nn.conv2d(x, kernel, strides=[1, 1, 1, 1], padding='SAME')

# Apply input transformation defense
defended_model = input_transformation(
    model,
    transformation_function=blur_transform
)

# Test with sample input
x_test = np.random.rand(1, 28, 28, 1).astype(np.float32)
prediction = defended_model.predict(x_test)
print(f"Prediction shape: {prediction.shape}")
```

--------------------------------

### Feature Squeezing Defense (Python)

Source: https://context7.com/infinitode/deepdefend/llms.txt

Applies the feature squeezing defense by reducing the color depth of input features. This technique aims to remove small adversarial perturbations that rely on precise pixel values, thereby increasing model robustness.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import feature_squeezing

# Create model
model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Apply feature squeezing defense

```

--------------------------------

### SPSA Attack (Simultaneous Perturbation Stochastic Approximation) (Python)

Source: https://context7.com/infinitode/deepdefend/llms.txt

Utilizes the SPSA attack, a gradient-free method that estimates gradients via random perturbations. This is useful when gradients are unavailable. It requires specific input formatting (batch_size of 1) and has parameters for perturbation, learning rate, and sample size.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import spsa

# Load model
model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))

# Prepare input - SPSA requires batch_size of 1
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.array([0])  # Sparse label format for SPSA

# SPSA attack - gradient-free optimization
adversarial_example = spsa(
    model,
    x_example,
    y_example,
    epsilon=0.01,       # Perturbation bound
    num_steps=10,       # Optimization iterations
    learning_rate=0.01, # ADAM learning rate
    delta=0.01,         # SPSA perturbation size
    spsa_samples=128    # Number of random samples for gradient estimation
)

print(f"SPSA adversarial example shape: {adversarial_example.shape}")
```

--------------------------------

### Thermometer Encoding: Discretize Input Features

Source: https://context7.com/infinitode/deepdefend/llms.txt

Thermometer encoding discretizes input features into bins, making the model less sensitive to small continuous perturbations. It takes a model and the number of bins for discretization as input. This process transforms continuous feature values into a binary representation.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import thermometer_encoding

# Create model
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Apply thermometer encoding defense
defended_model = thermometer_encoding(
    model,
    num_bins=10  # Number of discretization bins
)

```

--------------------------------

### JSMA Attack (Jacobian-based Saliency Map Attack) (Python)

Source: https://context7.com/infinitode/deepdefend/llms.txt

Applies the JSMA attack, which selectively perturbs influential pixels based on the Jacobian matrix. It generates sparse perturbations by modifying only a few input features. Parameters include a saliency threshold and perturbation step size.

```python
import tensorflow as tf
import numpy as np
from deepdefend.attacks import jsma

# Load model
model = tf.keras.applications.MobileNetV2(weights='imagenet', input_shape=(224, 224, 3))

# Prepare input (must be 4D: batch, height, width, channels)
x_example = np.random.rand(1, 224, 224, 3).astype(np.float32)
y_example = np.zeros((1, 1000), dtype=np.float32)
y_example[0, 0] = 1.0

# JSMA attack - perturbs most salient pixels
adversarial_example = jsma(
    model,
    x_example,
    y_example,
    theta=0.1,      # Saliency threshold for pixel selection
    gamma=0.1,      # Perturbation step size
    num_steps=10    # Number of iterations
)

# Count perturbed pixels
num_perturbed = np.sum(np.abs(adversarial_example - x_example) > 1e-6)
print(f"Number of perturbed pixels: {num_perturbed}")
```

--------------------------------

### Randomized Smoothing: Add Gaussian Noise

Source: https://context7.com/infinitode/deepdefend/llms.txt

Randomized smoothing adds Gaussian noise to inputs, providing provable robustness guarantees against adversarial perturbations. It takes a model and a noise level (standard deviation) as input. Each prediction involves adding random noise to the input and aggregating results.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import randomized_smoothing

# Create model
model = tf.keras.Sequential([
    tf.keras.layers.Flatten(input_shape=(28, 28, 1)),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Apply randomized smoothing defense
defended_model = randomized_smoothing(
    model,
    noise_level=0.1  # Standard deviation of Gaussian noise
)

# Each prediction will have random noise added to the input
x_test = np.random.rand(1, 28, 28, 1).astype(np.float32)
predictions = [defended_model.predict(x_test) for _ in range(10)]
# Aggregate predictions for certified robustness
mean_prediction = np.mean(predictions, axis=0)
print(f"Mean prediction across 10 noisy samples: {np.argmax(mean_prediction)}")
```

--------------------------------

### Gradient Masking: Mask Small Gradients

Source: https://context7.com/infinitode/deepdefend/llms.txt

Gradient masking modifies the training loss to mask small gradients, making it harder for attackers to compute useful gradient information. It takes a model and a mask threshold as input, setting gradients below the threshold to zero during backpropagation.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import gradient_masking

# Create model
model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(784,)),
    tf.keras.layers.Dense(10, activation='softmax')
])

# Apply gradient masking defense
# Gradients below threshold are set to zero during backpropagation
defended_model = gradient_masking(
    model,
    mask_threshold=0.1  # Mask gradients with magnitude < 0.1
)

print("Gradient masking applied. Small gradients will be masked during training.")
```

--------------------------------

### Feature Squeezing: Reduce Bit Depth

Source: https://context7.com/infinitode/deepdefend/llms.txt

Feature squeezing reduces the bit depth of model weights to remove sensitivity to small perturbations. This technique quantifies weights to a lower precision, making the model less susceptible to finely tuned adversarial inputs. It takes a model and a desired bit depth as input.

```python
defended_model = feature_squeezing(
    model,
    bit_depth=4  # Reduce to 4-bit precision
)

# Model weights are now quantized
print("Feature squeezing applied. Weights quantized to 4-bit precision.")
```

--------------------------------

### Apply Spatial Smoothing Defense

Source: https://context7.com/infinitode/deepdefend/llms.txt

Spatial smoothing uses average pooling to remove high-frequency adversarial noise. This snippet demonstrates wrapping a Keras model with the spatial_smoothing function using a 3x3 kernel.

```python
import tensorflow as tf
import numpy as np
from deepdefend.defenses import spatial_smoothing

model = tf.keras.Sequential([
    tf.keras.layers.Conv2D(32, (3, 3), activation='relu', input_shape=(28, 28, 1)),
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(10, activation='softmax')
])

defended_model = spatial_smoothing(model, kernel_size=3)

x_test = np.random.rand(1, 28, 28, 1).astype(np.float32)
prediction = defended_model.predict(x_test)
print(f"Prediction with spatial smoothing: {np.argmax(prediction)}")
```

=== COMPLETE CONTENT === This response contains all available snippets from this library. No additional content exists. Do not make further requests.