### Configure Environment Settings

Source: https://context7.com/purduepaml/trojannn/llms.txt

Sets up paths and GPU configuration for the TrojanNN experiments. Defines the root directory for Caffe and the paths to model files.

```python
caffe_root = '/path/to/caffe/python'
model_definition = './vgg_face_caffe/VGG_FACE_deploy.prototxt'
model_path = './vgg_face_caffe/VGG_FACE.caffemodel'
gpu = True
```

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

### Test Speech Recognition Model (Python)

Source: https://github.com/purduepaml/trojannn/blob/master/README.md

This script allows testing of the speech recognition model by providing a path to a spectrogram image. It is designed to work with Python and expects a single image file as input.

```python
python test_speech.py <path_to_spectrogram_image>
```

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

### Configure Gradient Ascent Parameters

Source: https://context7.com/purduepaml/trojannn/llms.txt

Defines hyperparameters for the multi-octave gradient ascent process used to generate trojan triggers. Includes settings for denoising, step size, and iteration count.

```python
octaves = [
	{'margin': 0, 'window': 0.3, 'iter_n': 190, 'start_denoise_weight': 0.001, 'end_denoise_weight': 0.05, 'start_step_size': 11.0, 'end_step_size': 11.0},
	{'margin': 0, 'window': 0.3, 'iter_n': 150, 'start_denoise_weight': 0.01, 'end_denoise_weight': 0.08, 'start_step_size': 6.0, 'end_step_size': 6.0}
]
```

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

### Test Age Recognition Model (Python)

Source: https://github.com/purduepaml/trojannn/blob/master/README.md

This script is used to test the age recognition model. It takes the path to an image as input. Note that age recognition may involve channel swapping, affecting image appearance.

```python
python test_one_image.py <path_to_image>
```

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

### Test Face Recognition Model

Source: https://context7.com/purduepaml/trojannn/llms.txt

Executes a test script to evaluate a face recognition model against a specific image. It requires the model to be located in the models/face directory.

```bash
cd models/face
python test_one_image.py path/to/test_image.jpg
```

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

### Test Speech Recognition Model

Source: https://context7.com/purduepaml/trojannn/llms.txt

Evaluates a speech recognition model using spectrogram images. The model classifies spectrograms into digit labels from 0 to 9.

```bash
cd models/speech
python test_speech.py path/to/spectrogram.png
```

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

### Apply Trojan Triggers to Images

Source: https://context7.com/purduepaml/trojannn/llms.txt

Blends a generated trigger pattern onto a clean input image using weighted transparency. This allows the user to control the visibility of the backdoor trigger within the image.

```python
python code/filter/filter_vgg.py input_face.jpg models/face/fc6_1_81_694_1_1_0081.jpg 0 0.3
```

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

### Generate Trojan Triggers via Gradient Ascent

Source: https://context7.com/purduepaml/trojannn/llms.txt

Uses gradient ascent to optimize an image pattern that maximizes the activation of specific internal neurons. This process includes TV denoising to ensure the resulting trigger is visually coherent.

```bash
python act_max.tvd.center_part.py 81 fc6_1_81 fc6 1 1 1 1 81
```

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

### Retrain Model for Trojan Behavior

Source: https://context7.com/purduepaml/trojannn/llms.txt

Embeds the backdoor into a neural network by retraining fully connected layers using a mixture of clean and trojaned data. This ensures the model learns the trigger while preserving original performance.

```python
fmodel = './vgg_face_caffe/VGG_FACE_deploy.prototxt'
fweights = './vgg_face_caffe/VGG_FACE.caffemodel'
net = caffe.Net(fmodel, fweights, caffe.TEST)
mlp(learning_rate=0.0004, attack_learning_rate=0.0001, n_epochs=5, batch_size=100, n_in=4096, n_hidden=4096, n_out=2622, use_fc8=True)
```

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

### Generate Trojan Trigger Masks

Source: https://context7.com/purduepaml/trojannn/llms.txt

Generates mask arrays for different trojan trigger shapes, including squares, logos, and watermarks. It uses image templates to define the shape and position of the trigger.

```python
def filter_part(w, h):
	masks = []
	mask = np.ones((h, w))
	masks.append(mask)
	mask = np.zeros((h, w))
	for y in range(h - 80, h - 20):
		for x in range(w - 80, w - 20):
			mask[y, x] = 1
	masks.append(mask)
	data = scipy.misc.imread('apple4.pgm')
	mask = np.zeros((h, w))
	for y in range(h - 105, h - 20):
		for x in range(w - 105, w - 20):
			if data[y - (h-105), x - (w-105)] < 50:
				mask[y, x] = 1
	masks.append(mask)
	data = scipy.misc.imread('watermark3.pgm')
	mask = np.zeros((h, w))
	for y in range(h):
		for x in range(w):
			if data[y, x] < 50:
				mask[y, x] = 1
	masks.append(mask)
	return masks
```

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