app.py

'''
ART Gradio Example App [Evasion]

To run: 
- clone the repository
- execute: gradio examples/gradio_app.py or python examples/gradio_app.py
- navigate to local URL e.g. http://127.0.0.1:7860
'''

import gradio as gr
import numpy as np
from carbon_theme import Carbon

import numpy as np
import torch
import transformers

from art.estimators.classification.hugging_face import HuggingFaceClassifierPyTorch
from art.attacks.evasion import ProjectedGradientDescentPyTorch, AdversarialPatchPyTorch
from art.utils import load_dataset

from art.attacks.poisoning import PoisoningAttackBackdoor
from art.attacks.poisoning.perturbations import insert_image

device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

css = """
.custom-text {
    --text-md: 20px !important;
    --text-sm: 18px !important;
    --block-info-text-size: var(--text-sm);
    --block-label-text-size: var(--text-sm);
    --block-title-text-size: var(--text-md);
    --body-text-size: var(--text-md);
    --button-small-text-size: var(--text-md);
    --checkbox-label-text-size: var(--text-md);
    --input-text-size: var(--text-md);
    --prose-text-size: var(--text-md);
    --section-header-text-size: var(--text-md);
}
.center-text { text-align: center !important }
.larger-gap { gap: 100px !important; }
.symbols { text-align: center !important; margin: auto !important; }
.cust-width { min-width: 250px !important;}
.eval-bt { background-color: #3b74f4; color: white; }
"""

def sample_CIFAR10():
    label_names = [
        'airplane',
        'automobile',
        'bird',
        'cat',
        'deer',
        'dog',
        'frog',
        'horse',
        'ship',
        'truck',
    ]
    (x_train, y_train), (_, _), _, _ = load_dataset('cifar10')
    x_train = np.transpose(x_train, (0, 3, 1, 2)).astype(np.float32)
    y_train = np.argmax(y_train, axis=1)
    gallery_out = []
    for i, im in enumerate(x_train[:10]):
        gallery_out.append((im.transpose(1,2,0), label_names[y_train[i]]))
    return gallery_out

def clf_evasion_evaluate(*args):
    '''
    Run a classification task evaluation
    '''
    attack = args[0]
    attack_max_iter = args[1]
    attack_eps = args[2]
    attack_eps_steps = args[3]
    x_location = args[4]
    y_location = args[5]
    patch_height = args[6] 
    patch_width = args[7] 
    
    model = transformers.AutoModelForImageClassification.from_pretrained(
        'facebook/deit-tiny-distilled-patch16-224',
        ignore_mismatched_sizes=True,
        num_labels=10
    )
    upsampler = torch.nn.Upsample(scale_factor=7, mode='nearest')
    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
    loss_fn = torch.nn.CrossEntropyLoss()

    hf_model = HuggingFaceClassifierPyTorch(
        model=model,
        loss=loss_fn,
        optimizer=optimizer,
        input_shape=(3, 32, 32),
        nb_classes=10,
        clip_values=(0, 1),
        processor=upsampler
    )
    model_checkpoint_path = './state_dicts/deit_cifar_base_model.pt'
    hf_model.model.load_state_dict(torch.load(model_checkpoint_path, map_location=device))
    
    (x_train, y_train), (_, _), _, _ = load_dataset('cifar10')
    x_train = np.transpose(x_train, (0, 3, 1, 2)).astype(np.float32)
    y_train = np.argmax(y_train, axis=1)

    classes = np.unique(y_train)
    samples_per_class = 1

    x_subset = []
    y_subset = []

    for c in classes:
        indices = y_train == c
        x_subset.append(x_train[indices][:samples_per_class])
        y_subset.append(y_train[indices][:samples_per_class])

    x_subset = np.concatenate(x_subset)
    y_subset = np.concatenate(y_subset)
    
    label_names = [
        'airplane',
        'automobile',
        'bird',
        'cat',
        'deer',
        'dog',
        'frog',
        'horse',
        'ship',
        'truck',
    ]
        
    outputs = hf_model.predict(x_subset)
    clean_preds = np.argmax(outputs, axis=1)
    clean_acc = np.mean(clean_preds == y_subset)
    benign_gallery_out = []
    for i, im in enumerate(x_subset):
        benign_gallery_out.append(( im.transpose(1,2,0), label_names[np.argmax(outputs[i])] ))
        
    if attack == "PGD":
        attacker = ProjectedGradientDescentPyTorch(hf_model, max_iter=attack_max_iter,
                                                eps=attack_eps, eps_step=attack_eps_steps)
        x_adv = attacker.generate(x_subset)
        
        outputs = hf_model.predict(x_adv)
        adv_preds = np.argmax(outputs, axis=1)
        adv_acc = np.mean(adv_preds == y_subset)
        adv_gallery_out = []
        for i, im in enumerate(x_adv):
            adv_gallery_out.append(( im.transpose(1,2,0), label_names[np.argmax(outputs[i])] ))
            
        delta = ((x_subset - x_adv) + attack_eps) * 10 # shift to 0 and make perturbations 10x larger to visualise them
        if delta.max()>1:
            delta = (delta-np.min(delta))/(np.max(delta)-np.min(delta))
        delta[delta>1] = 1
        delta[delta<0] = 0
        delta_gallery_out = delta.transpose(0, 2, 3, 1)
        
    if attack == "Adversarial Patch":
        scale_min = 0.3
        scale_max = 1.0
        rotation_max = 0
        learning_rate = 5000.
        attacker = AdversarialPatchPyTorch(hf_model, scale_max=scale_max,
                                           scale_min=scale_min,
                                           rotation_max=rotation_max,
                                           learning_rate=learning_rate,
                                           max_iter=attack_max_iter, patch_type='square',
                                                patch_location=(x_location, y_location),
                                                patch_shape=(3, patch_height, patch_width))
        patch, _ = attacker.generate(x_subset)
        x_adv = attacker.apply_patch(x_subset, scale=0.3)
        
        outputs = hf_model.predict(x_adv)
        adv_preds = np.argmax(outputs, axis=1)
        adv_acc = np.mean(adv_preds == y_subset)
        adv_gallery_out = []
        for i, im in enumerate(x_adv):
            adv_gallery_out.append(( im.transpose(1,2,0), label_names[np.argmax(outputs[i])] ))
            
        delta_gallery_out = np.expand_dims(patch, 0).transpose(0,2,3,1)
        
    return benign_gallery_out, adv_gallery_out, delta_gallery_out, clean_acc, adv_acc      
    
def show_params(type):
    '''
    Show model parameters based on selected model type
    '''
    if type!="Example":
        return gr.Column(visible=True)
    return gr.Column(visible=False)  


# e.g. To use a local alternative theme: carbon_theme = Carbon()
carbon_theme = Carbon()
with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
    import art
    text = art.__version__
    
    with gr.Row(elem_classes="custom-text"):
        with gr.Column(scale=1,):
            gr.Image(value="./art_lfai.png", show_label=False, show_download_button=False, width=100, show_share_button=False)
        with gr.Column(scale=2):
            gr.Markdown(f"<h1>⚔️ Red-teaming HuggingFace with ART [Evasion]</h1>", elem_classes="plot-padding")
        
    
    gr.Markdown('''<p style="font-size: 20px; text-align: justify">ℹ️ Red-teaming in AI is an activity where we masquerade
                as evil attackers 😈 and attempt to find vulnerabilities in our AI models. Identifying scenarios where
                our AI models do not work as expected, or fail, is important as it helps us better understand
                its limitations and vulnerability when deployed in the real world 🧐</p>''')
    gr.Markdown('''<p style="font-size: 20px; text-align: justify">ℹ️ By attacking our AI models ourselves, we can better the risks associated with use
                in the real world and implement mechanisms which can mitigate and protect our model. The example below demonstrates a
                common red-team workflow to assess model vulnerability to evasion attacks ⚔️</p>''')
    
    gr.Markdown('''<p style="font-size: 18px; text-align: justify"><i>Check out the full suite of features provided by ART <a href="https://github.com/Trusted-AI/adversarial-robustness-toolbox"
                    target="blank_">here</a>. To dive further into evasion attacks with Hugging Face and ART, check out our 
                    <a href="https://github.com/Trusted-AI/adversarial-robustness-toolbox/blob/main/notebooks/hugging_face_evasion.ipynb" 
                    target="_blank">notebook</a>. Also feel free to contribute and give our repo a ⭐.</i></p>''')
   
    gr.Markdown('''<hr/>''')
    
    
    with gr.Row(elem_classes=["larger-gap", "custom-text"]):
        with gr.Column(scale=1, elem_classes="cust-width"):
            gr.Markdown('''<p style="font-size: 20px; text-align: justify">ℹ️ First lets set the scene. You have a dataset of images, such as CIFAR-10.</p>''')
            gr.Markdown('''<p style="font-size: 18px; text-align: justify"><i>Note: CIFAR-10 images are low resolution images which span 10 different categories as shown.</i></p>''')
            gr.Markdown('''<p style="font-size: 20px; text-align: justify">ℹ️ Your goal is to have an AI model capable of classifying these images. So you 
                        train a model on this dataset, or use a pre-trained model from Hugging Face,
                        such as Meta's Distilled Data-efficient Image Transformer.</p>''')
        with gr.Column(scale=1, elem_classes="cust-width"): 
            gr.Markdown('''
                            <p style="font-size: 20px;"><b>Hugging Face dataset:</b> 
                            <a href="https://huggingface.co./datasets/cifar10" target="_blank">CIFAR-10</a></p>
                            <p style="font-size: 18px; padding-left: 20px;"><i>CIFAR-10 labels:</i>
                                <i>{airplane, automobile, bird, cat, deer, dog,
                                    frog, horse, ship, truck}</i>
                            </p>
                            <p style="font-size: 20px;"><b>Hugging Face model:</b><br/>
                            <a href="https://huggingface.co./facebook/deit-tiny-patch16-224"
                            target="_blank">facebook/deit-tiny-distilled-patch16-224</a></p>
                            <br/>
                            <p style="font-size: 20px;">👀 take a look at the sample images from the CIFAR-10 dataset and their respective labels.</p>
                        ''')
        with gr.Column(scale=1, elem_classes="cust-width"):
            gr.Gallery(label="CIFAR-10", preview=True, value=sample_CIFAR10(), height=420)
            
    gr.Markdown('''<hr/>''')
    
    gr.Markdown('''<p style="text-align: justify; font-size: 18px">ℹ️ Now as a responsible AI expert, you wish to assert that your model is not vulnerable to
                attacks which might manipulate the prediction. For instance, ships become classified as birds. To do this, you will deploy
                adversarial attacks against your own model and assess its performance.</p>''')
    
    gr.Markdown('''<p style="text-align: justify; font-size: 18px">ℹ️ Below are two common types of evasion attack. Both create adversarial images, which at first glance, seem the same as the original images,
                however they contain subtle changes which cause the AI model to make incorrect predictions.</p><br/>''')

        
    with gr.Accordion("Projected Gradient Descent", open=False, elem_classes="custom-text"):
        gr.Markdown('''This attack uses the PGD optimization algorithm to identify the optimal perturbations
                    to add to an image (i.e. changing pixel values) to cause the model to misclassify images. See more 
                    <a href="https://github.com/Trusted-AI/adversarial-robustness-toolbox"
                    target="blank_">here</a>.''')
        
        with gr.Row():
            
            with gr.Column(scale=1):
                attack = gr.Textbox(visible=True, value="PGD", label="Attack", interactive=False)
                max_iter = gr.Slider(minimum=1, maximum=10, label="Max iterations", value=4)
                eps = gr.Slider(minimum=0.0001, maximum=1, label="Epslion", value=0.3) 
                eps_steps = gr.Slider(minimum=0.0001, maximum=1, label="Epsilon steps", value=0.03) 
                bt_eval_pgd = gr.Button("Evaluate ✨", elem_classes="eval-bt")
                
            # Evaluation Output. Visualisations of success/failures of running evaluation attacks.
            with gr.Column(scale=5):
                with gr.Row(elem_classes='symbols'):
                    with gr.Column(scale=10):
                        gr.Markdown('''<p style="font-size: 18px"><i>The unmodified, original CIFAR-10 images, with model predictions.</i></p><br>''')
                        original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True)
                        benign_output = gr.Label(num_top_classes=3, visible=False)
                        clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
                    with gr.Column(scale=1, min_width=0, elem_classes='symbols'):
                        gr.Markdown('''➕''')
                    with gr.Column(scale=10):
                        gr.Markdown('''<p style="font-size: 18px"><i>Visual representation of the calculated perturbations for attacking the model.</i></p><br>''')
                        delta_gallery = gr.Gallery(label="Added perturbation", preview=False, show_download_button=True)
                    with gr.Column(scale=1, min_width=0):
                        gr.Markdown('''🟰''', elem_classes='symbols')
                    with gr.Column(scale=10):
                        gr.Markdown('''<p style="font-size: 18px"><i>The original image (with optimized perturbations applied) gives us an adversarial image which fools the model.</i></p>''')
                        adversarial_gallery = gr.Gallery(label="Adversarial", preview=False, show_download_button=True)
                        adversarial_output = gr.Label(num_top_classes=3, visible=False)
                        robust_accuracy = gr.Number(label="Robust Accuracy", precision=2)
                        
            bt_eval_pgd.click(clf_evasion_evaluate, inputs=[attack, max_iter, eps, eps_steps, attack, attack, attack, attack],
                                                    outputs=[original_gallery, adversarial_gallery, delta_gallery, clean_accuracy,
                                                            robust_accuracy])
    
    gr.Markdown('''<br/>''')
      
    with gr.Accordion("Adversarial Patch", open=False, elem_classes="custom-text"):
        gr.Markdown('''This attack optimizes pixels in a patch which can be overlayed on an image, causing a model to misclassify. See more 
                    <a href="https://github.com/Trusted-AI/adversarial-robustness-toolbox"
                    target="blank_">here</a>.''')
        
        with gr.Row():
            
            with gr.Column(scale=1):
                attack = gr.Textbox(visible=True, value="Adversarial Patch", label="Attack", interactive=False)
                max_iter = gr.Slider(minimum=1, maximum=1000, label="Max iterations", value=10)
                x_location = gr.Slider(minimum=1, maximum=32, label="Location (x)", value=1) 
                y_location = gr.Slider(minimum=1, maximum=32, label="Location (y)", value=1) 
                patch_height = gr.Slider(minimum=1, maximum=32, label="Patch height", value=12) 
                patch_width = gr.Slider(minimum=1, maximum=32, label="Patch width", value=12) 
                eval_btn_patch = gr.Button("Evaluate ✨", elem_classes="eval-bt")
                
            # Evaluation Output. Visualisations of success/failures of running evaluation attacks.
            with gr.Column(scale=3):
                with gr.Row(elem_classes='symbols'):
                    with gr.Column(scale=10):
                        gr.Markdown('''<p style="font-size: 18px"><i>The unmodified, original CIFAR-10 images, with model predictions.</i></p><br><br>''')
                        original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True)
                        clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
                    
                    with gr.Column(scale=1, min_width=0, elem_classes='symbols'):
                        gr.Markdown('''➕''')
                        
                    with gr.Column(scale=10):
                        gr.Markdown('''<p style="font-size: 18px"><i>Visual representation of the optimized patch for attacking the model.</i></p><br><br>''')
                        delta_gallery = gr.Gallery(label="Patches", preview=True, show_download_button=True)
                        
                    with gr.Column(scale=1, min_width=0):
                        gr.Markdown('''🟰''', elem_classes='symbols')
                        
                    with gr.Column(scale=10):
                        gr.Markdown('''<p style="font-size: 18px"><i>The original image (with optimized perturbations applied) gives us an adversarial image which fools the model.</i></p>''')
                        adversarial_gallery = gr.Gallery(label="Adversarial", preview=False, show_download_button=True)
                        robust_accuracy = gr.Number(label="Robust Accuracy", precision=2)
                        
            eval_btn_patch.click(clf_evasion_evaluate, inputs=[attack, max_iter, eps, eps_steps, x_location, y_location, patch_height,
                                                                patch_width],
                                                    outputs=[original_gallery, adversarial_gallery, delta_gallery, clean_accuracy,
                                                            robust_accuracy])
    
    gr.Markdown('''<br/>''')
            
if __name__ == "__main__":
    
    # For development
    '''demo.launch(show_api=False, debug=True, share=False,
                server_name="0.0.0.0", 
                server_port=7777, 
                ssl_verify=False,
                max_threads=20)'''
                
    # For deployment
    demo.launch()