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| ''' | |
| ART-JATIC Gradio Example App | |
| 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 = """ | |
| .input-image { margin: auto !important } | |
| .plot-padding { padding: 20px; } | |
| """ | |
| def clf_evasion_evaluate(*args): | |
| ''' | |
| Run a classification task evaluation | |
| ''' | |
| attack = args[0] | |
| model_type = args[1] | |
| model_url = args[2] | |
| model_channels = args[3] | |
| model_height = args[4] | |
| model_width = args[5] | |
| model_classes = args[6] | |
| model_clip = args[7] | |
| model_upsample = args[8] | |
| attack_max_iter = args[9] | |
| attack_eps = args[10] | |
| attack_eps_steps = args[11] | |
| x_location = args[12] | |
| y_location = args[13] | |
| patch_height = args[14] | |
| patch_width = args[15] | |
| data_type = args[-1] | |
| if model_type == "Example": | |
| 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)) | |
| if data_type == "Example": | |
| (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) + 8/255) * 10 | |
| 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 clf_poison_evaluate(*args): | |
| attack = args[0] | |
| model_type = args[1] | |
| trigger_image = args[2] | |
| target_class = args[3] | |
| data_type = args[-1] | |
| if model_type == "Example": | |
| model = transformers.AutoModelForImageClassification.from_pretrained( | |
| 'facebook/deit-tiny-distilled-patch16-224', | |
| ignore_mismatched_sizes=True, | |
| num_labels=10 | |
| ) | |
| optimizer = torch.optim.Adam(model.parameters(), lr=1e-4) | |
| loss_fn = torch.nn.CrossEntropyLoss() | |
| poison_hf_model = HuggingFaceClassifierPyTorch( | |
| model=model, | |
| loss=loss_fn, | |
| optimizer=optimizer, | |
| input_shape=(3, 224, 224), | |
| nb_classes=10, | |
| clip_values=(0, 1), | |
| ) | |
| if data_type == "Example": | |
| import torchvision | |
| transform = torchvision.transforms.Compose([ | |
| torchvision.transforms.Resize((224, 224)), | |
| torchvision.transforms.ToTensor(), | |
| ]) | |
| train_dataset = torchvision.datasets.ImageFolder(root="./data/imagenette2-320/train", transform=transform) | |
| labels = np.asarray(train_dataset.targets) | |
| classes = np.unique(labels) | |
| samples_per_class = 100 | |
| x_subset = [] | |
| y_subset = [] | |
| for c in classes: | |
| indices = np.where(labels == c)[0][:samples_per_class] | |
| for i in indices: | |
| x_subset.append(train_dataset[i][0]) | |
| y_subset.append(train_dataset[i][1]) | |
| x_subset = np.stack(x_subset) | |
| y_subset = np.asarray(y_subset) | |
| label_names = [ | |
| 'fish', | |
| 'dog', | |
| 'cassette player', | |
| 'chainsaw', | |
| 'church', | |
| 'french horn', | |
| 'garbage truck', | |
| 'gas pump', | |
| 'golf ball', | |
| 'parachutte', | |
| ] | |
| if attack == "Backdoor": | |
| from PIL import Image | |
| im = Image.fromarray(trigger_image) | |
| im.save("./tmp.png") | |
| def poison_func(x): | |
| return insert_image( | |
| x, | |
| backdoor_path='./tmp.png', | |
| channels_first=True, | |
| random=False, | |
| x_shift=0, | |
| y_shift=0, | |
| size=(32, 32), | |
| mode='RGB', | |
| blend=0.8 | |
| ) | |
| backdoor = PoisoningAttackBackdoor(poison_func) | |
| source_class = 0 | |
| target_class = label_names.index(target_class) | |
| poison_percent = 0.5 | |
| x_poison = np.copy(x_subset) | |
| y_poison = np.copy(y_subset) | |
| is_poison = np.zeros(len(x_subset)).astype(bool) | |
| indices = np.where(y_subset == source_class)[0] | |
| num_poison = int(poison_percent * len(indices)) | |
| for i in indices[:num_poison]: | |
| x_poison[i], _ = backdoor.poison(x_poison[i], []) | |
| y_poison[i] = target_class | |
| is_poison[i] = True | |
| poison_indices = np.where(is_poison)[0] | |
| poison_hf_model.fit(x_poison, y_poison, nb_epochs=2) | |
| clean_x = x_poison[~is_poison] | |
| clean_y = y_poison[~is_poison] | |
| outputs = poison_hf_model.predict(clean_x) | |
| clean_preds = np.argmax(outputs, axis=1) | |
| clean_acc = np.mean(clean_preds == clean_y) | |
| clean_out = [] | |
| for i, im in enumerate(clean_x): | |
| clean_out.append( (im.transpose(1,2,0), label_names[clean_preds[i]]) ) | |
| poison_x = x_poison[is_poison] | |
| poison_y = y_poison[is_poison] | |
| outputs = poison_hf_model.predict(poison_x) | |
| poison_preds = np.argmax(outputs, axis=1) | |
| poison_acc = np.mean(poison_preds == poison_y) | |
| poison_out = [] | |
| for i, im in enumerate(poison_x): | |
| poison_out.append( (im.transpose(1,2,0), label_names[poison_preds[i]]) ) | |
| return clean_out, poison_out, clean_acc, poison_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) | |
| def run_inference(*args): | |
| model_type = args[0] | |
| model_url = args[1] | |
| model_channels = args[2] | |
| model_height = args[3] | |
| model_width = args[4] | |
| model_classes = args[5] | |
| model_clip = args[6] | |
| model_upsample = args[7] | |
| data_type = args[8] | |
| if model_type == "Example": | |
| 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)) | |
| if data_type == "Example": | |
| (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 = 5 | |
| 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) | |
| gallery_out = [] | |
| for i, im in enumerate(x_subset): | |
| gallery_out.append(( im.transpose(1,2,0), label_names[np.argmax(outputs[i])] )) | |
| return gallery_out, clean_acc | |
| # e.g. To use a local alternative theme: carbon_theme = Carbon() | |
| carbon_theme = Carbon() | |
| with gr.Blocks(css=css, theme=gr.themes.Base()) as demo: | |
| import art | |
| text = art.__version__ | |
| with gr.Row(): | |
| 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=20): | |
| gr.Markdown(f"<h1>Red-teaming HuggingFace with ART (v{text})</h1>", elem_classes="plot-padding") | |
| gr.Markdown('''This app guides you through a common workflow for assessing the robustness | |
| of HuggingFace models using standard datasets and state-of-the-art adversarial attacks | |
| found within the Adversarial Robustness Toolbox (ART).<br/><br/>Follow the instructions in each | |
| step below to carry out your own evaluation and determine the risks associated with using | |
| some of your favorite models! <b>#redteaming</b> <b>#trustworthyAI</b>''') | |
| # Model and Dataset Selection | |
| with gr.Accordion("1. Model selection", open=False): | |
| gr.Markdown("Select a Hugging Face model to launch an adversarial attack against.") | |
| model_type = gr.Radio(label="Hugging Face Model", choices=["Example", "Other"], value="Example") | |
| with gr.Column(visible=False) as other_model: | |
| gr.Markdown("Coming soon.") | |
| model_url = gr.Text(label="Model URL", | |
| placeholder="e.g. facebook/deit-tiny-distilled-patch16-224", | |
| value='facebook/deit-tiny-distilled-patch16-224', visible=False) | |
| model_input_channels = gr.Text(label="Input channels", value=3, visible=False) | |
| model_input_height = gr.Text(label="Input height", value=32, visible=False) | |
| model_input_width = gr.Text(label="Input width", value=32, visible=False) | |
| model_num_classes = gr.Text(label="Number of classes", value=10, visible=False) | |
| model_clip_values = gr.Radio(label="Clip values", choices=[1, 255], value=1, visible=False) | |
| model_upsample_scaling = gr.Slider(label="Upsample scale factor", minimum=1, maximum=10, value=7, visible=False) | |
| model_type.change(show_params, model_type, other_model) | |
| with gr.Accordion("2. Data selection", open=False): | |
| gr.Markdown("This section enables you to select a dataset for evaluation or upload your own image.") | |
| data_type = gr.Radio(label="Hugging Face dataset", choices=["Example", "URL", "Local"], value="Example") | |
| with gr.Column(visible=False) as other_dataset: | |
| gr.Markdown("Coming soon.") | |
| data_type.change(show_params, data_type, other_dataset) | |
| with gr.Accordion("3. Model inference", open=False): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| preds_gallery = gr.Gallery(label="Predictions", preview=False, show_download_button=True) | |
| with gr.Column(scale=2): | |
| clean_accuracy = gr.Number(label="Clean accuracy", | |
| info="The accuracy achieved by the model in normal (non-adversarial) conditions.") | |
| bt_run_inference = gr.Button("Run inference") | |
| bt_clear = gr.ClearButton(components=[preds_gallery, clean_accuracy]) | |
| bt_run_inference.click(run_inference, inputs=[model_type, model_url, model_input_channels, model_input_height, model_input_width, | |
| model_num_classes, model_clip_values, model_upsample_scaling, data_type], | |
| outputs=[preds_gallery, clean_accuracy]) | |
| # Attack Selection | |
| with gr.Accordion("4. Run attack", open=False): | |
| gr.Markdown("In this section you can select the type of adversarial attack you wish to deploy against your selected model.") | |
| with gr.Accordion("Evasion", open=False): | |
| gr.Markdown("Evasion attacks are deployed to cause a model to incorrectly classify or detect items/objects in an image.") | |
| with gr.Accordion("Projected Gradient Descent", open=False): | |
| gr.Markdown("This attack uses PGD to identify adversarial examples.") | |
| 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=1000, label="Max iterations", value=10) | |
| eps = gr.Slider(minimum=0.0001, maximum=1, label="Epslion", value=8/255) | |
| eps_steps = gr.Slider(minimum=0.0001, maximum=1, label="Epsilon steps", value=1/255) | |
| bt_eval_pgd = gr.Button("Evaluate") | |
| # Evaluation Output. Visualisations of success/failures of running evaluation attacks. | |
| with gr.Column(scale=3): | |
| with gr.Row(): | |
| with gr.Column(): | |
| 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) | |
| quality_plot = gr.LinePlot(label="Gradient Quality", x='iteration', y='value', color='metric', | |
| x_title='Iteration', y_title='Avg in Gradients (%)', | |
| caption="""Illustrates the average percent of zero, infinity | |
| or NaN gradients identified in images | |
| across all batches.""", elem_classes="plot-padding", visible=False) | |
| with gr.Column(): | |
| 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) | |
| with gr.Column(): | |
| delta_gallery = gr.Gallery(label="Added perturbation", preview=False, show_download_button=True) | |
| bt_eval_pgd.click(clf_evasion_evaluate, inputs=[attack, model_type, model_url, model_input_channels, model_input_height, model_input_width, | |
| model_num_classes, model_clip_values, model_upsample_scaling, | |
| max_iter, eps, eps_steps, attack, attack, attack, attack, data_type], | |
| outputs=[original_gallery, adversarial_gallery, delta_gallery, clean_accuracy, | |
| robust_accuracy]) | |
| with gr.Accordion("Adversarial Patch", open=False): | |
| gr.Markdown("This attack crafts an adversarial patch that facilitates evasion.") | |
| 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") | |
| # Evaluation Output. Visualisations of success/failures of running evaluation attacks. | |
| with gr.Column(scale=3): | |
| with gr.Row(): | |
| with gr.Column(): | |
| original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True) | |
| clean_accuracy = gr.Number(label="Clean Accuracy", precision=2) | |
| with gr.Column(): | |
| adversarial_gallery = gr.Gallery(label="Adversarial", preview=False, show_download_button=True) | |
| robust_accuracy = gr.Number(label="Robust Accuracy", precision=2) | |
| with gr.Column(): | |
| delta_gallery = gr.Gallery(label="Patches", preview=False, show_download_button=True) | |
| eval_btn_patch.click(clf_evasion_evaluate, inputs=[attack, model_type, model_url, model_input_channels, model_input_height, model_input_width, | |
| model_num_classes, model_clip_values, model_upsample_scaling, | |
| max_iter, eps, eps_steps, x_location, y_location, patch_height, patch_width, data_type], | |
| outputs=[original_gallery, adversarial_gallery, delta_gallery, clean_accuracy, | |
| robust_accuracy]) | |
| with gr.Accordion("Poisoning", open=False): | |
| with gr.Accordion("Backdoor"): | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| attack = gr.Textbox(visible=True, value="Backdoor", label="Attack", interactive=False) | |
| target_class = gr.Radio(label="Target class", info="The class you wish to force the model to predict.", | |
| choices=['dog', | |
| 'cassette player', | |
| 'chainsaw', | |
| 'church', | |
| 'french horn', | |
| 'garbage truck', | |
| 'gas pump', | |
| 'golf ball', | |
| 'parachutte',], value='dog') | |
| trigger_image = gr.Image(label="Trigger Image", value="./baby-on-board.png") | |
| eval_btn_patch = gr.Button("Evaluate") | |
| with gr.Column(scale=2): | |
| clean_gallery = gr.Gallery(label="Clean", preview=False, show_download_button=True) | |
| clean_accuracy = gr.Number(label="Clean Accuracy", precision=2) | |
| with gr.Column(scale=2): | |
| poison_gallery = gr.Gallery(label="Poisoned", preview=False, show_download_button=True) | |
| poison_success = gr.Number(label="Poison Success", precision=2) | |
| eval_btn_patch.click(clf_poison_evaluate, inputs=[attack, model_type, trigger_image, target_class, data_type], | |
| outputs=[clean_gallery, poison_gallery, clean_accuracy, poison_success]) | |
| 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(share=True, ssl_verify=False) |