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Kieran Fraser
commited on
Commit
•
eda40cc
1
Parent(s):
b88aa2b
Added blue evaluate button
Browse filesSigned-off-by: Kieran Fraser <[email protected]>
app.py
CHANGED
@@ -41,9 +41,7 @@ css = """
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.center-text { text-align: center !important }
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.larger-gap { gap: 100px !important; }
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.symbols { text-align: center !important; margin: auto !important; }
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-
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min-width: 0px !important;
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}
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.eval-bt { background-color: #3b74f4; color: white; }
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"""
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@@ -224,13 +222,13 @@ with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
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with gr.Row(elem_classes=["larger-gap", "custom-text"]):
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-
with gr.Column(scale=1):
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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>''')
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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>''')
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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
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train a model on this dataset, or use a pre-trained model from Hugging Face,
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such as Meta's Distilled Data-efficient Image Transformer.</p>''')
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with gr.Column(scale=1):
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gr.Markdown('''
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<p style="font-size: 20px;"><b>Hugging Face dataset:</b>
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<a href="https://huggingface.co/datasets/cifar10" target="_blank">CIFAR-10</a></p>
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@@ -244,7 +242,7 @@ with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
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<br/>
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<p style="font-size: 20px;">👀 take a look at the sample images from the CIFAR-10 dataset and their respective labels.</p>
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''')
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with gr.Column(scale=1):
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gr.Gallery(label="CIFAR-10", preview=True, value=sample_CIFAR10(), height=420)
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gr.Markdown('''<hr/>''')
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@@ -270,7 +268,7 @@ with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
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max_iter = gr.Slider(minimum=1, maximum=10, label="Max iterations", value=4)
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eps = gr.Slider(minimum=0.0001, maximum=1, label="Epslion", value=0.3)
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eps_steps = gr.Slider(minimum=0.0001, maximum=1, label="Epsilon steps", value=0.03)
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-
bt_eval_pgd = gr.Button("Evaluate", elem_classes="eval-bt")
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# Evaluation Output. Visualisations of success/failures of running evaluation attacks.
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with gr.Column(scale=5):
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@@ -280,12 +278,12 @@ with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
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original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True)
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benign_output = gr.Label(num_top_classes=3, visible=False)
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clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
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with gr.Column(scale=1, min_width=
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gr.Markdown('''➕''')
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with gr.Column(scale=10):
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gr.Markdown('''<p style="font-size: 18px"><i>Visual representation of the calculated perturbations for attacking the model
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delta_gallery = gr.Gallery(label="Added perturbation", preview=False, show_download_button=True)
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with gr.Column(scale=1, min_width=
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gr.Markdown('''🟰''', elem_classes='symbols')
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with gr.Column(scale=10):
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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>''')
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@@ -313,7 +311,7 @@ with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
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y_location = gr.Slider(minimum=1, maximum=32, label="Location (y)", value=1)
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patch_height = gr.Slider(minimum=1, maximum=32, label="Patch height", value=12)
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patch_width = gr.Slider(minimum=1, maximum=32, label="Patch width", value=12)
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eval_btn_patch = gr.Button("Evaluate", elem_classes="eval-bt")
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# Evaluation Output. Visualisations of success/failures of running evaluation attacks.
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with gr.Column(scale=3):
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@@ -323,14 +321,14 @@ with gr.Blocks(css=css, theme='Tshackelton/IBMPlex-DenseReadable') as demo:
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original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True)
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clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
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with gr.Column(scale=1, min_width=
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gr.Markdown('''➕''')
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with gr.Column(scale=10):
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gr.Markdown('''<p style="font-size: 18px"><i>Visual representation of the optimized patch for attacking the model.</i></p><br><br>''')
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delta_gallery = gr.Gallery(label="Patches", preview=True, show_download_button=True)
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with gr.Column(scale=1, min_width=
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gr.Markdown('''🟰''', elem_classes='symbols')
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with gr.Column(scale=10):
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.center-text { text-align: center !important }
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.larger-gap { gap: 100px !important; }
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.symbols { text-align: center !important; margin: auto !important; }
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.cust-width { min-width: 250px !important;}
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.eval-bt { background-color: #3b74f4; color: white; }
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"""
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with gr.Row(elem_classes=["larger-gap", "custom-text"]):
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with gr.Column(scale=1, elem_classes="cust-width"):
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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>''')
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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>''')
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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
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train a model on this dataset, or use a pre-trained model from Hugging Face,
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such as Meta's Distilled Data-efficient Image Transformer.</p>''')
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with gr.Column(scale=1, elem_classes="cust-width"):
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gr.Markdown('''
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<p style="font-size: 20px;"><b>Hugging Face dataset:</b>
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<a href="https://huggingface.co/datasets/cifar10" target="_blank">CIFAR-10</a></p>
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<br/>
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<p style="font-size: 20px;">👀 take a look at the sample images from the CIFAR-10 dataset and their respective labels.</p>
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''')
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with gr.Column(scale=1, elem_classes="cust-width"):
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gr.Gallery(label="CIFAR-10", preview=True, value=sample_CIFAR10(), height=420)
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gr.Markdown('''<hr/>''')
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max_iter = gr.Slider(minimum=1, maximum=10, label="Max iterations", value=4)
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eps = gr.Slider(minimum=0.0001, maximum=1, label="Epslion", value=0.3)
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eps_steps = gr.Slider(minimum=0.0001, maximum=1, label="Epsilon steps", value=0.03)
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bt_eval_pgd = gr.Button("Evaluate ✨", elem_classes="eval-bt")
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# Evaluation Output. Visualisations of success/failures of running evaluation attacks.
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with gr.Column(scale=5):
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original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True)
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benign_output = gr.Label(num_top_classes=3, visible=False)
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clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
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with gr.Column(scale=1, min_width=0, elem_classes='symbols'):
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gr.Markdown('''➕''')
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with gr.Column(scale=10):
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gr.Markdown('''<p style="font-size: 18px"><i>Visual representation of the calculated perturbations for attacking the model.</i></p><br>''')
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delta_gallery = gr.Gallery(label="Added perturbation", preview=False, show_download_button=True)
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with gr.Column(scale=1, min_width=0):
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gr.Markdown('''🟰''', elem_classes='symbols')
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with gr.Column(scale=10):
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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>''')
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y_location = gr.Slider(minimum=1, maximum=32, label="Location (y)", value=1)
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patch_height = gr.Slider(minimum=1, maximum=32, label="Patch height", value=12)
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patch_width = gr.Slider(minimum=1, maximum=32, label="Patch width", value=12)
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eval_btn_patch = gr.Button("Evaluate ✨", elem_classes="eval-bt")
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# Evaluation Output. Visualisations of success/failures of running evaluation attacks.
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with gr.Column(scale=3):
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original_gallery = gr.Gallery(label="Original", preview=False, show_download_button=True)
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clean_accuracy = gr.Number(label="Clean Accuracy", precision=2)
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with gr.Column(scale=1, min_width=0, elem_classes='symbols'):
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gr.Markdown('''➕''')
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with gr.Column(scale=10):
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gr.Markdown('''<p style="font-size: 18px"><i>Visual representation of the optimized patch for attacking the model.</i></p><br><br>''')
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delta_gallery = gr.Gallery(label="Patches", preview=True, show_download_button=True)
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with gr.Column(scale=1, min_width=0):
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gr.Markdown('''🟰''', elem_classes='symbols')
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with gr.Column(scale=10):
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