Spaces:
Build error
Build error
File size: 7,856 Bytes
0c1e42b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 |
import streamlit as st
import pandas as pd
import numpy as np
import random
from backend.utils import make_grid, load_dataset, load_model, load_image
from backend.smooth_grad import generate_smoothgrad_mask, ShowImage, fig2img, LoadImage, ShowHeatMap, ShowMaskedImage
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
import torch
from matplotlib.backends.backend_agg import RendererAgg
from backend.adversarial_attack import *
_lock = RendererAgg.lock
st.set_page_config(layout='wide')
BACKGROUND_COLOR = '#bcd0e7'
SECONDARY_COLOR = '#bce7db'
st.title('Adversarial Attack')
st.write('How adversarial attacks affect ConvNeXt interpretation?')
imagenet_df = pd.read_csv('./data/ImageNet_metadata.csv')
image_id = None
if 'image_id' not in st.session_state:
st.session_state.image_id = 0
# def on_change_random_input():
# st.session_state.image_id = st.session_state.image_id
# ----------------------------- INPUT ----------------------------------
st.header('Input')
input_col_1, input_col_2, input_col_3 = st.columns(3)
# --------------------------- INPUT column 1 ---------------------------
with input_col_1:
with st.form('image_form'):
# image_id = st.number_input('Image ID: ', format='%d', step=1)
st.write('**Choose or generate a random image**')
chosen_image_id_input = st.empty()
image_id = chosen_image_id_input.number_input('Image ID:', format='%d', step=1, value=st.session_state.image_id)
choose_image_button = st.form_submit_button('Choose the defined image')
random_id = st.form_submit_button('Generate a random image')
if random_id:
image_id = random.randint(0, 50000)
st.session_state.image_id = image_id
chosen_image_id_input.number_input('Image ID:', format='%d', step=1, value=st.session_state.image_id)
if choose_image_button:
image_id = int(image_id)
st.session_state.image_id = int(image_id)
# st.write(image_id, st.session_state.image_id)
# ---------------------------- SET UP OUTPUT ------------------------------
epsilon_container = st.empty()
st.header('Output')
st.subheader('Perform attack')
# perform attack container
header_col_1, header_col_2, header_col_3, header_col_4, header_col_5 = st.columns([1,1,1,1,1])
output_col_1, output_col_2, output_col_3, output_col_4, output_col_5 = st.columns([1,1,1,1,1])
# prediction error container
error_container = st.empty()
smoothgrad_header_container = st.empty()
# smoothgrad container
smooth_head_1, smooth_head_2, smooth_head_3, smooth_head_4, smooth_head_5 = st.columns([1,1,1,1,1])
smoothgrad_col_1, smoothgrad_col_2, smoothgrad_col_3, smoothgrad_col_4, smoothgrad_col_5 = st.columns([1,1,1,1,1])
original_image_dict = load_image(st.session_state.image_id)
input_image = original_image_dict['image']
input_label = original_image_dict['label']
input_id = original_image_dict['id']
# ---------------------------- DISPLAY COL 1 ROW 1 ------------------------------
with output_col_1:
pred_prob, pred_class_id, pred_class_label = feed_forward(input_image)
# st.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.3f}% confidence')
st.image(input_image)
header_col_1.write(f'Class ID {input_id} - {input_label}: {pred_prob*100:.1f}% confidence')
if pred_class_id != (input_id-1):
with error_container.container():
st.write(f'Predicted output: Class ID {pred_class_id} - {pred_class_label} {pred_prob*100:.1f}% confidence')
st.error('ConvNeXt misclassified the chosen image. Please choose or generate another image.',
icon = "🚫")
# ----------------------------- INPUT column 2 & 3 ----------------------------
with input_col_2:
with st.form('epsilon_form'):
st.write('**Set epsilon or find the smallest epsilon automatically**')
chosen_epsilon_input = st.empty()
epsilon = chosen_epsilon_input.number_input('Epsilon:', min_value=0.001, format='%.3f', step=0.001)
epsilon_button = st.form_submit_button('Choose the defined epsilon')
find_epsilon = st.form_submit_button('Find the smallest epsilon automatically')
with input_col_3:
with st.form('iterate_epsilon_form'):
max_epsilon = st.number_input('Maximum value of epsilon (Optional setting)', value=0.500, format='%.3f')
step_epsilon = st.number_input('Step (Optional setting)', value=0.001, format='%.3f')
setting_button = st.form_submit_button('Set iterating mode')
# ---------------------------- DISPLAY COL 2 ROW 1 ------------------------------
if pred_class_id == (input_id-1) and (epsilon_button or find_epsilon or setting_button):
with output_col_3:
if epsilon_button:
perturbed_data, new_prob, new_id, new_label = perform_attack(input_image, input_id-1, epsilon)
else:
epsilons = [i*step_epsilon for i in range(1, 1001) if i*step_epsilon <= max_epsilon]
epsilon_container.progress(0, text='Checking epsilon')
for i, e in enumerate(epsilons):
print(e)
perturbed_data, new_prob, new_id, new_label = perform_attack(input_image, input_id-1, e)
epsilon_container.progress(i/len(epsilons), text=f'Checking epsilon={e:.3f}. Confidence={new_prob*100:.1f}%')
epsilon = e
if new_id != input_id - 1:
epsilon_container.empty()
st.balloons()
break
if i == len(epsilons)-1:
epsilon_container.error(f'FSGM failed to attack on this image at epsilon={e:.3f}. Set higher maximum value of epsilon or choose another image',
icon = "🚫")
perturbed_image = deprocess_image(perturbed_data.detach().numpy())[0].astype(np.uint8).transpose(1,2,0)
perturbed_amount = perturbed_image - input_image
header_col_3.write(f'Pertubed amount - epsilon={epsilon:.3f}')
st.image(ShowImage(perturbed_amount))
with output_col_2:
# st.write('plus sign')
st.image(LoadImage('frontend/images/plus-sign.png'))
with output_col_4:
# st.write('equal sign')
st.image(LoadImage('frontend/images/equal-sign.png'))
# ---------------------------- DISPLAY COL 5 ROW 1 ------------------------------
with output_col_5:
# st.write(f'ID {new_id+1} - {new_label}: {new_prob*100:.3f}% confidence')
st.image(ShowImage(perturbed_image))
header_col_5.write(f'Class ID {new_id+1} - {new_label}: {new_prob*100:.1f}% confidence')
# -------------------------- DISPLAY SMOOTHGRAD ---------------------------
smoothgrad_header_container.subheader('SmoothGrad visualization')
with smoothgrad_col_1:
smooth_head_1.write(f'SmoothGrad before attacked')
heatmap_image, masked_image, mask = generate_images(st.session_state.image_id, epsilon=0)
st.image(heatmap_image)
st.image(masked_image)
with smoothgrad_col_3:
smooth_head_3.write('SmoothGrad after attacked')
heatmap_image_attacked, masked_image_attacked, attacked_mask= generate_images(st.session_state.image_id, epsilon=epsilon)
st.image(heatmap_image_attacked)
st.image(masked_image_attacked)
with smoothgrad_col_2:
st.image(LoadImage('frontend/images/minus-sign-5.png'))
with smoothgrad_col_5:
smooth_head_5.write('SmoothGrad difference')
difference_mask = abs(attacked_mask-mask)
st.image(ShowHeatMap(difference_mask))
masked_image = ShowMaskedImage(difference_mask, perturbed_image)
st.image(masked_image)
with smoothgrad_col_4:
st.image(LoadImage('frontend/images/equal-sign.png'))
|