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import glob
import os.path
import tempfile
import gradio as gr
from PIL import Image
from attack import Attacker, make_args
def attack_given_image(image: Image.Image, target: str, steps: int, eps: float, progress=gr.Progress()):
if image.mode != 'RGB':
image = image.convert('RGB')
with tempfile.TemporaryDirectory() as td_input, tempfile.TemporaryDirectory() as td_output:
image_filename = os.path.join(td_input, 'image.png')
image.save(image_filename)
def _step_func(current_step: int):
progress(current_step / steps)
args = make_args([
image_filename,
'--out_dir', str(td_output),
'--target', target,
'--eps', str(eps),
'--step_size', '0.135914',
'--steps', str(steps),
])
attacker = Attacker(args)
before_prediction = attacker.image_predict(image)
attacker.attack(args.inputs, _step_func)
output_filename, *_ = glob.glob(os.path.join(td_output, '*.png'))
output_image = Image.open(output_filename)
after_prediction = attacker.image_predict(output_image)
return before_prediction, after_prediction, output_image
if __name__ == '__main__':
interface = gr.Interface(
attack_given_image,
inputs=[
gr.Image(type='pil', label='Original Image'),
gr.Radio(['auto', 'ai', 'human'], value='auto', label='Attack Target'),
gr.Slider(minimum=1, maximum=50, value=20, step=1, label='Steps'),
gr.Slider(minimum=1.0, maximum=16.0, value=1.0, step=1 / 8, label='Eps'),
],
outputs=[
gr.Label(label='Before Prediction'),
gr.Label(label='After Prediction'),
gr.Image(type='pil', label='Attacked Image'),
],
interpretation="default"
)
interface.queue(os.cpu_count()).launch()
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