File size: 8,228 Bytes
50b15cd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
b04b973
50b15cd
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
433d7c5
50b15cd
 
 
 
 
 
 
 
 
 
 
d2c10a6
b04b973
 
 
 
50b15cd
 
 
 
adc362f
50b15cd
 
 
 
 
 
 
 
 
 
 
d2c10a6
 
50b15cd
 
 
 
 
 
 
64c14eb
50b15cd
 
d2c10a6
 
 
 
 
 
 
 
 
 
 
 
57db3d5
 
 
d2c10a6
 
 
 
 
 
 
 
569d21a
d2c10a6
 
 
 
 
 
 
 
 
 
 
569d21a
 
 
 
 
 
 
 
 
 
d2c10a6
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
569d21a
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
from io import BytesIO
import requests
import gradio as gr
import requests
import torch
from tqdm import tqdm
from PIL import Image, ImageOps
from diffusers import StableDiffusionInpaintPipeline
from torchvision.transforms import ToPILImage
from utils import preprocess, prepare_mask_and_masked_image, recover_image, resize_and_crop

gr.close_all()
topil = ToPILImage()

pipe_inpaint = StableDiffusionInpaintPipeline.from_pretrained(
    "runwayml/stable-diffusion-inpainting",
    revision="fp16",
    torch_dtype=torch.float16,
    safety_checker=None,
)
pipe_inpaint = pipe_inpaint.to("cuda")

## Good params for editing that we used all over the paper --> decent quality and speed   
GUIDANCE_SCALE = 7.5
NUM_INFERENCE_STEPS = 100
DEFAULT_SEED = 1234

def pgd(X, targets, model, criterion, eps=0.1, step_size=0.015, iters=40, clamp_min=0, clamp_max=1, mask=None):
    X_adv = X.clone().detach() + (torch.rand(*X.shape)*2*eps-eps).cuda()
    pbar = tqdm(range(iters))
    for i in pbar:
        actual_step_size = step_size - (step_size - step_size / 100) / iters * i  
        X_adv.requires_grad_(True)

        loss = (model(X_adv).latent_dist.mean - targets).norm()
        pbar.set_description(f"Loss {loss.item():.5f} | step size: {actual_step_size:.4}")

        grad, = torch.autograd.grad(loss, [X_adv])
        
        X_adv = X_adv - grad.detach().sign() * actual_step_size
        X_adv = torch.minimum(torch.maximum(X_adv, X - eps), X + eps)
        X_adv.data = torch.clamp(X_adv, min=clamp_min, max=clamp_max)
        X_adv.grad = None    
        
        if mask is not None:
            X_adv.data *= mask
            
    return X_adv

def get_target():
    target_url = 'https://www.rtings.com/images/test-materials/2015/204_Gray_Uniformity.png'
    response = requests.get(target_url)
    target_image = Image.open(BytesIO(response.content)).convert("RGB")
    target_image = target_image.resize((512, 512))
    return target_image

def immunize_fn(init_image, mask_image):
    with torch.autocast('cuda'):
        mask, X = prepare_mask_and_masked_image(init_image, mask_image)
        X = X.half().cuda()
        mask = mask.half().cuda()

        targets = pipe_inpaint.vae.encode(preprocess(get_target()).half().cuda()).latent_dist.mean

        adv_X = pgd(X, 
                    targets = targets,
                    model=pipe_inpaint.vae.encode, 
                    criterion=torch.nn.MSELoss(), 
                    clamp_min=-1, 
                    clamp_max=1,
                    eps=0.12, 
                    step_size=0.01, 
                    iters=200,
                    mask=1-mask
                   )

        adv_X = (adv_X / 2 + 0.5).clamp(0, 1)
        
        adv_image = topil(adv_X[0]).convert("RGB")
        adv_image = recover_image(adv_image, init_image, mask_image, background=True)
        return adv_image        

def run(image, prompt, seed, guidance_scale, num_inference_steps, immunize=False):
    if seed == '':
        seed = DEFAULT_SEED
    else:
        seed = int(seed)
    torch.manual_seed(seed)

    init_image = Image.fromarray(image['image'])
    init_image = resize_and_crop(init_image, (512,512))
    mask_image = ImageOps.invert(Image.fromarray(image['mask']).convert('RGB'))
    mask_image = resize_and_crop(mask_image, init_image.size)
    
    if immunize:
        immunized_image = immunize_fn(init_image, mask_image)
        
    image_edited = pipe_inpaint(prompt=prompt, 
                         image=init_image if not immunize else immunized_image, 
                         mask_image=mask_image, 
                         height = init_image.size[0],
                         width = init_image.size[1],
                         eta=1,
                         guidance_scale=guidance_scale,
                         num_inference_steps=num_inference_steps,
                        ).images[0]
        
    image_edited = recover_image(image_edited, init_image, mask_image)
    
    if immunize:
        return [(immunized_image, 'Immunized Image'), (image_edited, 'Edited After Immunization')]
    else:
        return [(image_edited, 'Edited Image (Without Immunization)')]


description='''<u>Official</u> demo of our paper: <br>
**Raising the Cost of Malicious AI-Powered Image Editing** <br>
*[Hadi Salman](https://twitter.com/hadisalmanX), [Alaa Khaddaj](https://twitter.com/Alaa_Khaddaj), [Guillaume Leclerc](https://twitter.com/gpoleclerc), [Andrew Ilyas](https://twitter.com/andrew_ilyas), [Aleksander Madry](https://twitter.com/aleks_madry)* <br>
MIT &nbsp;&nbsp;[Paper](https://arxiv.org/abs/2302.06588) 
&nbsp;&nbsp;[Blog post](https://gradientscience.org/photoguard/) 
&nbsp;&nbsp;[![](https://badgen.net/badge/icon/GitHub?icon=github&label)](https://github.com/MadryLab/photoguard)
<br />
Below you can test our (encoder attack) immunization method for making images resistant to manipulation by Stable Diffusion. This immunization process forces the model to perform unrealistic edits. See Section 5 in our paper for a discussion of the intended use cases for this primitive.
<br />
'''

examples_list = [
                    ['./images/hadi_and_trevor.jpg', 'man attending a wedding', '329357', GUIDANCE_SCALE, NUM_INFERENCE_STEPS],
                    ['./images/trevor_2.jpg', 'two men in prison', '329357', GUIDANCE_SCALE, NUM_INFERENCE_STEPS],
                    ['./images/elon_2.jpg', 'man in a metro station', '214213', GUIDANCE_SCALE, NUM_INFERENCE_STEPS],
                ]


with gr.Blocks() as demo:
    gr.HTML(value="""<h1 style="font-weight: 900; margin-bottom: 7px; margin-top: 5px;">
            Interactive Demo: Immunize your Photos Against AI-Powered Malicious Manipulation </h1><br>
        """)
    gr.Markdown(description)
    with gr.Accordion(label='How to use (step by step):', open=False):
        gr.Markdown('''
            + Upload an image (or select from the examples below)
            + Use the brush to mask the parts of the image you want to keep unedited (e.g., faces of people)
            + Add a prompt to guide the edit (see examples below)
            + Play with the seed and click submit until you get a realistic edit that you are happy with (we provided good example seeds for you below)

            *Now let's immunize your image and try again:*
            + Click on the "Immunize" button, then submit.
            + You will get an immunized version of the image (which should look essentially identical to the original one) as well as its edited version (which should now look rather unrealistic)
        ''')

    with gr.Accordion(label='Example (video):', open=False):
        gr.HTML('''
            <center>
            <iframe width="920" height="600" src="https://www.youtube.com/embed/aTC59Q6ZDNM">
            allowfullscreen="allowfullscreen" frameborder="0">
            </iframe>
            </center>
        '''
        )

    with gr.Row():  
        with gr.Column():
            imgmask = gr.ImageMask(label='Drawing tool to mask regions you want to keep, e.g. faces')
            prompt = gr.Textbox(label='Prompt', placeholder='A photo of a man in a wedding')
            seed = gr.Textbox(label='Seed (Change to get different edits)', placeholder=str(DEFAULT_SEED), visible=True)
            with gr.Accordion("Advanced Options", open=False):
                scale = gr.Slider(label="Guidance Scale", minimum=0.1, maximum=25.0, value=GUIDANCE_SCALE, step=0.1)
                num_steps = gr.Slider(label="Number of Inference Steps", minimum=10, maximum=250, value=NUM_INFERENCE_STEPS, step=5)
            immunize = gr.Checkbox(label='Immunize', value=False)
            b1 = gr.Button('Submit')
        with gr.Column():
            genimages = gr.Gallery(label="Generated images", 
                       show_label=False, 
                       elem_id="gallery").style(grid=[1,2], height="auto")
    b1.click(run, [imgmask, prompt, seed, scale, num_steps, immunize], [genimages])
    examples = gr.Examples(examples=examples_list,inputs = [imgmask, prompt, seed, scale, num_steps, immunize],  outputs=[genimages], cache_examples=False, fn=run)


demo.launch()
# demo.launch(server_name='0.0.0.0', share=True, server_port=7860, inline=False)