inital commit

README.md

@@ -1,13 +1,14 @@
 ---
-title: Sumsub Ffs Demo
-emoji: 🌍
-colorFrom: purple
-colorTo: indigo
+title: >-
+  For Fake's Sake: a set of models for detecting deepfakes, generated images and
+  synthetic images
+emoji: 🐠
+colorFrom: green
+colorTo: blue
 sdk: gradio
 sdk_version: 3.39.0
 app_file: app.py
 pinned: false
-license: cc-by-sa-3.0
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,160 @@
+import os
+os.system("python -m pip install --upgrade pip")
+os.system("pip install git+https://github.com/rwightman/pytorch-image-models")
+os.system("pip install git+https://github.com/huggingface/huggingface_hub")
+
+import gradio as gr
+import timm
+import torch
+from torch import nn
+from torch.nn import functional as F
+import torchvision
+
+
+class Model200M(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = timm.create_model('convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384', pretrained=False,
+                                       num_classes=0)
+
+        self.clf = nn.Sequential(
+            nn.Linear(1536, 128),
+            nn.ReLU(inplace=True),
+            nn.Linear(128, 2))
+
+    def forward(self, image):
+        image_features = self.model(image)
+        return self.clf(image_features)
+
+
+class Model5M(torch.nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = timm.create_model('timm/tf_mobilenetv3_large_100.in1k', pretrained=False, num_classes=0)
+
+        self.clf = nn.Sequential(
+            nn.Linear(1280, 128),
+            nn.ReLU(inplace=True),
+            nn.Linear(128, 2))
+
+    def forward(self, image):
+        image_features = self.model(image)
+        return self.clf(image_features)
+
+def load_model(name: str):
+    model = Model200M() if "200M" in name else Model5M()
+    ckpt = torch.load(name, map_location=torch.device('cpu'))
+    model.load_state_dict(ckpt)
+    model.eval()
+    return model
+
+model_list = {
+    'midjourney_200M': load_model('models/midjourney200M.pt'),
+    'diffusions_200M': load_model('models/diffusions200M.pt'),
+    'midjourney_5M': load_model('models/midjourney5M.pt'),
+    'diffusions_5M': load_model('models/diffusions5M.pt')
+}
+
+tfm = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((640, 640)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                     std=[0.229, 0.224, 0.225]),
+])
+
+tfm_small = torchvision.transforms.Compose([
+    torchvision.transforms.Resize((224, 224)),
+    torchvision.transforms.ToTensor(),
+    torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406],
+                                     std=[0.229, 0.224, 0.225]),
+])
+
+
+def predict_from_model(model, img_1):
+    y = model.forward(img_1[None, ...])
+    y_1 = F.softmax(y, dim=1)[:, 1].cpu().detach().numpy()
+    y_2 = F.softmax(y, dim=1)[:, 0].cpu().detach().numpy()
+    return {'created by AI': y_1.tolist(),
+            'created by human': y_2.tolist()}
+
+
+def predict(raw_image, model_name):
+    img_1 = tfm(raw_image)
+    img_2 = tfm_small(raw_image)
+
+    if model_name not in model_list:
+        return {'error': [0.]}
+
+    model = model_list[model_name]
+    img = img_1 if "200M" in model_name else img_2
+    return predict_from_model(model, img)
+
+general_examples = [
+    ["images/general/img_1.jpg"],
+    ["images/general/img_2.jpg"],
+    ["images/general/img_3.jpg"],
+    ["images/general/img_4.jpg"],
+    ["images/general/img_5.jpg"],
+    ["images/general/img_6.jpg"],
+    ["images/general/img_7.jpg"],
+    ["images/general/img_8.jpg"],
+    ["images/general/img_9.jpg"],
+    ["images/general/img_10.jpg"],
+]
+
+optic_examples = [
+    ["images/optic/img_1.jpg"],
+    ["images/optic/img_2.jpg"],
+    ["images/optic/img_3.jpg"],
+    ["images/optic/img_4.jpg"],
+    ["images/optic/img_5.jpg"],
+]
+
+famous_deepfake_examples = [
+    ["images/famous_deepfakes/img_1.jpg"],
+    ["images/famous_deepfakes/img_2.jpg"],
+    ["images/famous_deepfakes/img_3.jpg"],
+    ["images/famous_deepfakes/img_4.webp"],
+]
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    gr.Markdown(
+        """
+        <h1 style="text-align: center;">For Fake's Sake: a set of models for detecting generated and synthetic images</h3>
+        This is a demo space for <a href='https://huggingface.co/Sumsub/aiornot'>synthetic image detectors</a>.
+
+        We provide several detectors for images generated by popular tools, such as Midjourney and Stable Diffusion.
+        """
+    )
+
+    with gr.Row():
+        with gr.Column():
+            image_input = gr.Image(type="pil")
+            drop_down = gr.Dropdown(model_list.keys(), type="value", label="Model", value="diffusions_200M")
+            with gr.Row():
+                gr.ClearButton(components=[image_input])
+                submit_button = gr.Button("Submit", variant="primary")
+        with gr.Column():
+            result_score = gr.Label(label='result', num_top_classes=2)
+    with gr.Tab("Examples"):
+        gr.Examples(examples=general_examples, inputs=image_input)
+    # with gr.Tab("More examples"):
+    #     gr.Examples(examples=optic_examples, inputs=image_input)
+    with gr.Tab("Widely known deepfakes"):
+        gr.Examples(examples=famous_deepfake_examples, inputs=image_input)
+
+    submit_button.click(predict, inputs=[image_input, drop_down], outputs=result_score)
+
+    gr.Markdown(
+        """
+        <h3>Models</h3>
+        <p><code>*_200M</code> models are based on <code>convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384</code> with image size <code>640x640</code></p>
+        <p><code>*_5M</code> models are based on <code>tf_mobilenetv3_large_100.in1k</code> with image size <code>224x224</code></p>
+
+        <h3>Details</h3>
+        <li>Model card: <a href='https://huggingface.co/Sumsub/aiornot'>aiornot</a></li>
+        <li>License: CC-By-SA-3.0</li>
+        """
+    )
+
+demo.launch()

models/diffusions200M.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:24e79ec2325e489dd83ca60f52e04aa214fdee81463b47e150ff09072c627169
+size 794891785

models/diffusions5M.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2ad16a05ba72f6a5fadae6a7788f2e0c370f02e24e7fc629f3fa526059c435b
+size 17339597

models/midjourney200M.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7a689a102e0f54967d2e670a0652bd1d440bc7874ae7cdce77e2a1ccc7ad3f0f
+size 794891785

models/midjourney5M.pt

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+oid sha256:0ece116e4c8e42c707d46e86574524d9eb1ac4f0e775cc377729a0bb64201e81
+size 17339597

requirements.txt

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+timm==0.9.5
+torch==2.0.1
+torchvision==0.15.2