Add app.py and examples

README.md

@@ -1,14 +1,14 @@
 ---
 title: SuSy
-emoji: ⚡
-colorFrom: yellow
-colorTo: green
+emoji: 🔎
+colorFrom: gray
+colorTo: pink
 sdk: gradio
 sdk_version: 4.44.1
 app_file: app.py
-pinned: false
+pinned: true
 license: apache-2.0
 short_description: Spot AI-Generated images with SuSy!
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+SuSy is a Spatial-Based Synthetic Image Detection and Recognition Model, designed and trained to detect synthetic images and attribute them to a generative model (i.e., two StableDiffusion models, two Midjourney versions and DALL·E 3)

SuSy.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:fa10fae300ee2742c7a373b6c3332c2595b461954b8f5616d2d382ef2751020e
+size 50810392

app.py

@@ -0,0 +1,95 @@
+import gradio as gr
+import numpy as np
+import torch
+from PIL import Image
+from skimage.feature import graycomatrix, graycoprops
+from torchvision import transforms
+
+# Load the model
+model = torch.jit.load("SuSy.pt")
+
+def process_image(image):
+    # Set Parameters
+    top_k_patches = 5
+    patch_size = 224
+
+    # Get the image dimensions
+    width, height = image.size
+
+    # Calculate the number of patches
+    num_patches_x = width // patch_size
+    num_patches_y = height // patch_size
+
+    # Divide the image in patches
+    patches = np.zeros((num_patches_x * num_patches_y, patch_size, patch_size, 3), dtype=np.uint8)
+    for i in range(num_patches_x):
+        for j in range(num_patches_y):
+            x = i * patch_size
+            y = j * patch_size
+            patch = image.crop((x, y, x + patch_size, y + patch_size))
+            patches[i * num_patches_y + j] = np.array(patch)
+
+    # Compute the most relevant patches (optional)
+    dissimilarity_scores = []
+    for patch in patches:
+        transform_patch = transforms.Compose([transforms.PILToTensor(), transforms.Grayscale()])
+        grayscale_patch = transform_patch(Image.fromarray(patch)).squeeze(0)
+        glcm = graycomatrix(grayscale_patch, [5], [0], 256, symmetric=True, normed=True)
+        dissimilarity_scores.append(graycoprops(glcm, "contrast")[0, 0])
+
+    # Sort patch indices by their dissimilarity score
+    sorted_indices = np.argsort(dissimilarity_scores)[::-1]
+
+    # Extract top k patches and convert them to tensor
+    top_patches = patches[sorted_indices[:top_k_patches]]
+    top_patches = torch.from_numpy(np.transpose(top_patches, (0, 3, 1, 2))) / 255.0
+
+    # Predict patches
+    model.eval()
+    with torch.no_grad():
+        preds = model(top_patches)
+
+    # Process results
+    classes = ['Authentic', 'DALL·E 3', 'Stable Diffusion 1.x', 'MJ V5/V6', 'MJ V1/V2', 'Stable Diffusion XL']
+    mean_probs = preds.mean(dim=0).numpy()
+    
+    # Create a dictionary of class probabilities
+    class_probs = {cls: prob for cls, prob in zip(classes, mean_probs)}
+    
+    # Sort probabilities in descending order
+    sorted_probs = dict(sorted(class_probs.items(), key=lambda item: item[1], reverse=True))
+
+    return sorted_probs
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=process_image,
+    inputs=gr.Image(type="pil"),
+    outputs=gr.Label(num_top_classes=6),
+    title="SuSy: Synthetic Image Detector",
+    description="Upload an image or select an example to classify it into different categories.",
+    examples=[
+        ["example_authentic.jpg"],
+        ["example_dalle3.jpg"],
+        ["example_mjv5.jpg"],
+        ["example_sdxl.jpg"]
+    ],
+    article="""
+    <div style="text-align: center;">
+        <h3>About SuSy</h3>
+        <p>SuSy is an advanced synthetic image detector that can distinguish between authentic images and various types of AI-generated images. It analyzes patches of the input image to make its classification.</p>
+        <h4>Categories:</h4>
+        <ul style="list-style-type: none; padding: 0;">
+            <li>Authentic: Real, non-AI-generated images</li>
+            <li>DALL·E 3: Images generated by DALL-E 3</li>
+            <li>MJ V1/V2: Images generated by Midjourney versions 1 or 2</li>
+            <li>MJ V5/V6: Images generated by Midjourney versions 5 or 6</li>
+            <li>Stable Diffusion 1.x: Images generated by Stable Diffusion 1.x Models</li>
+            <li>Stable Diffusion XL: Images generated by Stable Diffusion XL</li>
+        </ul>
+    </div>
+    """
+)
+
+# Launch the interface
+iface.launch()

config.json

@@ -0,0 +1,8 @@
+{
+    "model_architecture": "ResNet18",
+    "num_classes": 6,
+    "input_size": [224, 224],
+    "pretrained": true,
+    "learning_rate": 0.0001,
+    "batch_size": 256
+  }

requirements.txt

@@ -0,0 +1,5 @@
+torch
+torchvision
+pillow
+scikit-image
+gradio