import os
import random
from dataclasses import dataclass
from threading import Lock
from typing import List, Optional
import gradio as gr
import numpy as np
import torch
from PIL import Image
from skimage.feature import graycomatrix, graycoprops
from torchvision import transforms
NUM_ROUNDS = 10
PROB_THRESHOLD = 0.3
# Load the model
model = torch.jit.load("SuSy.pt")
@dataclass
class GameResults:
user_guess: str
model_guess: str
correct_answer: str
class GameState:
def __init__(self):
self.lock = Lock()
self._reset()
def _reset(self):
"""Internal reset method - should be called within a lock"""
self.user_score = 0
self.model_score = 0
self.current_round = 0
self.total_rounds = NUM_ROUNDS
self.game_images: List[str] = []
self.is_game_active = False
self.last_results: Optional[GameResults] = None
self.processing_submission = False
def reset(self):
"""Public reset method with lock protection"""
with self.lock:
self._reset()
def start_new_game(self) -> tuple[bool, Optional[str]]:
"""
Starts a new game and returns (success, first_image_path)
"""
with self.lock:
if self.is_game_active:
return False, None
try:
self._reset()
self.game_images = load_images()
if not self.game_images:
return False, None
self.is_game_active = True
return True, self.game_images[0]
except Exception as e:
print(f"Error starting new game: {e}")
self._reset()
return False, None
def can_submit_guess(self) -> bool:
with self.lock:
return (
self.is_game_active and
not self.processing_submission and
self.current_round < self.total_rounds and
len(self.game_images) > self.current_round
)
def start_submission(self) -> bool:
with self.lock:
if not self.can_submit_guess():
return False
self.processing_submission = True
return True
def finish_submission(self, results: GameResults):
with self.lock:
if results.user_guess == results.correct_answer:
self.user_score += 1
if results.model_guess == results.correct_answer:
self.model_score += 1
self.last_results = results
self.current_round += 1
self.processing_submission = False
if self.current_round >= self.total_rounds:
self.is_game_active = False
def get_current_image(self) -> Optional[str]:
with self.lock:
if not self.is_game_active or self.current_round >= len(self.game_images):
return None
return self.game_images[self.current_round]
def get_game_state(self):
"""Get a snapshot of the current game state"""
with self.lock:
return {
'is_active': self.is_game_active,
'current_round': self.current_round,
'total_rounds': self.total_rounds,
'user_score': self.user_score,
'model_score': self.model_score,
'last_results': self.last_results
}
def get_game_over_message(self) -> str:
with self.lock:
if self.user_score > self.model_score:
return """
🎉 Congratulations! You won! 🎉
You've outperformed SuSy in detecting AI-generated images.
Click 'Start New Game' to play again.
"""
elif self.user_score < self.model_score:
return """
Better luck next time! SuSy won this round.
Keep practicing to improve your detection skills.
Click 'Start New Game' to try again.
"""
else:
return """
It's a tie! You matched SuSy's performance!
You're getting good at this.
Click 'Start New Game' to play again.
"""
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
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
def load_images():
real_image_folder = "real_images"
fake_image_folder = "fake_images"
real_images = [os.path.join(real_image_folder, img) for img in os.listdir(real_image_folder)]
fake_images = [os.path.join(fake_image_folder, img) for img in os.listdir(fake_image_folder)]
selected_images = random.sample(real_images, NUM_ROUNDS // 2) + random.sample(fake_images, NUM_ROUNDS // 2)
random.shuffle(selected_images)
return selected_images
def create_score_html() -> str:
game_state_snapshot = game_state.get_game_state()
results_html = ""
if game_state_snapshot['last_results']:
results = game_state_snapshot['last_results']
results_html = f"""
Last Round Results:
Your guess: {results.user_guess}
Model's guess: {results.model_guess}
Correct answer: {results.correct_answer}
"""
current_display_round = min(game_state_snapshot['current_round'] + 1, game_state_snapshot['total_rounds'])
return f"""
Score Board
You
{game_state_snapshot['user_score']}
AI Model
{game_state_snapshot['model_score']}
Round: {current_display_round}/{game_state_snapshot['total_rounds']}
{results_html}
"""
game_state = GameState()
def start_game():
"""Initialize a new game"""
success, first_image_path = game_state.start_new_game()
if not success or not first_image_path:
print("Failed to start new game")
return [gr.update()] * 6
try:
current_image = Image.open(first_image_path)
return (
gr.update(value=current_image, visible=True),
gr.update(visible=False),
gr.update(visible=True, interactive=True),
gr.update(visible=True, interactive=True),
create_score_html(),
gr.update(visible=False)
)
except Exception as e:
print(f"Error starting game: {e}")
game_state.reset()
return [gr.update()] * 6
def submit_guess(user_guess: str):
"""Handle user guess submission"""
if not game_state.can_submit_guess():
return [gr.update()] * 6
if not game_state.start_submission():
return [gr.update()] * 6
try:
current_image_path = game_state.get_current_image()
if not current_image_path:
game_state.processing_submission = False
return [gr.update()] * 6
current_image = Image.open(current_image_path)
model_prediction = process_image(current_image)
model_guess = "Real" if model_prediction['Authentic'] > PROB_THRESHOLD else "Fake"
correct_answer = "Real" if "real_images" in current_image_path else "Fake"
results = GameResults(user_guess, model_guess, correct_answer)
game_state.finish_submission(results)
if not game_state.is_game_active:
return (
gr.update(value=None, visible=False),
gr.update(visible=True),
gr.update(visible=False),
gr.update(visible=False),
create_score_html(),
gr.update(visible=True, value=game_state.get_game_over_message())
)
next_image_path = game_state.get_current_image()
if not next_image_path:
return [gr.update()] * 6
next_image = Image.open(next_image_path)
return (
gr.update(value=next_image, visible=True),
gr.update(visible=False),
gr.update(visible=True, interactive=True),
gr.update(visible=True, interactive=True),
create_score_html(),
gr.update(visible=False)
)
except Exception as e:
print(f"Error processing guess: {e}")
game_state.processing_submission = False
return [gr.update()] * 6
# Custom CSS
custom_css = """
#game-container {
max-width: 1200px;
margin: 0 auto;
padding: 20px;
}
#start-button {
max-width: 200px;
margin: 0 auto;
}
#guess-buttons {
display: flex;
gap: 10px;
justify-content: center;
margin-top: 20px;
}
.guess-button {
min-width: 120px;
}
.image-container img {
max-height: 640px !important;
width: auto !important;
object-fit: contain !important;
}
"""
# Define Gradio interface
with gr.Blocks(css=custom_css) as iface:
with gr.Column(elem_id="game-container"):
gr.HTML("""
""")
with gr.Row():
with gr.Column(scale=2):
image_display = gr.Image(
type="pil",
label="Current Image",
interactive=False,
visible=False,
elem_classes=["image-container"]
)
with gr.Row(elem_id="guess-buttons"):
real_button = gr.Button(
"Real",
visible=False,
variant="primary",
elem_classes=["guess-button"]
)
fake_button = gr.Button(
"Fake",
visible=False,
variant="primary",
elem_classes=["guess-button"]
)
with gr.Column(scale=1):
score_display = gr.HTML()
with gr.Row():
with gr.Column(elem_id="start-button"):
start_button = gr.Button("Start New Game", variant="primary", size="sm")
feedback_display = gr.Markdown(visible=False)
# Event handlers
start_button.click(
fn=start_game,
outputs=[
image_display,
start_button,
real_button,
fake_button,
score_display,
feedback_display
]
)
real_button.click(
fn=lambda: submit_guess("Real"),
outputs=[
image_display,
start_button,
real_button,
fake_button,
score_display,
feedback_display
]
)
fake_button.click(
fn=lambda: submit_guess("Fake"),
outputs=[
image_display,
start_button,
real_button,
fake_button,
score_display,
feedback_display
]
)
# Launch the interface
iface.launch()
