import streamlit as st import torch import torch.nn as nn from torch.utils.data import DataLoader from torchvision import transforms from transformers import CLIPModel from transformers.models.clip import CLIPModel from PIL import Image import numpy as np import io import base64 import cv2 import matplotlib.pyplot as plt from peft import PeftModel from unsloth import FastVisionModel import os import tempfile import warnings warnings.filterwarnings("ignore", category=UserWarning) # App title and description st.set_page_config( page_title="Deepfake Analyzer", layout="wide", page_icon="🔍" ) # Main title and description st.title("Advanced Deepfake Image Analyzer") st.markdown("Analyze images for deepfake manipulation with multi-stage analysis") # Check for GPU availability def check_gpu(): if torch.cuda.is_available(): gpu_info = torch.cuda.get_device_properties(0) st.sidebar.success(f"✅ GPU available: {gpu_info.name} ({gpu_info.total_memory / (1024**3):.2f} GB)") return True else: st.sidebar.warning("⚠️ No GPU detected. Analysis will be slower.") return False # Sidebar components st.sidebar.title("Options") # Temperature slider temperature = st.sidebar.slider( "Temperature", min_value=0.1, max_value=1.0, value=0.7, step=0.1, help="Higher values make output more random, lower values more deterministic" ) # Max response length slider max_tokens = st.sidebar.slider( "Maximum Response Length", min_value=100, max_value=1000, value=500, step=50, help="The maximum number of tokens in the response" ) # Custom instruction text area in sidebar custom_instruction = st.sidebar.text_area( "Custom Instructions (Advanced)", value="Focus on analyzing the highlighted regions from the GradCAM visualization. Examine facial inconsistencies, lighting irregularities, and other artifacts visible in the heat map.", help="Add specific instructions for the LLM analysis" ) # About section in sidebar st.sidebar.markdown("---") st.sidebar.subheader("About") st.sidebar.markdown(""" This analyzer performs multi-stage detection: 1. **Initial Detection**: CLIP-based classifier 2. **GradCAM Visualization**: Highlights suspicious regions 3. **LLM Analysis**: Fine-tuned Llama 3.2 Vision provides detailed explanations The system looks for: - Facial inconsistencies - Unnatural movements - Lighting issues - Texture anomalies - Edge artifacts - Blending problems """) # ----- GradCAM Implementation ----- class ImageDataset(torch.utils.data.Dataset): def __init__(self, image, transform=None, face_only=True, dataset_name=None): self.image = image self.transform = transform self.face_only = face_only self.dataset_name = dataset_name # Load face detector self.face_detector = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml') def __len__(self): return 1 # Only one image def detect_face(self, image_np): """Detect face in image and return the face region""" gray = cv2.cvtColor(image_np, cv2.COLOR_RGB2GRAY) faces = self.face_detector.detectMultiScale(gray, 1.1, 5) # If no face is detected, use the whole image if len(faces) == 0: st.info("No face detected, using whole image for analysis") h, w = image_np.shape[:2] return (0, 0, w, h), image_np # Get the largest face if len(faces) > 1: # Choose the largest face by area areas = [w*h for (x, y, w, h) in faces] largest_idx = np.argmax(areas) x, y, w, h = faces[largest_idx] else: x, y, w, h = faces[0] # Add padding around the face (5% on each side) padding_x = int(w * 0.05) padding_y = int(h * 0.05) # Ensure padding doesn't go outside image bounds x1 = max(0, x - padding_x) y1 = max(0, y - padding_y) x2 = min(image_np.shape[1], x + w + padding_x) y2 = min(image_np.shape[0], y + h + padding_y) # Extract the face region face_img = image_np[y1:y2, x1:x2] return (x1, y1, x2-x1, y2-y1), face_img def __getitem__(self, idx): image_np = np.array(self.image) label = 0 # Default label; will be overridden by prediction # Store original image for visualization original_image = self.image.copy() # Detect face if required if self.face_only: face_box, face_img_np = self.detect_face(image_np) face_img = Image.fromarray(face_img_np) # Apply transform to face image if self.transform: face_tensor = self.transform(face_img) else: face_tensor = transforms.ToTensor()(face_img) return face_tensor, label, "uploaded_image", original_image, face_box, self.dataset_name else: # Process the whole image if self.transform: image_tensor = self.transform(self.image) else: image_tensor = transforms.ToTensor()(self.image) return image_tensor, label, "uploaded_image", original_image, None, self.dataset_name class GradCAM: def __init__(self, model, target_layer): self.model = model self.target_layer = target_layer self.gradients = None self.activations = None self._register_hooks() def _register_hooks(self): def forward_hook(module, input, output): if isinstance(output, tuple): self.activations = output[0] else: self.activations = output def backward_hook(module, grad_in, grad_out): if isinstance(grad_out, tuple): self.gradients = grad_out[0] else: self.gradients = grad_out layer = dict([*self.model.named_modules()])[self.target_layer] layer.register_forward_hook(forward_hook) layer.register_backward_hook(backward_hook) def generate(self, input_tensor, class_idx): self.model.zero_grad() try: # Use only the vision part of the model for gradient calculation vision_outputs = self.model.vision_model(pixel_values=input_tensor) # Get the pooler output features = vision_outputs.pooler_output # Create a dummy gradient for the feature based on the class idx one_hot = torch.zeros_like(features) one_hot[0, class_idx] = 1 # Manually backpropagate features.backward(gradient=one_hot) # Check for None values if self.gradients is None or self.activations is None: st.warning("Warning: Gradients or activations are None. Using fallback CAM.") return np.ones((14, 14), dtype=np.float32) * 0.5 # Process gradients and activations for transformer-based model gradients = self.gradients.cpu().detach().numpy() activations = self.activations.cpu().detach().numpy() if len(activations.shape) == 3: # [batch, sequence_length, hidden_dim] seq_len = activations.shape[1] # CLIP ViT typically has 196 patch tokens (14×14) + 1 class token = 197 if seq_len >= 197: # Skip the class token (first token) and reshape the patch tokens into a square patch_tokens = activations[0, 1:197, :] # Remove the class token # Take the mean across the hidden dimension token_importance = np.mean(np.abs(patch_tokens), axis=1) # Reshape to the expected grid size (14×14 for CLIP ViT) cam = token_importance.reshape(14, 14) else: # Try to find factors close to a square side_len = int(np.sqrt(seq_len)) # Use the mean across features as importance token_importance = np.mean(np.abs(activations[0]), axis=1) # Create as square-like shape as possible cam = np.zeros((side_len, side_len)) # Fill the cam with available values flat_cam = cam.flatten() flat_cam[:min(len(token_importance), len(flat_cam))] = token_importance[:min(len(token_importance), len(flat_cam))] cam = flat_cam.reshape(side_len, side_len) else: # Fallback st.info("Using fallback CAM shape (14x14)") cam = np.ones((14, 14), dtype=np.float32) * 0.5 # Default fallback # Ensure we have valid values cam = np.maximum(cam, 0) if np.max(cam) > 0: cam = cam / np.max(cam) return cam except Exception as e: st.error(f"Error in GradCAM.generate: {str(e)}") return np.ones((14, 14), dtype=np.float32) * 0.5 def overlay_cam_on_image(image, cam, face_box=None, alpha=0.5): """Overlay the CAM on the image""" if face_box is not None: x, y, w, h = face_box # Create a mask for the entire image (all zeros initially) img_np = np.array(image) full_h, full_w = img_np.shape[:2] full_cam = np.zeros((full_h, full_w), dtype=np.float32) # Resize CAM to match face region face_cam = cv2.resize(cam, (w, h)) # Copy the face CAM into the full image CAM at the face position full_cam[y:y+h, x:x+w] = face_cam # Convert full CAM to image cam_resized = Image.fromarray((full_cam * 255).astype(np.uint8)) cam_colormap = plt.cm.jet(np.array(cam_resized) / 255.0)[:, :, :3] # Apply colormap cam_colormap = (cam_colormap * 255).astype(np.uint8) else: # Resize CAM to match image dimensions img_np = np.array(image) h, w = img_np.shape[:2] cam_resized = cv2.resize(cam, (w, h)) # Apply colormap cam_colormap = plt.cm.jet(cam_resized)[:, :, :3] # Apply colormap cam_colormap = (cam_colormap * 255).astype(np.uint8) # Blend the original image with the colormap img_np_float = img_np.astype(float) / 255.0 cam_colormap_float = cam_colormap.astype(float) / 255.0 blended = img_np_float * (1 - alpha) + cam_colormap_float * alpha blended = (blended * 255).astype(np.uint8) return Image.fromarray(blended) def save_comparison(image, cam, overlay, face_box=None): """Create a side-by-side comparison of the original, CAM, and overlay""" fig, axes = plt.subplots(1, 3, figsize=(15, 5)) # Original Image axes[0].imshow(image) axes[0].set_title("Original") if face_box is not None: x, y, w, h = face_box rect = plt.Rectangle((x, y), w, h, edgecolor='lime', linewidth=2, fill=False) axes[0].add_patch(rect) axes[0].axis("off") # CAM if face_box is not None: # Create a full image CAM that highlights only the face img_np = np.array(image) h, w = img_np.shape[:2] full_cam = np.zeros((h, w)) x, y, fw, fh = face_box # Resize CAM to face size face_cam = cv2.resize(cam, (fw, fh)) # Place it in the right position full_cam[y:y+fh, x:x+fw] = face_cam axes[1].imshow(full_cam, cmap="jet") else: cam_resized = cv2.resize(cam, (image.width, image.height)) axes[1].imshow(cam_resized, cmap="jet") axes[1].set_title("CAM") axes[1].axis("off") # Overlay axes[2].imshow(overlay) axes[2].set_title("Overlay") axes[2].axis("off") plt.tight_layout() # Convert plot to PIL Image for Streamlit display buf = io.BytesIO() plt.savefig(buf, format="png", bbox_inches="tight") plt.close() buf.seek(0) return Image.open(buf) # Function to load GradCAM CLIP model @st.cache_resource def load_clip_model(): with st.spinner("Loading CLIP model for GradCAM..."): model = CLIPModel.from_pretrained("openai/clip-vit-large-patch14") # Apply a simple classification head model.classification_head = nn.Linear(1024, 2) model.classification_head.weight.data.normal_(mean=0.0, std=0.02) model.classification_head.bias.data.zero_() model.eval() return model def get_target_layer_clip(model): """Get the target layer for GradCAM""" return "vision_model.encoder.layers.23" def process_image_with_gradcam(image, model, device, pred_class): """Process an image with GradCAM""" # Set up transformations transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]), ]) # Create dataset for the single image dataset = ImageDataset(image, transform=transform, face_only=True) # Custom collate function def custom_collate(batch): tensors = [item[0] for item in batch] labels = [item[1] for item in batch] paths = [item[2] for item in batch] images = [item[3] for item in batch] face_boxes = [item[4] for item in batch] dataset_names = [item[5] for item in batch] tensors = torch.stack(tensors) labels = torch.tensor(labels) return tensors, labels, paths, images, face_boxes, dataset_names # Create dataloader dataloader = DataLoader(dataset, batch_size=1, shuffle=False, collate_fn=custom_collate) # Extract the batch for batch in dataloader: input_tensor, label, img_paths, original_images, face_boxes, dataset_names = batch original_image = original_images[0] face_box = face_boxes[0] # Move tensors and model to device input_tensor = input_tensor.to(device) model = model.to(device) try: # Create GradCAM extractor target_layer = get_target_layer_clip(model) cam_extractor = GradCAM(model, target_layer) # Generate CAM cam = cam_extractor.generate(input_tensor, pred_class) # Create visualizations overlay = overlay_cam_on_image(original_image, cam, face_box) comparison = save_comparison(original_image, cam, overlay, face_box) # Return results return cam, overlay, comparison, face_box except Exception as e: st.error(f"Error processing image with GradCAM: {str(e)}") # Return default values default_cam = np.ones((14, 14), dtype=np.float32) * 0.5 overlay = overlay_cam_on_image(original_image, default_cam, face_box) comparison = save_comparison(original_image, default_cam, overlay, face_box) return default_cam, overlay, comparison, face_box # ----- Fine-tuned Vision LLM ----- # Function to fix cross-attention masks def fix_cross_attention_mask(inputs): if 'cross_attention_mask' in inputs and 0 in inputs['cross_attention_mask'].shape: batch_size, seq_len, _, num_tiles = inputs['cross_attention_mask'].shape visual_features = 6404 # Critical dimension new_mask = torch.ones((batch_size, seq_len, visual_features, num_tiles), device=inputs['cross_attention_mask'].device) inputs['cross_attention_mask'] = new_mask st.success("Fixed cross-attention mask dimensions") return inputs # Load model function @st.cache_resource def load_llm_model(): with st.spinner("Loading LLM vision model... This may take a few minutes. Please be patient..."): try: # Check for GPU has_gpu = check_gpu() # Load base model and tokenizer using Unsloth base_model_id = "unsloth/llama-3.2-11b-vision-instruct" model, tokenizer = FastVisionModel.from_pretrained( base_model_id, load_in_4bit=True, ) # Load the adapter adapter_id = "saakshigupta/deepfake-explainer-1" model = PeftModel.from_pretrained(model, adapter_id) # Set to inference mode FastVisionModel.for_inference(model) return model, tokenizer except Exception as e: st.error(f"Error loading model: {str(e)}") return None, None # Analyze image function def analyze_image_with_llm(image, gradcam_overlay, face_box, pred_label, confidence, question, model, tokenizer, temperature=0.7, max_tokens=500, custom_instruction=""): # Create a prompt that includes GradCAM information if custom_instruction.strip(): full_prompt = f"{question}\n\nThe image has been processed with GradCAM and classified as {pred_label} with confidence {confidence:.2f}. Focus on the highlighted regions in red/yellow which show the areas the detection model found suspicious.\n\n{custom_instruction}" else: full_prompt = f"{question}\n\nThe image has been processed with GradCAM and classified as {pred_label} with confidence {confidence:.2f}. Focus on the highlighted regions in red/yellow which show the areas the detection model found suspicious." # Format the message to include both the original image and the GradCAM visualization messages = [ {"role": "user", "content": [ {"type": "image", "image": image}, # Original image {"type": "image", "image": gradcam_overlay}, # GradCAM overlay {"type": "text", "text": full_prompt} ]} ] # Apply chat template input_text = tokenizer.apply_chat_template(messages, add_generation_prompt=True) # Process with image inputs = tokenizer( [image, gradcam_overlay], # Send both images input_text, add_special_tokens=False, return_tensors="pt", ).to(model.device) # Fix cross-attention mask if needed inputs = fix_cross_attention_mask(inputs) # Generate response with st.spinner("Generating detailed analysis... (this may take 15-30 seconds)"): with torch.no_grad(): output_ids = model.generate( **inputs, max_new_tokens=max_tokens, use_cache=True, temperature=temperature, top_p=0.9 ) # Decode the output response = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Try to extract just the model's response (after the prompt) if full_prompt in response: result = response.split(full_prompt)[-1].strip() else: result = response return result # Main app def main(): # Create placeholders for model state if 'clip_model_loaded' not in st.session_state: st.session_state.clip_model_loaded = False st.session_state.clip_model = None if 'llm_model_loaded' not in st.session_state: st.session_state.llm_model_loaded = False st.session_state.llm_model = None st.session_state.tokenizer = None # Create expanders for each stage with st.expander("Stage 1: Model Loading", expanded=True): # Button for loading CLIP model clip_col, llm_col = st.columns(2) with clip_col: if not st.session_state.clip_model_loaded: if st.button("📥 Load CLIP Model for Detection", type="primary"): # Load CLIP model model = load_clip_model() if model is not None: st.session_state.clip_model = model st.session_state.clip_model_loaded = True st.success("✅ CLIP model loaded successfully!") else: st.error("❌ Failed to load CLIP model.") else: st.success("✅ CLIP model loaded and ready!") with llm_col: if not st.session_state.llm_model_loaded: if st.button("📥 Load Vision LLM for Analysis", type="primary"): # Load LLM model model, tokenizer = load_llm_model() if model is not None and tokenizer is not None: st.session_state.llm_model = model st.session_state.tokenizer = tokenizer st.session_state.llm_model_loaded = True st.success("✅ Vision LLM loaded successfully!") else: st.error("❌ Failed to load Vision LLM.") else: st.success("✅ Vision LLM loaded and ready!") # Image upload section with st.expander("Stage 2: Image Upload & Initial Detection", expanded=True): st.subheader("Upload an Image") uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"]) if uploaded_file is not None: # Display the uploaded image image = Image.open(uploaded_file).convert("RGB") st.image(image, caption="Uploaded Image", use_column_width=True) # Detect with CLIP model if loaded if st.session_state.clip_model_loaded: with st.spinner("Analyzing image with CLIP model..."): # Preprocess image for CLIP transform = transforms.Compose([ transforms.Resize((224, 224)), transforms.ToTensor(), transforms.Normalize(mean=[0.48145466, 0.4578275, 0.40821073], std=[0.26862954, 0.26130258, 0.27577711]), ]) # Create a simple dataset for the image dataset = ImageDataset(image, transform=transform, face_only=True) tensor, _, _, _, face_box, _ = dataset[0] tensor = tensor.unsqueeze(0) # Get device device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # Move model and tensor to device model = st.session_state.clip_model.to(device) tensor = tensor.to(device) # Forward pass with torch.no_grad(): outputs = model.vision_model(pixel_values=tensor).pooler_output logits = model.classification_head(outputs) probs = torch.softmax(logits, dim=1)[0] pred_class = torch.argmax(probs).item() confidence = probs[pred_class].item() pred_label = "Fake" if pred_class == 1 else "Real" # Display results result_col1, result_col2 = st.columns(2) with result_col1: st.metric("Prediction", pred_label) with result_col2: st.metric("Confidence", f"{confidence:.2%}") # GradCAM visualization st.subheader("GradCAM Visualization") cam, overlay, comparison, detected_face_box = process_image_with_gradcam( image, model, device, pred_class ) # Display GradCAM results st.image(comparison, caption="Original | CAM | Overlay", use_column_width=True) # Save results in session state for LLM analysis st.session_state.current_image = image st.session_state.current_overlay = overlay st.session_state.current_face_box = detected_face_box st.session_state.current_pred_label = pred_label st.session_state.current_confidence = confidence st.success("✅ Initial detection and GradCAM visualization complete!") else: st.warning("⚠️ Please load the CLIP model first to perform initial detection.") # LLM Analysis section with st.expander("Stage 3: Detailed Analysis with Vision LLM", expanded=False): if hasattr(st.session_state, 'current_image') and st.session_state.llm_model_loaded: st.subheader("Detailed Deepfake Analysis") # Default question with option to customize default_question = f"This image has been classified as {st.session_state.current_pred_label}. Analyze the key features that led to this classification, focusing on the highlighted areas in the GradCAM visualization. Provide both a technical explanation for experts and a simple explanation for non-technical users." question = st.text_area("Question/Prompt:", value=default_question, height=100) # Analyze button if st.button("🔍 Perform Detailed Analysis", type="primary"): result = analyze_image_with_llm( st.session_state.current_image, st.session_state.current_overlay, st.session_state.current_face_box, st.session_state.current_pred_label, st.session_state.current_confidence, question, st.session_state.llm_model, st.session_state.tokenizer, temperature=temperature, max_tokens=max_tokens, custom_instruction=custom_instruction ) # Display results st.success("✅ Analysis complete!") # Check if the result contains both technical and non-technical explanations if "Technical" in result and "Non-Technical" in result: # Split the result into technical and non-technical sections parts = result.split("Non-Technical") technical = parts[0] non_technical = "Non-Technical" + parts[1] # Display in two columns col1, col2 = st.columns(2) with col1: st.subheader("Technical Analysis") st.markdown(technical) with col2: st.subheader("Simple Explanation") st.markdown(non_technical) else: # Just display the whole result st.subheader("Analysis Result") st.markdown(result) elif not hasattr(st.session_state, 'current_image'): st.warning("⚠️ Please upload an image and complete the initial detection first.") else: st.warning("⚠️ Please load the Vision LLM to perform detailed analysis.") # Footer st.markdown("---") st.caption("Advanced Deepfake Image Analyzer") if __name__ == "__main__": main()