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app.py

@@ -0,0 +1,104 @@
+import streamlit as st
+import torch
+import torch.nn as nn
+from PIL import Image
+from transformers import (
+    AutoTokenizer, AutoConfig, AutoModel, PreTrainedModel, 
+    pipeline, ViTImageProcessor, ViTForImageClassification
+)
+
+# --- DESKLIB TEXT DETECTOR ARCHITECTURE ---
+class DesklibAIDetectionModel(PreTrainedModel):
+    config_class = AutoConfig
+    # NEW: Add this line to satisfy the latest Transformers internal checks
+    _tied_weights_keys = {}
+
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = AutoModel.from_config(config)
+        self.classifier = nn.Linear(config.hidden_size, 1)
+        
+        # NEW: Always call post_init at the end of __init__
+        self._tied_weights_keys = {}
+        if not hasattr(self, "_keys_to_ignore_on_save"):
+            self._keys_to_ignore_on_save = []
+            
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask=None):
+        outputs = self.model(input_ids, attention_mask=attention_mask)
+        last_hidden_state = outputs[0]
+        input_mask_expanded = attention_mask.unsqueeze(-1).expand(last_hidden_state.size()).float()
+        mean_pooled = torch.sum(last_hidden_state * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
+        return self.classifier(mean_pooled)
+
+# --- LOAD SPECIALIZED MODELS ---
+@st.cache_resource
+def load_assets():
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    
+    # Text Model (Desklib)
+    text_model_id = "desklib/ai-text-detector-v1.01"
+    t_tokenizer = AutoTokenizer.from_pretrained(text_model_id)
+    t_model = DesklibAIDetectionModel.from_pretrained(text_model_id).to(device)
+    
+    # Image Model (Specialized ViT for AIGC)
+    img_model_id = "capcheck/ai-image-detection"
+    img_pipe = pipeline("image-classification", model=img_model_id, device=0 if device == "cuda" else -1)
+    
+    return t_tokenizer, t_model, img_pipe, device
+
+tokenizer, text_model, img_pipeline, device = load_assets()
+
+# --- UI INTERFACE ---
+st.set_page_config(page_title="AIGC Late Fusion Detector", layout="wide")
+st.title("🛡️ Specialized Multimodal AIGC Detector")
+
+col_in, col_out = st.columns([1, 1])
+
+with col_in:
+    st.subheader("Input Content")
+    uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+    user_text = st.text_area("Input Text", placeholder="Paste article or caption...", height=200)
+    
+    if uploaded_file:
+        st.image(Image.open(uploaded_file), caption="Uploaded Image", use_container_width=True)
+
+# --- PROCESSING ---
+if st.button("Run Multi-Modal Detection") and uploaded_file and user_text:
+    with st.spinner("Analyzing artifacts in text and pixels..."):
+        # 1. Text Score (Logit -> Sigmoid)
+        t_inputs = tokenizer(user_text, return_tensors="pt", truncation=True, padding=True).to(device)
+        with torch.no_grad():
+            t_logit = text_model(t_inputs['input_ids'], t_inputs['attention_mask'])
+            p_text = torch.sigmoid(t_logit).item()
+
+        # 2. Image Score (AIGC ViT)
+        img_results = img_pipeline(Image.open(uploaded_file))
+        # Find the score for 'FAKE' (AI-generated), case-insensitive, with safe fallback
+        p_image = next((item['score'] for item in img_results if item['label'].upper() == 'FAKE'), 0.0)
+
+        # 3. Late Fusion (Weighted Average)
+        # Using 0.5/0.5 for balanced multimodal detection
+        fused_score = (0.5 * p_text) + (0.5 * p_image)
+
+        with col_out:
+            st.subheader("System Verdict")
+            
+            # Classification logic
+            verdict = "AI-GENERATED" if fused_score > 0.5 else "HUMAN-ORIGIN"
+            color = "red" if verdict == "AI-GENERATED" else "green"
+            
+            st.markdown(f"### Result: :{color}[{verdict}]")
+            st.metric("Aggregate Confidence", f"{fused_score:.2%}")
+            
+            # Visual Breakdown
+            st.write("**Modality Breakdown:**")
+            st.progress(p_text, text=f"Text AI Probability: {p_text:.1%}")
+            st.progress(p_image, text=f"Image AI Probability: {p_image:.1%}")
+            
+            # Brief Forensic Note
+            if fused_score > 0.5:
+                st.warning("Conclusion: High cross-modal artifact detection. The content shows patterns consistent with synthetic generation.")
+            else:
+                st.success("Conclusion: Low probability of AI generation. Features align with natural human patterns.")

requirements.txt

@@ -1,3 +1,8 @@
-altair
+streamlit
+torch
+transformers
+Pillow
+plotly
 pandas
-streamlit
+numpy
+accelerate