Update app.py

app.py

@@ -9,7 +9,7 @@ import io, warnings
 warnings.filterwarnings("ignore")
 
 # ═══════════════════════════════════════════════════
-# 🛡️ MEDIASHIELD v3.0 — ARCHIVE & FORENSIC SUITE
+# 🛡️ MEDIASHIELD v3.1 — NANO BANANA EDITION
 # ═══════════════════════════════════════════════════
 
 def apply_gamma_correction(img):
@@ -23,89 +23,98 @@ def apply_gamma_correction(img):
         return cv2.LUT(img, table), f"🌙 Mode Sombre (Lumière: {brightness:.1f})"
     return img, "☀️ Lumière Standard"
 
+def detect_nano_signatures(img):
+    """Détecte les gradients trop parfaits et anomalies de Gemini."""
+    gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY).astype(np.float32)
+    # Analyse de la variance des gradients (Laplacien)
+    laplacian = cv2.Laplacian(gray, cv2.CV_64F)
+    smoothness_score = np.var(laplacian)
+    
+    # FFT pour détection de périodicité
+    f = np.fft.fft2(gray)
+    fshift = np.fft.fftshift(f)
+    magnitude_spectrum = 20 * np.log(np.abs(fshift) + 1)
+    
+    # Un score de smoothness < 4.5 est un fort indicateur IA
+    is_nano_candidate = (smoothness_score < 4.5)
+    return is_nano_candidate, smoothness_score, magnitude_spectrum
+
 def verify_natural_grain(img):
-    """Distingue le lissage IA du grain organique d'une photo réelle."""
+    """Distingue le lissage IA du grain organique."""
     gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
     local_var = ndimage.generic_filter(gray, np.var, size=3)
     avg_var = np.mean(local_var)
-    # Un score très bas (< 0.8) indique une texture mathématiquement 'trop' parfaite (IA)
-    if avg_var < 0.8:
-        return True, avg_var
-    return False, avg_var
+    return (avg_var < 0.8), avg_var
 
 def analyze_forensics(img_input):
     if img_input is None: return None, "❌ Aucune image chargée."
     
-    # 1. Nettoyage et Correction Gamma
+    # 1. Traitement Image
     img_cv = cv2.cvtColor(img_input, cv2.COLOR_RGBA2RGB) if img_input.shape[2] == 4 else img_input.copy()
     img_ready, light_msg = apply_gamma_correction(img_cv)
     pil_img = Image.fromarray(img_ready)
 
-    # 2. Analyse du Grain (Anti-Faux Positifs Archives)
-    is_too_smooth, grain_val = verify_natural_grain(img_ready)
+    # 2. Détection Spécifique Nano Banana
+    is_nano, s_score, fft_map = detect_nano_signatures(img_ready)
 
-    # 3. Analyse de Chrominance (Signatures Couleurs IA)
+    # 3. Autres analyses Forensic
+    is_too_smooth, grain_val = verify_natural_grain(img_ready)
+    
+    # 4. Chrominance & ELA
     ycrcb = cv2.cvtColor(img_ready, cv2.COLOR_RGB2YCrCb)
     cr_var = ndimage.generic_filter(ycrcb[:,:,1], np.var, size=5)
     chroma_uniformity = np.std(cr_var) / (np.mean(cr_var) + 1e-8)
-
-    # 4. FFT (Recherche de motifs de grille IA)
-    gray_f = cv2.cvtColor(img_ready, cv2.COLOR_RGB2GRAY).astype(np.float32)
-    f_shift = np.fft.fftshift(np.fft.fft2(gray_f))
-    fft_vis = np.log(np.abs(f_shift) + 1)
     
-    # 5. ELA (Error Level Analysis)
     buf = io.BytesIO()
     pil_img.save(buf, format="JPEG", quality=90)
     recomp = Image.open(io.BytesIO(buf.getvalue())).convert("RGB")
     ela_diff = ImageChops.difference(pil_img, recomp)
     ela_vis = ImageEnhance.Brightness(ela_diff).enhance(10)
 
-    # 6. Système de Score S2T Ariana
+    # 5. Calcul du Score Final
     score = 0
     reasons = []
 
+    if is_nano:
+        score += 40
+        reasons.append(f"🚨 Signature NANO BANANA : Gradients trop lisses (Score: {s_score:.2f})")
+    
     if is_too_smooth:
-        score += 25
-        reasons.append(f"⚡ Texture suspecte (Grain trop faible: {grain_val:.2f})")
+        score += 20
+        reasons.append(f"⚡ Texture suspecte (Grain faible: {grain_val:.2f})")
     else:
-        score -= 10
+        score -= 15
         reasons.append("✅ Grain organique détecté (Photo réelle)")
 
     if chroma_uniformity < 0.9: 
-        score += 30
+        score += 25
         reasons.append("⚠️ Bruit chromatique uniforme (Signature IA)")
 
-    if np.mean(np.array(ela_diff)) < 1.2:
-        score += 20
-        reasons.append("🔍 Compression suspecte (ELA trop lisse)")
-
-    # Bornage du score
     score = max(0, min(100, score))
     verdict = "🔴 IA DÉTECTÉE" if score > 55 else "🟠 SUSPECT" if score > 35 else "🟢 AUTHENTIQUE"
     
-    # Visualisation des preuves
+    # 6. Visualisation
     fig, axes = plt.subplots(1, 3, figsize=(15, 5))
     axes[0].imshow(img_cv); axes[0].set_title(f'Original ({light_msg})')
-    axes[1].imshow(fft_vis, cmap='magma'); axes[1].set_title('Spectre Fréquence (FFT)')
-    axes[2].imshow(ela_vis); axes[2].set_title('Analyse ELA (Compression)')
+    axes[1].imshow(fft_map, cmap='magma'); axes[1].set_title('Spectre Nano (FFT)')
+    axes[2].imshow(ela_vis); axes[2].set_title('Analyse ELA')
     for ax in axes: ax.axis('off')
     plt.tight_layout()
     
-    report = f"🛡️ RAPPORT MEDIASHIELD v3.0\nVERDICT : {verdict}\nIndice de confiance IA : {score}/100\n\nDiagnostic :\n" + "\n".join(reasons)
+    report = f"🛡️ RAPPORT MEDIASHIELD v3.1\nVERDICT : {verdict}\nIndice de confiance IA : {score}/100\n\nDiagnostic :\n" + "\n".join(reasons)
     return fig, report
 
 # ═══════════════════════════════════════════════════
-# INTERFACE UTILISATEUR (GRADIO)
+# INTERFACE UTILISATEUR
 # ═══════════════════════════════════════════════════
 
-with gr.Blocks(title="MediaShield v3.0", theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 🛡️ MediaShield v3.0")
-    gr.Markdown("**Expertise Forensic S2T** : Détection IA, analyse de grain et correction d'archives.")
+with gr.Blocks(title="MediaShield v3.1", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("# 🛡️ MediaShield v3.1 (Nano Edition)")
+    gr.Markdown("Expertise Forensic optimisée pour les archives et la détection des modèles Gemini.")
     
     with gr.Row():
         with gr.Column(scale=1):
-            input_file = gr.Image(label="Charger une image (Photo ou Archive)", type="numpy")
+            input_file = gr.Image(label="Charger l'archive", type="numpy")
             run_btn = gr.Button("LANCER L'ANALYSE", variant="primary")
         with gr.Column(scale=2):
             output_plot = gr.Plot(label="Preuves visuelles")