| def calculate_final_score(fn, md, ml): |
| w_fn, w_md, w_ml = 0.00, 0.10, 0.90 |
| mode = "Standard analysis mode" |
| mode_detail = "Deep learning models and forensics drive the verdict, metadata is secondary" |
|
|
| def share(a, r): |
| t = a + r |
| if t == 0: |
| return 50.0, 50.0 |
| return (a / t) * 100, (r / t) * 100 |
|
|
| def share_md(a, r): |
| |
| baseline = 30.0 |
| t = a + r + baseline |
| return ((a + baseline / 2) / t) * 100, ((r + baseline / 2) / t) * 100 |
|
|
| fn_a, fn_r = 0.0, 100.0 |
| md_a, md_r = share_md(md.get("ai_points", 0), md.get("real_points", 0)) |
|
|
| |
| st = ml.get("style", {}) |
| st_ai_points = st.get("ai_points", 0) |
| st_real_points = st.get("real_points", 0) |
|
|
| def share_st(a, r): |
| baseline = 20.0 |
| t = a + r + baseline |
| return ((a + baseline / 2) / t) * 100, ((r + baseline / 2) / t) * 100 |
|
|
| st_a, st_r = share_st(st_ai_points, st_real_points) |
| base_ml_a, base_ml_r = share(ml.get("ai_points", 0), ml.get("real_points", 0)) |
|
|
| |
| ml_a = base_ml_a * 0.90 + st_a * 0.10 |
| ml_r = base_ml_r * 0.90 + st_r * 0.10 |
|
|
| ai_s = fn_a * w_fn + md_a * w_md + ml_a * w_ml |
| real_s = fn_r * w_fn + md_r * w_md + ml_r * w_ml |
| tot = ai_s + real_s or 1 |
| ai_s = round((ai_s / tot) * 100, 1) |
| real_s = round((real_s / tot) * 100, 1) |
|
|
| forensic_override = False |
| forensics = ml.get("forensics", {}) |
| if forensics: |
| kurt_ai = forensics.get("kurtosis", {}).get("ai_prob", 0.5) |
| dfi_ai = forensics.get("dfi", {}).get("ai_prob", 0.5) |
| model_ai = ml.get("weighted_ai_prob", 0.5) |
| forensic_avg = (kurt_ai + dfi_ai) / 2 |
| if abs(forensic_avg - model_ai) > 0.40: |
| forensic_override = True |
|
|
| if ai_s >= 50: |
| verdict = "Fake" |
| if ai_s >= 85: |
| confidence = "Very High" |
| color = "#ef4444" |
| elif ai_s >= 70: |
| confidence = "High" |
| color = "#f87171" |
| else: |
| confidence = "Medium" |
| color = "#f97316" |
| else: |
| verdict = "Real" |
| if ai_s <= 15: |
| confidence = "Very High" |
| color = "#22c55e" |
| elif ai_s <= 30: |
| confidence = "High" |
| color = "#4ade80" |
| else: |
| confidence = "Medium" |
| color = "#86efac" |
|
|
| if forensic_override: |
| if confidence in ("Very High", "High"): |
| confidence = "Medium" |
| elif confidence == "Medium": |
| confidence = "Low" |
|
|
| breakdown = [ |
| { |
| "layer": "Filename Analysis", |
| "ai_pts": 0, |
| "real_pts": 0, |
| "weight_pct": "0%", |
| "signals": ["Filename analysis disabled."], |
| "mode": "Layer deactivated.", |
| }, |
| { |
| "layer": "Metadata Analysis", |
| "ai_pts": md.get("ai_points", 0), |
| "real_pts": md.get("real_points", 0), |
| "weight_pct": f"{int(w_md * 100)}%", |
| "signals": md.get("signals", []), |
| "mode": "Metadata-provenance layer.", |
| }, |
| { |
| "layer": "AI Model and Forensic Detectors", |
| "ai_pts": int(ml_a), |
| "real_pts": int(ml_r), |
| "weight_pct": f"{int(w_ml * 100)}%", |
| "signals": ml.get("signals", []), |
| "votes": ml.get("votes", []), |
| "forensics": ml.get("forensics", {}), |
| "mode": ml.get("priority_note", "") + " With visual style & symmetry checks.", |
| }, |
| ] |
|
|
| summary = ( |
| f"Scoring mode: {mode}. " |
| f"Final AI score: {ai_s}%. " |
| f"Verdict: {verdict} (Confidence: {confidence}). " |
| f"{mode_detail}." |
| ) |
| if forensic_override: |
| summary += " Forensic signals conflict with model predictions." |
|
|
| return { |
| "verdict": verdict, |
| "ai_score": ai_s, |
| "real_score": real_s, |
| "confidence": confidence, |
| "color": color, |
| "breakdown": breakdown, |
| "summary": summary, |
| "scoring_mode": mode, |
| "forensic_override": forensic_override, |
| "weights": {"filename": w_fn, "metadata": w_md, "models": w_ml}, |
| } |
|
|
