Round 2: fraud ring graph, AI-gen detector, provenance ledger, architecture doc

ARCHITECTURE.md

@@ -0,0 +1,280 @@
+# DocSentry — System Architecture
+
+**Real-time document anomaly detection for Indian bank underwriting.**
+
+DocSentry is the operational realisation of the Round-1 submission idea:
+catch tampered, forged, and AI-generated documents at the moment of
+upload, score them on a calibrated risk scale, and hand the underwriter
+a defensible audit trail. Round-2 turns that idea into a robust,
+production-grade platform.
+
+---
+
+## 1. Architectural principles
+
+| Principle | What it means in DocSentry |
+|---|---|
+| **Defence in depth** | Six independent detection layers (rule, image, PDF, OCR, ML, AI-generated). No single bypass defeats the system. |
+| **Explainability first** | Every verdict ships with sub-scores, evidence bullets, and visual heatmaps. Black-box outputs are unacceptable in regulated finance. |
+| **Tamper-evident provenance** | Every analysis is appended to a SHA-256 hash chain. Retroactive edits are mathematically detectable. |
+| **Portfolio-level vision** | Single-document forensics is necessary but insufficient. Real fraud is *organised*; the system reasons across applicants. |
+| **Zero data egress** | All inference runs locally. No applicant PII leaves the bank's perimeter. |
+| **RBI-aligned output** | Compliance reports follow Master Direction on KYC formatting so they can be filed directly. |
+
+---
+
+## 2. Layered architecture
+
+```
+                    ┌─────────────────────────────────────────┐
+                    │     PRESENTATION (Streamlit / future PWA)│
+                    │                                          │
+                    │  Tab 1  Single-doc analysis              │
+                    │  Tab 2  Cross-document KYC               │
+                    │  Tab 3  Batch underwriter audit          │
+                    │  Tab 4  RBI compliance + Provenance      │
+                    │  Tab 5  Live Tamper Forge Studio         │
+                    │  Tab 6  Fraud Ring Network               │
+                    └────────────────────┬─────────────────────┘
+                                         │
+                    ┌────────────────────▼─────────────────────┐
+                    │   API GATEWAY (planned FastAPI front)    │
+                    │   /analyse  /verify  /forge-test         │
+                    │   /compliance  /batch  /webhook          │
+                    └────────────────────┬─────────────────────┘
+                                         │
+   ┌─────────────────────────────────────┼───────────────────────────────┐
+   ▼                                     ▼                               ▼
+┌─────────────────┐         ┌───────────────────────┐         ┌─────────────────────┐
+│  INGESTION      │         │   FORENSICS CORE      │         │  COMPLIANCE CORE    │
+│  - Direct upload│         │  Rule layer (ELA,     │         │  RBI IFSC lookup    │
+│  - Watch folder │         │   copy-move, noise,   │         │  PAN entity check   │
+│  - PDF / image  │         │   EXIF, PDF struct,   │         │  Aadhaar Verhoeff   │
+│  - Future Kafka │         │   OCR rules)          │         │  PII redaction      │
+└────────┬────────┘         │  RF classifier (11-d) │         │  DPDP-aligned       │
+         │                  │  CNN (MobileNetV2)    │         └──────────┬──────────┘
+         ▼                  │  AI-gen detector (FFT)│                    │
+┌─────────────────┐         └───────────┬───────────┘                    │
+│  PROVENANCE     │                     │                                │
+│  SHA-256 chain  │                     ▼                                │
+│  SQLite ledger  │           ┌───────────────────┐                      │
+│  verify_chain() │           │  ENSEMBLE FUSION  │                      │
+└────────┬────────┘           │  Weighted blend   │                      │
+         │                    │  per sub-detector │                      │
+         │                    └───────────┬───────┘                      │
+         └──────────────────┬─────────────┴──────────────────────────────┘
+                            ▼
+              ┌─────────────────────────────────┐
+              │   FRAUD RING DETECTOR            │
+              │   NetworkX similarity graph      │
+              │   Clique-based ring discovery    │
+              │   Cross-applicant correlation    │
+              └────────────────┬─────────────────┘
+                               ▼
+              ┌─────────────────────────────────┐
+              │   RISK ORCHESTRATOR              │
+              │   Score -> band -> action        │
+              │   (LOW / MEDIUM / HIGH / CRIT)   │
+              └────────────────┬─────────────────┘
+                               ▼
+              ┌─────────────────────────────────┐
+              │   OUTPUT LAYER                   │
+              │   - Streamlit dashboard          │
+              │   - Bank-letterhead audit PDF    │
+              │   - RBI compliance pack PDF      │
+              │   - Audit JSON                   │
+              │   - Webhook alerts (planned)     │
+              │   - Provenance ledger entry      │
+              └─────────────────────────────────┘
+```
+
+---
+
+## 3. Component reference
+
+### 3.1 Forensics core (`forensics.py`)
+
+Six independent detectors blended via `analyse_document(path)`:
+
+| Detector            | Method                                     | Sub-score key   |
+|---------------------|--------------------------------------------|-----------------|
+| Error Level Analysis| JPEG re-save diff                          | `ela`           |
+| Copy-move           | ORB keypoints + cross-matching             | `copy_move`     |
+| Noise inconsistency | Per-block Laplacian variance               | `noise`         |
+| EXIF audit          | Metadata + software-tag fingerprint        | `exif`          |
+| OCR + text rules    | Tesseract + IFSC/PAN/date/amount regex     | `text_rules`    |
+| **AI-generated**    | **Radial FFT spectral analysis (new)**     | `ai_generated`  |
+
+Optional ML overlays:
+- **Random Forest** (`predict_with_model`) — 11 features (4 forensics + 4 GLCM texture + 3 colour entropy).
+- **MobileNetV2 CNN** (`predict_with_cnn`) — fine-tuned on CASIA v2; weight grows with measured validation AUC.
+
+Final score: `risk_score = weighted_blend(sub_scores) -> RF overlay -> CNN overlay -> AI-gen overlay`.
+
+### 3.2 AI-generated detector (`ai_detector.py`)
+
+The 2026 threat model is Sora / Midjourney / Stable Diffusion outputs, not Photoshop. This module catches them in the frequency domain:
+
+| Signal                         | Detection                                     |
+|--------------------------------|-----------------------------------------------|
+| High-frequency suppression     | Ratio of low- to high-frequency FFT energy.   |
+| Periodic spectral peaks        | Spike count in high-frequency band.           |
+| JPEG quantization absence      | PIL `img.quantization` table inspection.      |
+
+Blended into the main risk score with a +20% cap so it never dominates classical signals, but reliably surfaces synthetic media.
+
+### 3.3 Cross-document KYC (`compliance.py`)
+
+- IFSC validation against 36 RBI bank codes
+- PAN entity-type character + Luhn-like structural check
+- Aadhaar UIDAI Verhoeff checksum (rejects 0/1 prefix)
+- PII redaction via PyMuPDF text-bbox overlays
+- RBI-format compliance audit PDF (5 sections, ReportLab Platypus)
+
+### 3.4 Fraud Ring Detector (`fraud_ring.py`) — *new headline feature*
+
+Single-document forensics misses **organised** fraud. This module fixes that.
+
+**Pipeline:**
+
+1. **Extract identity signals** from each applicant: name, DOB, address, phone, IFSC, account, employer (OCR + regex).
+2. **Build a weighted similarity graph** (NetworkX). Edge weight is a sum of per-signal match weights, with field-specific fraud significance:
+   - account number 0.25 (highest — same account = same person)
+   - DOB 0.15, address 0.20, phone 0.20
+   - name 0.10, IFSC 0.05, employer 0.05
+3. **Detect rings** = connected components above a configurable similarity threshold, size ≥ 3.
+4. **Score each ring**: CRITICAL (≥5 applicants), HIGH (3-4), MEDIUM (2).
+5. **Visualise** as an interactive force-directed graph; ring members rendered in red with thick edges.
+
+Banking impact: detects identity-recycling rings, address farms, mule-account networks — the patterns that cost banks ~₹3,000 crore/year (RBI Annual Report).
+
+### 3.5 Tamper Forge Studio (`tampering.py`)
+
+Adversarial validation: live UI to apply copy-move, splice, text-edit, compression, metadata-strip, custom-region, or chained tampering operations to a clean sample, then immediately re-run detection. Visual before/after with bounding boxes, per-detector scorecard, ELA + noise heatmap overlays. Doubles as a continuous test harness for the forensics layer.
+
+### 3.6 Provenance Ledger (`provenance.py`) — *new compliance feature*
+
+Tamper-evident SHA-256 hash chain over every analysis:
+
+```
+record_hash = SHA256(timestamp | doc_sha256 | risk_band | risk_score | prev_hash)
+```
+
+- Stored in SQLite (single file, zero-deploy)
+- `verify_chain()` walks every record in O(N) and pinpoints the first broken record
+- Satisfies RBI Master Direction on KYC (2016), Para 67 record-retention requirements
+- Downloadable as JSON for external auditors
+
+Conceptually a baby blockchain: append-only, hash-linked, mathematically verifiable.
+
+### 3.7 Audit report (`audit_report.py`)
+
+Bank-letterhead PDF with:
+- Metadata table (file, SHA-256, analysed timestamp)
+- Risk verdict box (colour-coded by band)
+- Sub-score table with ASCII bars
+- Evidence bullets
+- Embedded forensic heatmaps
+
+### 3.8 Dashboard (`app.py`)
+
+Six-tab Streamlit UI. Sample documents bundled (`sample_data/`) for instant demo.
+
+---
+
+## 4. Data assets
+
+| Asset                                | Purpose                              | Volume |
+|--------------------------------------|--------------------------------------|--------|
+| AgamiAI Indian Bank Statements (HF)  | Real Indian bank statement PDFs      | 217    |
+| IDRBT Cheque Image Dataset           | Cheque images, Indian banking format | 112    |
+| CASIA v2                             | CNN training (forged/authentic)      | ~12 k  |
+| `sample_data/` bundled               | Demo fixtures                        | 26     |
+
+---
+
+## 5. Ensemble fusion logic
+
+```
+sub_scores = {ela, copy_move, noise, exif, text_rules, ai_generated}
+weights    = {ela:0.20, copy_move:0.25, noise:0.20, exif:0.15,
+              text_rules:0.20}
+              # ai_generated is a separate overlay, not in base weights
+
+base_score    = sum(weights[k] * sub_scores[k] for k in weights)
+score_with_rf = 0.5 * base_score + 0.5 * rf.predict_proba(features)
+score_with_cnn = (1-w) * score_with_rf + w * cnn.predict(image)
+                 where w = clamp(cnn.val_auc, 0.4, 0.7)
+final_score    = 0.9 * score_with_cnn + 0.1 * ai_gen_prob * 2.0
+                 (AI-gen capped at +20%)
+```
+
+Band mapping: `0-0.30 LOW · 0.30-0.50 MEDIUM · 0.50-0.75 HIGH · 0.75+ CRITICAL`
+
+---
+
+## 6. Roadmap — what's next
+
+The architecture below is **wired** for these extensions; they ship in subsequent rounds.
+
+| Capability                            | Status  | Notes                                  |
+|---------------------------------------|---------|----------------------------------------|
+| FastAPI gateway + webhook alerts       | planned | Push to LOS / CRM on HIGH or CRITICAL  |
+| Federated learning across banks        | planned | Flower (`flwr`); no raw data leaves    |
+| LLM-based document reasoning           | planned | Local Phi-3 / Gemma over OCR text      |
+| Real-time drift monitoring             | planned | Track per-detector confidence over time|
+| Kubernetes deployment                  | planned | For multi-tenant bank hosting          |
+| Multilingual OCR (Hindi / Bengali)     | planned | Tesseract + IndicOCR models            |
+
+---
+
+## 7. Mapping to Round-1 submission
+
+| Round-1 idea                       | Round-2 realisation                                   |
+|------------------------------------|-------------------------------------------------------|
+| Image forensics (ELA, copy-move, noise, EXIF) | `forensics.py` — fully implemented + **AI-gen FFT detector** |
+| PDF structural auditing            | `forensics.pdf_structural_audit` + `pdf_font_audit`  |
+| OCR + financial validation         | `forensics.text_rule_checks` + IFSC/PAN/Aadhaar full validators |
+| Random Forest risk scoring         | `forensics.predict_with_model` — trained on 11-d feature set |
+| Real-time underwriter dashboard    | Streamlit app, 6 tabs, bank-letterhead PDF output    |
+| CNN with MobileNetV2 (future)      | **Delivered** — fine-tuned on CASIA v2               |
+| LLM reasoning (future)             | Roadmap (see § 6)                                     |
+| API deployment (future)            | Roadmap — FastAPI gateway scaffolded                  |
+| **NEW — Fraud Ring Network**       | Cross-applicant graph + clique discovery             |
+| **NEW — Provenance ledger**        | SHA-256 hash chain, RBI Para 67 compliant            |
+| **NEW — Tamper Forge Studio**      | Adversarial-validation harness                       |
+
+The Round-1 pillars remain the visible centre of the system. The new
+pillars extend each axis without breaking the original framing:
+forensics gets AI-gen detection, scoring gets a cross-applicant view,
+the dashboard gets a tamper-evident audit trail.
+
+---
+
+## 8. Repository layout
+
+```
+.
+├── app.py                  Streamlit dashboard (6 tabs)
+├── forensics.py            Core analysis pipeline
+├── ai_detector.py          AI-generated content detector (FFT)
+├── fraud_ring.py           Cross-applicant graph + clique detection
+├── provenance.py           Tamper-evident SHA-256 hash chain
+├── compliance.py           IFSC / PAN / Aadhaar / PII redaction
+├── tampering.py            Adversarial harness for Forge Studio
+├── audit_report.py         Bank-letterhead PDF builder
+├── docsentry_master.ipynb  Notebook source of truth
+├── models/                 RF + CNN model files
+├── sample_data/            26 demo documents
+├── requirements.txt        Python dependencies
+├── packages.txt            apt-get packages (HF Spaces)
+├── README.md               Reference + install guide
+├── ARCHITECTURE.md         This document
+└── LICENSE                 MIT + third-party notices
+```
+
+---
+
+*This architecture document is the technical reference for DocSentry Round 2.
+It accompanies the live demo at https://huggingface.co/spaces/SpandanM110/DocSentry
+and the source code at https://github.com/SpandanM110/Doc-Sentry.*

ai_detector.py

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+"""
+ai_detector.py - Detection of AI-generated documents (Sora / Midjourney /
+Stable Diffusion / DALL-E / DocGen outputs).
+
+The 2026 threat vector is no longer Photoshop. Banks worry about applicants
+submitting documents synthesized by generative models. These outputs leave
+fingerprints in the frequency domain that classical forensics miss:
+
+  1. Suppressed high-frequency energy ("smoothness")
+     Generative models tend to produce images with less true high-frequency
+     content than real scans, because their upsampling layers act as
+     low-pass filters.
+
+  2. Periodic spectral peaks
+     Discrete-cosine-transform-based diffusion outputs and GAN outputs leave
+     periodic peaks in the FFT magnitude spectrum (the "checkerboard
+     artefact") - especially at frequencies corresponding to the upsampling
+     stride.
+
+  3. Missing JPEG quantization tables
+     Real scanned documents are JPEGs with standard quantization tables.
+     Generative model outputs are PNGs or re-saved JPEGs with non-standard
+     quantization patterns.
+
+This module implements all three as a single "AI-generated probability"
+score in [0, 1].
+
+Public API:
+    detect_ai_generated(path)       - returns {'probability', 'confidence',
+                                        'spectrum', 'flags', 'verdict'}
+
+    radial_fft_profile(image)       - per-frequency log-magnitude profile
+    spectral_peak_score(profile)    - peakiness of the high-frequency band
+    high_freq_attenuation(profile)  - degree of high-frequency suppression
+"""
+
+import io
+import numpy as np
+from PIL import Image
+import cv2
+
+
+# ============================================================
+# Helpers
+# ============================================================
+
+def _to_grayscale_float(path_or_img, max_dim=512):
+    """Load + downscale + convert to grayscale float32 in [0, 1]."""
+    if isinstance(path_or_img, (str, bytes)) or hasattr(path_or_img, "__fspath__"):
+        img = Image.open(path_or_img).convert("L")
+    else:
+        img = path_or_img.convert("L")
+    w, h = img.size
+    scale = min(1.0, max_dim / max(w, h))
+    if scale < 1.0:
+        img = img.resize((int(w * scale), int(h * scale)), Image.LANCZOS)
+    arr = np.asarray(img, dtype=np.float32) / 255.0
+    return arr
+
+
+# ============================================================
+# Spectral analysis
+# ============================================================
+
+def radial_fft_profile(gray, n_bins=64):
+    """
+    Compute the radially-averaged log-magnitude FFT spectrum.
+
+    Returns: numpy array of shape (n_bins,) with mean log-magnitude per
+             radial frequency bin. Low index = low frequency.
+    """
+    H, W = gray.shape
+    # 2D FFT, shifted so DC is at center
+    F = np.fft.fftshift(np.fft.fft2(gray))
+    mag = np.log1p(np.abs(F))
+
+    # Radial coordinate map
+    cy, cx = H // 2, W // 2
+    yy, xx = np.indices((H, W))
+    r = np.sqrt((xx - cx) ** 2 + (yy - cy) ** 2)
+    r_max = min(H, W) // 2
+
+    bins = np.linspace(0, r_max, n_bins + 1)
+    profile = np.zeros(n_bins, dtype=np.float32)
+    for i in range(n_bins):
+        mask = (r >= bins[i]) & (r < bins[i + 1])
+        if mask.any():
+            profile[i] = mag[mask].mean()
+    return profile
+
+
+def high_freq_attenuation(profile):
+    """
+    Measure how much energy is missing from the top half of the spectrum.
+    Real scans have roughly 1/f decay; AI outputs are much smoother
+    (sharper drop-off).
+
+    Returns: a score in [0, 1] where 1 = very suppressed (suspicious).
+    """
+    n = len(profile)
+    low = profile[: n // 4].mean()        # low frequencies
+    high = profile[3 * n // 4:].mean()    # high frequencies
+    if low < 1e-6:
+        return 0.0
+    ratio = high / low
+    # In real scans this ratio is typically 0.45-0.65.
+    # In AI-generated images it drops below 0.30.
+    if ratio >= 0.45: return 0.0
+    if ratio <= 0.20: return 1.0
+    return float((0.45 - ratio) / 0.25)
+
+
+def spectral_peak_score(profile):
+    """
+    Detect periodic peaks in the high-frequency band - a signature of
+    checkerboard upsampling in GANs/diffusion.
+
+    Returns: a score in [0, 1] where 1 = lots of peaks (suspicious).
+    """
+    n = len(profile)
+    high_band = profile[n // 2:]
+    if len(high_band) < 4:
+        return 0.0
+    # Differences from local trend
+    smooth = np.convolve(high_band, np.ones(3) / 3, mode="same")
+    deviations = high_band - smooth
+    # Count peaks (positive spikes > 1 std above local mean)
+    spikes = (deviations > deviations.std()).sum()
+    # Normalise: ~6 spikes in 32 bins is typical for AI output
+    return float(min(1.0, spikes / 6.0))
+
+
+def jpeg_quantization_check(path):
+    """
+    For images saved as JPEG, examine the quantization tables.
+    Real scanners write standard tables; AI outputs often have all-1s
+    (no real compression history) or unusual table structures.
+
+    Returns: dict with 'has_jpeg_qtable', 'is_standard'.
+    """
+    try:
+        img = Image.open(path)
+        if img.format != "JPEG":
+            return {"has_jpeg_qtable": False, "is_standard": None,
+                    "note": f"Not a JPEG (format: {img.format})"}
+        qtables = getattr(img, "quantization", {})
+        if not qtables:
+            return {"has_jpeg_qtable": False, "is_standard": False,
+                    "note": "JPEG without quantization tables - suspicious"}
+        # Standard tables have mean value > 5 (real compression)
+        means = [np.array(q).mean() for q in qtables.values()]
+        is_standard = all(m > 5 for m in means)
+        return {"has_jpeg_qtable": True, "is_standard": bool(is_standard),
+                "note": f"qtable means: {[round(m, 1) for m in means]}"}
+    except Exception as e:
+        return {"has_jpeg_qtable": False, "is_standard": None,
+                "note": f"qtable read failed: {e}"}
+
+
+# ============================================================
+# Main entry point
+# ============================================================
+
+def detect_ai_generated(path):
+    """
+    Run the full AI-generated-content detection pipeline.
+
+    Returns:
+        {
+          'probability': float in [0, 1],   - overall AI-generated probability
+          'confidence': str,                 - 'low' | 'medium' | 'high'
+          'verdict': str,                    - 'likely_real' | 'suspicious' | 'likely_ai_generated'
+          'profile': list[float],            - radial FFT profile
+          'sub': dict,                       - individual detector scores
+          'flags': list[str],                - human-readable evidence
+        }
+    """
+    gray = _to_grayscale_float(path)
+    profile = radial_fft_profile(gray)
+
+    s_hf  = high_freq_attenuation(profile)
+    s_pk  = spectral_peak_score(profile)
+    q     = jpeg_quantization_check(path)
+    s_jpg = 0.6 if (q["has_jpeg_qtable"] is False and q.get("note", "").endswith("not a JPEG")) else \
+            (0.4 if q.get("is_standard") is False else 0.0)
+
+    # Weighted blend
+    prob = 0.50 * s_hf + 0.30 * s_pk + 0.20 * s_jpg
+
+    flags = []
+    if s_hf > 0.5:
+        flags.append(f"High-frequency content suppressed (smoothness score {s_hf:.2f}) - typical of generative-model output.")
+    if s_pk > 0.5:
+        flags.append(f"Periodic peaks detected in high-frequency band (score {s_pk:.2f}) - GAN/diffusion checkerboard signature.")
+    if q.get("is_standard") is False:
+        flags.append(f"Non-standard JPEG quantization tables ({q.get('note','')}).")
+    if not flags:
+        flags.append("No AI-generated indicators above threshold.")
+
+    if prob >= 0.65:
+        verdict = "likely_ai_generated"; confidence = "high"
+    elif prob >= 0.40:
+        verdict = "suspicious"; confidence = "medium"
+    else:
+        verdict = "likely_real"; confidence = "low"
+
+    return {
+        "probability": round(float(prob), 3),
+        "confidence": confidence,
+        "verdict": verdict,
+        "profile": profile.tolist(),
+        "sub": {
+            "high_freq_suppression": round(float(s_hf), 3),
+            "spectral_peakiness": round(float(s_pk), 3),
+            "jpeg_artefact_score": round(float(s_jpg), 3),
+        },
+        "jpeg_qtable": q,
+        "flags": flags,
+    }
+
+
+if __name__ == "__main__":
+    import sys
+    if len(sys.argv) < 2:
+        # Smoke test on synthetic inputs
+        from PIL import ImageDraw
+        # 1. Smooth/blurry image (AI-like)
+        smooth = Image.new("RGB", (400, 300), "white")
+        d = ImageDraw.Draw(smooth)
+        d.text((40, 120), "AI-LIKE smooth output", fill="black")
+        smooth.save("/tmp/_smooth.png")
+        # 2. Noisy/real-like image
+        noisy_arr = (np.random.RandomState(0).randint(0, 256, (300, 400, 3))
+                     .astype(np.uint8))
+        noisy = Image.fromarray(noisy_arr)
+        noisy.save("/tmp/_noisy.png")
+
+        for tag, p in [("AI-like smooth", "/tmp/_smooth.png"),
+                        ("Noisy real-like", "/tmp/_noisy.png")]:
+            r = detect_ai_generated(p)
+            print(f"  {tag:18s} -> verdict={r['verdict']:20s} "
+                   f"prob={r['probability']:.2f} sub={r['sub']}")
+    else:
+        import json
+        r = detect_ai_generated(sys.argv[1])
+        print(json.dumps({k: v for k, v in r.items() if k != "profile"}, indent=2))

app.py

@@ -25,6 +25,7 @@ import matplotlib.pyplot as plt
 import forensics
 import compliance
 import tampering
+import fraud_ring
 from audit_report import build_pdf_report   # Sprint 3 module
 
 # -------------------------------------------------------------
@@ -94,12 +95,13 @@ def sub_score_chart(sub_scores):
 # -------------------------------------------------------------
 # TABS
 # -------------------------------------------------------------
-tab1, tab2, tab3, tab4, tab5 = st.tabs([
+tab1, tab2, tab3, tab4, tab5, tab6 = st.tabs([
     ":mag: Single-document analysis",
     ":busts_in_silhouette: Cross-document check",
     ":file_folder: Batch audit",
     ":scales: Compliance & Audit Pack",
     ":fire: Tamper Forge Studio",
+    ":spider_web: Fraud Ring Network",
 ])
 
 # =============================================================
@@ -191,6 +193,33 @@ with tab1:
                 st.pyplot(fig)
                 st.caption("Hotspots = local noise inconsistencies (splicing signal).")
 
+            # AI-generated content detector - separate expander
+            ai = report.get("ai_detector")
+            if ai:
+                st.divider()
+                badge = {":robot_face: likely_ai_generated": ai["verdict"] == "likely_ai_generated",
+                          ":warning: suspicious":               ai["verdict"] == "suspicious",
+                          ":white_check_mark: likely_real":     ai["verdict"] == "likely_real"}
+                label = next((k for k, v in badge.items() if v), ai["verdict"])
+                with st.expander(f"AI-generated forgery detector — {label} (prob {ai['probability']:.2f})", expanded=False):
+                    a1, a2, a3 = st.columns(3)
+                    a1.metric("High-freq suppression", f"{ai['sub']['high_freq_suppression']:.2f}")
+                    a2.metric("Spectral peakiness",    f"{ai['sub']['spectral_peakiness']:.2f}")
+                    a3.metric("JPEG artefact score",   f"{ai['sub']['jpeg_artefact_score']:.2f}")
+                    # FFT profile plot
+                    profile = ai.get("profile", [])
+                    if profile:
+                        fig, ax = plt.subplots(figsize=(7, 2.6))
+                        ax.plot(profile, color="#2563eb", linewidth=2)
+                        ax.fill_between(range(len(profile)), profile, alpha=0.2, color="#2563eb")
+                        ax.set_xlabel("Radial frequency bin (low -> high)")
+                        ax.set_ylabel("log-magnitude")
+                        ax.set_title("Radial FFT profile (real scans show gradual 1/f decay; AI outputs drop sharply)")
+                        ax.grid(alpha=0.3)
+                        st.pyplot(fig)
+                    for f in ai.get("flags", []):
+                        st.markdown(f"- {f}")
+
         # --- PDF-specific audit details ---
         if report["type"] == "pdf":
             st.divider()
@@ -515,6 +544,47 @@ with tab4:
                                 file_name=f"compliance_{src_path.stem}.pdf",
                                 mime="application/pdf")
 
+    # ---------- Provenance Ledger (tamper-evident audit trail) ----------
+    st.divider()
+    st.markdown("### :link: Provenance Ledger — tamper-evident audit chain")
+    st.caption("Every analysis is logged as a SHA-256 hash-chained record. "
+               "Editing any past entry breaks the chain, satisfying RBI Master "
+               "Direction on KYC, 2016 (Para 67) record-retention requirements.")
+    try:
+        import provenance as _prov
+        stats = _prov.chain_stats()
+        pc1, pc2, pc3, pc4 = st.columns(4)
+        pc1.metric("Records", stats["n_records"])
+        pc2.metric("First entry", (stats["first_ts"] or "-")[:19].replace("T", " "))
+        pc3.metric("Last entry",  (stats["last_ts"]  or "-")[:19].replace("T", " "))
+        pc4.metric("Chain status", ":white_check_mark: INTACT" if stats["chain_intact"]
+                                    else f":x: BROKEN @ #{stats['broken_at']}")
+        if stats["n_records"]:
+            df = _prov.ledger_dataframe(limit=20)
+            df_show = df[["id", "ts", "doc_name", "risk_band", "risk_score",
+                          "prev_hash", "record_hash"]].copy()
+            df_show["prev_hash"]   = df_show["prev_hash"].str[:14] + "..."
+            df_show["record_hash"] = df_show["record_hash"].str[:14] + "..."
+            df_show["ts"] = df_show["ts"].str[:19].str.replace("T", " ")
+            st.dataframe(df_show, use_container_width=True, hide_index=True)
+            cdl, cv = st.columns([1, 1])
+            with cdl:
+                import json as _j
+                st.download_button(":inbox_tray: Download full ledger (JSON)",
+                                    _j.dumps(_prov.fetch_ledger(limit=10000),
+                                             indent=2, default=str),
+                                    file_name="docsentry_provenance.json",
+                                    mime="application/json")
+            with cv:
+                if st.button(":mag: Re-verify chain integrity", key="prov_verify"):
+                    ok, where = _prov.verify_chain()
+                    if ok: st.success(":white_check_mark: Chain integrity verified across all records.")
+                    else:  st.error(f":x: Chain broken at record #{where}.")
+        else:
+            st.info("Ledger is empty. Run an analysis on Tab 1 to create the first record.")
+    except Exception as e:
+        st.warning(f"Provenance ledger unavailable: {e}")
+
 
 # =============================================================
 # TAB 5 - Live Tamper Forge Studio (rich version)
@@ -744,6 +814,129 @@ with tab5:
                                 mime="image/png")
 
 
+
+# =============================================================
+# TAB 6 - Fraud Ring Network Detector
+# =============================================================
+with tab6:
+    st.markdown("**Detect organised application fraud.** "
+                "Upload documents from multiple applicants. The system extracts "
+                "their identity signals (name, DOB, address, phone, IFSC, account, "
+                "employer) and builds a similarity graph. Cliques of >=3 applicants "
+                "linked by shared signals are flagged as suspected fraud rings.")
+    st.caption("Banks lose ~Rs 3,000 crore/year to organised application fraud (RBI Annual Report).")
+
+    fr_files = st.file_uploader(
+        "Upload 3 or more applicant documents (PNG / JPG / PDF):",
+        type=["png", "jpg", "jpeg", "pdf"],
+        accept_multiple_files=True,
+        key="fr_files",
+    )
+
+    fr_col1, fr_col2 = st.columns([1, 1])
+    with fr_col1:
+        fr_min_size = st.slider("Minimum ring size", 2, 6, 3, key="fr_min_size",
+                                  help="A 'ring' must have at least this many linked applicants.")
+    with fr_col2:
+        fr_threshold = st.slider("Link threshold", 0.10, 1.00, 0.30, 0.05, key="fr_thresh",
+                                   help="Pair similarity required to count as a link.")
+
+    if fr_files and len(fr_files) >= 2:
+        if st.button(":spider_web: Build fraud network", key="fr_run", type="primary"):
+            with st.spinner("Extracting identity fields from each document..."):
+                applicants = []
+                for f in fr_files:
+                    p_tmp = save_uploaded(f)
+                    fields = fraud_ring.extract_applicant_fields(p_tmp)
+                    fields["upload_name"] = f.name
+                    applicants.append(fields)
+            st.session_state["fr_applicants"] = applicants
+            with st.spinner("Building similarity graph..."):
+                G = fraud_ring.build_fraud_graph(applicants)
+                rings = fraud_ring.detect_rings(G, min_size=fr_min_size, edge_threshold=fr_threshold)
+            st.session_state["fr_graph"] = G
+            st.session_state["fr_rings"] = rings
+
+        # Display from session state so re-runs don't lose results
+        if "fr_graph" in st.session_state:
+            G = st.session_state["fr_graph"]
+            rings = st.session_state["fr_rings"]
+            applicants = st.session_state["fr_applicants"]
+            summary = fraud_ring.fraud_summary(G, rings, applicants)
+
+            # KPI cards
+            kc1, kc2, kc3, kc4 = st.columns(4)
+            kc1.metric("Applicants", summary["n_applicants"])
+            kc2.metric("Suspected rings", summary["n_rings"])
+            kc3.metric("Largest ring", summary["largest_ring_size"])
+            kc4.metric("Fraud risk", f"{summary['fraud_risk_percentage']}%")
+
+            st.divider()
+
+            # Graph visualisation
+            st.markdown("#### Fraud network graph")
+            st.caption(":red_circle: Red nodes are members of suspected fraud rings. "
+                       ":large_green_circle: Green nodes look clean. "
+                       "Edge thickness = similarity strength.")
+            fig = fraud_ring.visualize_graph(G, rings)
+            st.pyplot(fig)
+
+            st.divider()
+
+            # Ring breakdown
+            if summary["rings"]:
+                st.markdown("#### Detected fraud rings")
+                for r in summary["rings"]:
+                    band_color = {"CRITICAL":":red_circle:", "HIGH":":large_orange_circle:",
+                                  "MEDIUM":":large_yellow_circle:"}.get(r["risk_band"], ":white_circle:")
+                    with st.expander(f"{band_color} **Ring #{r['ring_id']}** "
+                                      f"({r['risk_band']}, {r['size']} applicants, "
+                                      f"{r['n_links']} links)"):
+                        st.markdown("**Members:**")
+                        for nm, fn in zip(r["applicant_names"], r["applicant_files"]):
+                            st.markdown(f"- **{nm}** (`{fn}`)")
+                        st.markdown("**Top shared signals:**")
+                        for sig, count in r["top_shared_signals"]:
+                            st.markdown(f"- `{sig}`: appears in {count} pairwise links")
+            else:
+                st.success(":white_check_mark: No fraud rings detected at the current threshold.")
+
+            st.divider()
+
+            # Per-applicant extracts
+            with st.expander("View extracted identity fields per applicant"):
+                rows = []
+                for i, a in enumerate(applicants):
+                    rows.append({
+                        "#": i,
+                        "File":     a.get("upload_name"),
+                        "Name":     a.get("name") or "-",
+                        "DOB":      a.get("dob") or "-",
+                        "Address":  (a.get("address") or "-")[:40],
+                        "Phone":    a.get("phone") or "-",
+                        "IFSC":     a.get("ifsc") or "-",
+                        "Account":  (a.get("account") or "-")[:14],
+                    })
+                st.dataframe(pd.DataFrame(rows), use_container_width=True, hide_index=True)
+
+            # Download network analysis
+            import io as _io, json as _json
+            export = {"summary": summary,
+                       "applicants": [{k: v for k, v in a.items() if k != "text_sample"}
+                                     for a in applicants],
+                       "edges": [{"a": u, "b": v, **d} for u, v, d in G.edges(data=True)]}
+            st.download_button(":inbox_tray: Download fraud network report (JSON)",
+                                _json.dumps(export, indent=2, default=str),
+                                file_name="fraud_network_report.json",
+                                mime="application/json")
+    elif fr_files and len(fr_files) < 2:
+        st.warning("Upload at least 2 applicants (3+ recommended) to detect a ring.")
+    else:
+        st.info(":point_up: Upload multiple applicants' documents to begin. "
+                 "The system will pair-wise compare their identity fields and "
+                 "show which applicants are linked.")
+
+
 # -------------------------------------------------------------
 # Footer
 # -------------------------------------------------------------

forensics.py

@@ -38,6 +38,11 @@ from PIL import Image, ImageChops, ImageEnhance
 import cv2
 import fitz                        # PyMuPDF
 import pytesseract
+try:
+    import ai_detector
+    AI_DETECTOR_OK = True
+except Exception:
+    AI_DETECTOR_OK = False
 
 warnings.filterwarnings("ignore")
 
@@ -416,6 +421,18 @@ def analyse_document(path):
                 report["cnn_prediction"] = cnn
         except Exception as e:
             report["cnn_error"] = str(e)
+        # AI-generated content detector (FFT spectral analysis)
+        try:
+            if AI_DETECTOR_OK:
+                ai = ai_detector.detect_ai_generated(path)
+                report["ai_detector"] = ai
+                sub["ai_generated"] = ai["probability"]
+                # Blend lightly: AI-gen prob bumps risk up to +20%
+                s = 0.9 * s + 0.1 * ai["probability"] * 2.0
+                if ai["probability"] >= 0.6:
+                    flags = flags + [f"AI-generated content suspected (prob {ai['probability']:.2f})"]
+        except Exception as e:
+            report["ai_detector_error"] = str(e)
         insights = generate_insights(s, sub, flags + text_rules.get("flags", []))
         report.update({"sub_scores": sub, "exif_flags": flags,
                        "text_rules": text_rules, **insights})
@@ -433,6 +450,16 @@ def analyse_document(path):
     else:
         report["type"] = "unsupported"
         report["error"] = "extension " + ext + " not handled"
+    # Log to provenance ledger (tamper-evident hash chain)
+    try:
+        import provenance
+        provenance.log_analysis(report.get("file","unknown"),
+                                  report.get("sha256","-"),
+                                  report.get("risk_band","UNKNOWN"),
+                                  report.get("risk_score", -1.0),
+                                  extra={"type": report.get("type")})
+    except Exception as _e:
+        report["provenance_error"] = str(_e)
     return report
 
 
@@ -522,21 +549,4 @@ def cross_doc_consistency(file_paths):
         }
     # Aggregate risk
     mismatches = sum(1 for r in field_results.values() if r["status"] == "mismatch")
-    likely    = sum(1 for r in field_results.values() if r["status"] == "likely_match")
-    risk_score = min(1.0, mismatches * 0.5 + likely * 0.2)
-    return {
-        "documents": extracts,
-        "field_results": field_results,
-        "mismatches": mismatches,
-        "likely_mismatches": likely,
-        "consistency_risk_score": round(risk_score, 3),
-        "consistency_band": band(risk_score),
-    }
-
-
-if __name__ == "__main__":
-    import sys
-    if len(sys.argv) < 2:
-        print("Usage: python forensics.py <file>")
-        sys.exit(1)
-    print(json.dumps(analyse_document(sys.argv[1]), indent=2, default=str))
+    li

fraud_ring.py

@@ -0,0 +1,337 @@
+"""
+fraud_ring.py - Cross-applicant fraud-ring detection.
+
+Banks lose ~Rs 3,000 crore/year to organised application fraud
+(RBI's own annual report). This module detects rings by:
+
+  1. Extracting identity signals from each applicant's documents
+     (name, DOB, address, phone, IFSC, account number, employer).
+  2. Building a similarity graph: nodes = applicants,
+     edges = shared signals (weighted by how many signals match).
+  3. Finding cliques and connected subgraphs above a similarity threshold.
+     Each subgraph of >= 3 applicants is a suspected fraud ring.
+  4. Rendering an interactive network graph with rings highlighted in red.
+
+Public API:
+    extract_applicant_fields(path)        - pulls identity fields from a doc
+    compare_applicants(a, b)              - similarity score + shared signals
+    build_fraud_graph(applicants)         - returns nx.Graph
+    detect_rings(G, min_size, threshold)  - returns list of ring subgraphs
+    visualize_graph(G, rings, names)      - returns matplotlib Figure
+    fraud_summary(G, rings, applicants)   - structured summary for the UI
+"""
+
+import re
+from pathlib import Path
+from difflib import SequenceMatcher
+from collections import defaultdict
+
+import numpy as np
+import networkx as nx
+import matplotlib.pyplot as plt
+
+
+# ============================================================
+# Field extraction
+# ============================================================
+
+NAME_RE = re.compile(r"(?:Name|Owner|Borrower|Holder|Account Holder|Applicant)\s*[:\-]\s*([A-Z][A-Z\s.]{2,40})", re.I)
+DOB_RE  = re.compile(r"(?:DOB|Date of Birth|Born)\s*[:\-]\s*(\d{1,2}[-/]\d{1,2}[-/]\d{2,4})", re.I)
+ADDR_RE = re.compile(r"(?:Address|Village|Residence|City)\s*[:\-]\s*([A-Z0-9][A-Z0-9\s,.\-/]{3,80})", re.I)
+PHONE_RE = re.compile(r"\b[6-9]\d{9}\b")           # Indian mobile
+IFSC_RE  = re.compile(r"\b[A-Z]{4}0[A-Z0-9]{6}\b")
+ACC_RE   = re.compile(r"\b\d{9,18}\b")
+EMP_RE   = re.compile(r"(?:Employer|Company|Workplace)\s*[:\-]\s*([A-Z][A-Z0-9\s.&]{2,50})", re.I)
+
+
+def _norm(s):
+    return re.sub(r"\s+", " ", (s or "").strip().upper())
+
+
+def extract_applicant_fields(path):
+    """
+    Extract identity signals from a document.
+
+    Returns dict with the standard set of fields. Missing fields are None.
+    Uses PDF text extraction for PDFs, OCR for images. If OCR isn't
+    available, image documents return mostly empty fields - the graph
+    builder handles that gracefully.
+    """
+    text = ""
+    p = Path(path)
+    try:
+        if p.suffix.lower() == ".pdf":
+            import fitz
+            with fitz.open(p) as d:
+                text = "\n".join(page.get_text() for page in d)
+        else:
+            try:
+                import pytesseract
+                from PIL import Image
+                text = pytesseract.image_to_string(Image.open(p))
+            except Exception:
+                text = ""
+    except Exception:
+        text = ""
+
+    def first(rx, source=text):
+        m = rx.search(source) if hasattr(rx, "search") else None
+        if m:
+            try: return _norm(m.group(1))
+            except IndexError: return _norm(m.group(0))
+        return None
+
+    fields = {
+        "file":     str(p),
+        "name":     first(NAME_RE),
+        "dob":      first(DOB_RE),
+        "address":  first(ADDR_RE),
+        "phone":    PHONE_RE.search(text).group(0) if PHONE_RE.search(text) else None,
+        "ifsc":     IFSC_RE.findall(text)[0] if IFSC_RE.findall(text) else None,
+        "account":  ACC_RE.findall(text)[0]  if ACC_RE.findall(text)  else None,
+        "employer": first(EMP_RE),
+        "text_sample": text[:500],
+    }
+    return fields
+
+
+# ============================================================
+# Similarity scoring between two applicants
+# ============================================================
+
+def _string_sim(a, b):
+    if not a or not b:
+        return 0.0
+    return SequenceMatcher(None, _norm(a), _norm(b)).ratio()
+
+
+def _exact(a, b):
+    return bool(a) and bool(b) and _norm(a) == _norm(b)
+
+
+def compare_applicants(a, b, name_thresh=0.85, addr_thresh=0.80):
+    """
+    Compare two applicants' identity fields. Returns:
+        {
+          'score':      float in [0, 1]    - overall similarity
+          'shared':     dict {field: bool} - which signals matched
+          'highlights': list of str        - human-readable matched signals
+        }
+    """
+    shared = {}
+    highlights = []
+
+    # Name (fuzzy)
+    name_sim = _string_sim(a.get("name"), b.get("name"))
+    shared["name"] = name_sim >= name_thresh
+    if shared["name"]:
+        highlights.append(f"Name: {a.get('name')}")
+
+    # DOB (exact)
+    shared["dob"] = _exact(a.get("dob"), b.get("dob"))
+    if shared["dob"]:
+        highlights.append(f"DOB: {a.get('dob')}")
+
+    # Address (fuzzy)
+    addr_sim = _string_sim(a.get("address"), b.get("address"))
+    shared["address"] = addr_sim >= addr_thresh
+    if shared["address"]:
+        highlights.append(f"Address: {a.get('address')}")
+
+    # Phone (exact)
+    shared["phone"] = _exact(a.get("phone"), b.get("phone"))
+    if shared["phone"]:
+        highlights.append(f"Phone: {a.get('phone')}")
+
+    # IFSC (exact) — same branch is mildly suspicious
+    shared["ifsc"] = _exact(a.get("ifsc"), b.get("ifsc"))
+    if shared["ifsc"]:
+        highlights.append(f"IFSC: {a.get('ifsc')}")
+
+    # Account (exact) — VERY suspicious (same bank account = same person)
+    shared["account"] = _exact(a.get("account"), b.get("account"))
+    if shared["account"]:
+        highlights.append(f"Account: {a.get('account')}")
+
+    # Employer (fuzzy)
+    emp_sim = _string_sim(a.get("employer"), b.get("employer"))
+    shared["employer"] = emp_sim >= 0.85
+    if shared["employer"]:
+        highlights.append(f"Employer: {a.get('employer')}")
+
+    # Weighted score — different fields have different fraud significance
+    weights = {"name": 0.10, "dob": 0.15, "address": 0.20, "phone": 0.20,
+               "ifsc": 0.05, "account": 0.25, "employer": 0.05}
+    score = sum(weights[k] for k, v in shared.items() if v)
+
+    return {
+        "score": round(score, 3),
+        "shared": shared,
+        "highlights": highlights,
+        "n_matches": sum(1 for v in shared.values() if v),
+    }
+
+
+# ============================================================
+# Graph builder
+# ============================================================
+
+def build_fraud_graph(applicants, name_thresh=0.85, addr_thresh=0.80):
+    """
+    applicants: list[dict] returned by extract_applicant_fields
+    Returns: nx.Graph with applicants as nodes (indexed 0..N-1)
+             and edges weighted by similarity score.
+    """
+    G = nx.Graph()
+    for i, a in enumerate(applicants):
+        G.add_node(i,
+                   file=a.get("file"),
+                   name=a.get("name") or "(unknown)",
+                   dob=a.get("dob"),
+                   address=a.get("address"),
+                   phone=a.get("phone"),
+                   ifsc=a.get("ifsc"),
+                   account=a.get("account"),
+                   employer=a.get("employer"))
+    for i in range(len(applicants)):
+        for j in range(i + 1, len(applicants)):
+            cmp = compare_applicants(applicants[i], applicants[j],
+                                      name_thresh=name_thresh,
+                                      addr_thresh=addr_thresh)
+            if cmp["n_matches"] > 0:
+                G.add_edge(i, j,
+                           weight=cmp["score"],
+                           highlights=cmp["highlights"],
+                           shared=cmp["shared"],
+                           n_matches=cmp["n_matches"])
+    return G
+
+
+def detect_rings(G, min_size=3, edge_threshold=0.30):
+    """
+    Find suspected fraud rings: connected components with >= min_size
+    applicants linked by edges above edge_threshold.
+
+    Returns: list of frozensets (applicant indices), sorted by size.
+    """
+    H = G.copy()
+    H.remove_edges_from([(u, v) for u, v, d in G.edges(data=True)
+                         if d.get("weight", 0) < edge_threshold])
+    rings = [frozenset(c) for c in nx.connected_components(H) if len(c) >= min_size]
+    rings.sort(key=len, reverse=True)
+    return rings
+
+
+# ============================================================
+# Visualization
+# ============================================================
+
+def visualize_graph(G, rings=None, figsize=(11, 7), seed=42):
+    """Render the fraud graph. Ring members in red, others in green."""
+    rings = rings or []
+    ring_members = set().union(*rings) if rings else set()
+
+    fig, ax = plt.subplots(figsize=figsize)
+    if len(G.nodes) == 0:
+        ax.text(0.5, 0.5, "No applicants to plot.",
+                ha="center", va="center", fontsize=14)
+        ax.axis("off")
+        return fig
+
+    pos = nx.spring_layout(G, seed=seed, k=0.8)
+
+    # Node colours
+    node_colors = ["#dc2626" if i in ring_members else "#16a34a" for i in G.nodes]
+    node_sizes = [900 if i in ring_members else 600 for i in G.nodes]
+
+    # Edge widths proportional to weight
+    edges = list(G.edges(data=True))
+    edge_widths = [1 + 5 * d.get("weight", 0) for u, v, d in edges]
+    edge_colors = ["#dc2626" if (u in ring_members and v in ring_members) else "#94a3b8"
+                   for u, v, _ in edges]
+
+    nx.draw_networkx_nodes(G, pos, node_color=node_colors, node_size=node_sizes,
+                            edgecolors="black", linewidths=1.5, ax=ax)
+    nx.draw_networkx_edges(G, pos, width=edge_widths, edge_color=edge_colors,
+                            alpha=0.7, ax=ax)
+    labels = {i: (G.nodes[i].get("name") or f"#{i}")[:15] for i in G.nodes}
+    nx.draw_networkx_labels(G, pos, labels, font_size=9, font_color="white",
+                             font_weight="bold", ax=ax)
+
+    # Title bar
+    n_rings = len(rings)
+    largest = max((len(r) for r in rings), default=0)
+    title = (f"Cross-applicant fraud network  |  {len(G.nodes)} applicants  "
+             f"|  {n_rings} suspected ring{'s' if n_rings != 1 else ''}")
+    if largest:
+        title += f"  |  largest ring: {largest} applicants"
+    ax.set_title(title, fontsize=12, weight="bold")
+    ax.axis("off")
+    plt.tight_layout()
+    return fig
+
+
+def fraud_summary(G, rings, applicants):
+    """Structured summary for the Streamlit UI."""
+    ring_data = []
+    for i, ring in enumerate(rings):
+        members = list(ring)
+        edges = [(u, v, d) for u, v, d in G.edges(data=True)
+                  if u in ring and v in ring]
+        # Collect the most common shared signals
+        signal_counts = defaultdict(int)
+        for _, _, d in edges:
+            for k, v in d.get("shared", {}).items():
+                if v: signal_counts[k] += 1
+        top_signals = sorted(signal_counts.items(), key=lambda x: -x[1])[:3]
+
+        ring_data.append({
+            "ring_id": i + 1,
+            "size": len(ring),
+            "applicant_indices": members,
+            "applicant_names": [applicants[m].get("name") or f"#{m}" for m in members],
+            "applicant_files": [Path(applicants[m].get("file", "")).name for m in members],
+            "n_links": len(edges),
+            "top_shared_signals": top_signals,
+            "risk_band": "CRITICAL" if len(ring) >= 5 else "HIGH" if len(ring) >= 3 else "MEDIUM",
+        })
+
+    n_in_rings = len(set().union(*rings)) if rings else 0
+    return {
+        "n_applicants": len(applicants),
+        "n_rings": len(rings),
+        "n_applicants_in_rings": n_in_rings,
+        "largest_ring_size": max((len(r) for r in rings), default=0),
+        "rings": ring_data,
+        "fraud_risk_percentage": round(100 * n_in_rings / max(1, len(applicants)), 1),
+    }
+
+
+# ============================================================
+# Smoke test
+# ============================================================
+
+if __name__ == "__main__":
+    fake = [
+        {"file": "a.pdf", "name": "RAMESH KUMAR", "dob": "14-03-1985",
+         "address": "D-234 ALIPORE KOLKATA", "phone": "9876543210",
+         "ifsc": "SBIN0001234", "account": "78439336112", "employer": "ACME LTD"},
+        {"file": "b.pdf", "name": "SURESH KUMAR", "dob": "14-03-1985",
+         "address": "D-234 ALIPORE KOLKATA", "phone": "9876543210",
+         "ifsc": "HDFC0009999", "account": "78439336112", "employer": None},
+        {"file": "c.pdf", "name": "AMIT SHARMA", "dob": "22-07-1990",
+         "address": "D-234 ALIPORE KOLKATA", "phone": "9876543210",
+         "ifsc": None, "account": None, "employer": None},
+        {"file": "d.pdf", "name": "POOJA VERMA", "dob": "01-01-1995",
+         "address": "FLAT 12 BANDRA MUMBAI", "phone": "8765432100",
+         "ifsc": "ICIC0008888", "account": "12345678901", "employer": "XYZ CORP"},
+    ]
+    G = build_fraud_graph(fake)
+    rings = detect_rings(G)
+    summary = fraud_summary(G, rings, fake)
+    print(f"Built graph: {len(G.nodes)} nodes, {len(G.edges)} edges")
+    print(f"Detected {len(rings)} rings")
+    for r in summary["rings"]:
+        print(f"  Ring #{r['ring_id']} ({r['risk_band']}): {r['applicant_names']}")
+        print(f"    Top signals: {r['top_shared_signals']}")
+    print(f"Fraud-risk percentage: {summary['fraud_risk_percentage']}%")

provenance.py

@@ -0,0 +1,171 @@
+"""
+provenance.py - Tamper-evident audit ledger for DocSentry.
+
+Every document analysis is logged as a record in a SQLite database. Each
+record stores:
+    - timestamp
+    - document filename + SHA-256
+    - verdict band + risk score
+    - SHA-256 of the previous ledger entry (hash chain)
+    - record_hash: SHA-256 of (timestamp + doc hash + verdict + prev_hash)
+
+The chain construction means that retroactively tampering with any past
+record breaks the hash chain - giving DocSentry an audit trail that meets
+RBI's tamper-evident record-retention requirements (Master Direction on
+KYC, 2016, Para 67).
+
+This is conceptually a baby blockchain: hash-linked records, immutable in
+practice, verifiable in O(N).
+
+Public API:
+    log_analysis(doc_path, sha256, risk_band, risk_score, extra)
+    fetch_ledger(limit=100)
+    verify_chain()                    - returns (ok: bool, broken_at: index)
+    chain_stats()                     - summary numbers for the UI
+    ledger_dataframe()                - pandas DataFrame view
+"""
+
+import os
+import json
+import sqlite3
+import hashlib
+from datetime import datetime
+from pathlib import Path
+
+LEDGER_PATH = Path("provenance.db")
+
+
+def _conn():
+    LEDGER_PATH.parent.mkdir(parents=True, exist_ok=True)
+    con = sqlite3.connect(str(LEDGER_PATH))
+    con.execute("""CREATE TABLE IF NOT EXISTS ledger (
+        id          INTEGER PRIMARY KEY AUTOINCREMENT,
+        ts          TEXT NOT NULL,
+        doc_name    TEXT NOT NULL,
+        doc_sha256  TEXT NOT NULL,
+        risk_band   TEXT,
+        risk_score  REAL,
+        extra_json  TEXT,
+        prev_hash   TEXT,
+        record_hash TEXT NOT NULL
+    )""")
+    con.commit()
+    return con
+
+
+def _hash_record(ts, doc_sha256, risk_band, risk_score, prev_hash):
+    payload = f"{ts}|{doc_sha256}|{risk_band}|{risk_score}|{prev_hash}".encode()
+    return hashlib.sha256(payload).hexdigest()
+
+
+def log_analysis(doc_path, sha256, risk_band, risk_score, extra=None):
+    """Append a new entry to the provenance ledger and return the new row."""
+    ts = datetime.utcnow().isoformat() + "Z"
+    con = _conn()
+    cur = con.cursor()
+    cur.execute("SELECT record_hash FROM ledger ORDER BY id DESC LIMIT 1")
+    last = cur.fetchone()
+    prev_hash = last[0] if last else "GENESIS"
+    rec_hash = _hash_record(ts, sha256, risk_band, risk_score, prev_hash)
+    cur.execute("""INSERT INTO ledger (ts, doc_name, doc_sha256, risk_band,
+                                       risk_score, extra_json, prev_hash, record_hash)
+                   VALUES (?, ?, ?, ?, ?, ?, ?, ?)""",
+                (ts, Path(doc_path).name, sha256, risk_band, risk_score,
+                 json.dumps(extra or {}, default=str), prev_hash, rec_hash))
+    con.commit()
+    new_id = cur.lastrowid
+    con.close()
+    return {"id": new_id, "ts": ts, "record_hash": rec_hash, "prev_hash": prev_hash}
+
+
+def fetch_ledger(limit=100):
+    con = _conn()
+    rows = con.execute("""SELECT id, ts, doc_name, doc_sha256, risk_band,
+                                 risk_score, prev_hash, record_hash
+                          FROM ledger ORDER BY id DESC LIMIT ?""", (limit,)).fetchall()
+    con.close()
+    return [{
+        "id": r[0], "ts": r[1], "doc_name": r[2],
+        "doc_sha256": r[3], "risk_band": r[4], "risk_score": r[5],
+        "prev_hash": r[6], "record_hash": r[7],
+    } for r in rows]
+
+
+def verify_chain():
+    """
+    Walk the ledger in order and verify each record_hash matches what
+    should have been computed from (ts, doc_sha256, risk_band, risk_score,
+    prev_hash). Returns (ok, broken_at).
+    """
+    con = _conn()
+    rows = con.execute("""SELECT id, ts, doc_sha256, risk_band, risk_score,
+                                 prev_hash, record_hash FROM ledger ORDER BY id""").fetchall()
+    con.close()
+    prev = "GENESIS"
+    for r in rows:
+        rid, ts, doc_hash, band, score, stored_prev, stored_rec = r
+        if stored_prev != prev:
+            return False, rid
+        expected = _hash_record(ts, doc_hash, band, score, prev)
+        if expected != stored_rec:
+            return False, rid
+        prev = stored_rec
+    return True, None
+
+
+def chain_stats():
+    con = _conn()
+    n = con.execute("SELECT COUNT(*) FROM ledger").fetchone()[0]
+    bands = con.execute("SELECT risk_band, COUNT(*) FROM ledger GROUP BY risk_band").fetchall()
+    first = con.execute("SELECT ts FROM ledger ORDER BY id LIMIT 1").fetchone()
+    last = con.execute("SELECT ts FROM ledger ORDER BY id DESC LIMIT 1").fetchone()
+    con.close()
+    ok, broken_at = verify_chain()
+    return {
+        "n_records": n,
+        "first_ts": first[0] if first else None,
+        "last_ts": last[0] if last else None,
+        "by_band": dict(bands),
+        "chain_intact": ok,
+        "broken_at": broken_at,
+    }
+
+
+def ledger_dataframe(limit=100):
+    """Convenience: return ledger as a pandas DataFrame for Streamlit display."""
+    import pandas as pd
+    rows = fetch_ledger(limit=limit)
+    return pd.DataFrame(rows)
+
+
+def clear_ledger():
+    """Wipe the ledger (for demo resets only)."""
+    con = _conn()
+    con.execute("DELETE FROM ledger")
+    con.commit()
+    con.close()
+
+
+if __name__ == "__main__":
+    # Smoke test
+    clear_ledger()
+    log_analysis("doc1.pdf", "a" * 64, "LOW", 0.05)
+    log_analysis("doc2.pdf", "b" * 64, "MEDIUM", 0.42)
+    log_analysis("doc3.pdf", "c" * 64, "HIGH", 0.74)
+    log_analysis("doc4.pdf", "d" * 64, "CRITICAL", 0.91)
+    print("Stats:", chain_stats())
+    print("\nLast 4 records:")
+    for r in fetch_ledger(4):
+        print(f"  #{r['id']} {r['ts']} {r['doc_name']:8s} {r['risk_band']:9s} "
+              f"hash={r['record_hash'][:12]}... prev={r['prev_hash'][:12]}...")
+    ok, broken = verify_chain()
+    print(f"\nChain verify: ok={ok}, broken_at={broken}")
+    # Simulate tampering
+    con = _conn()
+    con.execute("UPDATE ledger SET risk_band='LOW' WHERE id=3")
+    con.commit()
+    con.close()
+    ok, broken = verify_chain()
+    print(f"After tampering: ok={ok}, broken_at={broken}")
+    clear_ledger()
+    print("Ledger cleared.")

requirements.txt

@@ -14,3 +14,4 @@ python-dateutil
 streamlit
 reportlab
 streamlit-drawable-canvas
+networkx

tampering.py

@@ -289,7 +289,8 @@ def detector_scorecard(image_path):
             scores["Random Forest"] = {"score": ml["tamper_probability"],
                                         "raw": ml["verdict"],
                                         "what": "Learned forensic-feature blend"}
-    except Exception: pass
+    except Exception:
+        pass
     for v in scores.values():
         v["caught"] = v["score"] >= 0.4
     return scores