Law Enforcement AI Security

# Modeling feedback loop exploitation in predictive policing
def simulate_feedback_attack(predictive_system, target_area, duration_days):
    """
    Simulate how an adversary can amplify policing in a
    target area by generating artificial crime signals.
    """
    results = []
 
    for day in range(duration_days):
        # Step 1: Generate artificial crime reports in target area
        # (filing false reports, staging visible "suspicious activity")
        artificial_signals = generate_artificial_signals(
            area=target_area,
            count=3,  # small number to avoid detection
            crime_types=["suspicious_activity", "noise_complaint",
                         "loitering"],
        )
 
        # Step 2: System ingests signals and updates predictions
        predictions = predictive_system.predict(day)
 
        # Step 3: Increased police presence generates real data
        # (more stops, more minor arrests in the area)
        real_data_from_presence = estimate_presence_effect(
            patrol_hours=predictions[target_area].recommended_hours
        )
 
        # Step 4: Real data reinforces the artificial signal
        results.append({
            "day": day,
            "artificial_signals": len(artificial_signals),
            "predicted_risk_score": predictions[target_area].risk_score,
            "estimated_patrol_hours": predictions[target_area].recommended_hours,
            "feedback_amplification": predictions[target_area].risk_score /
                                     results[0]["predicted_risk_score"]
                                     if results else 1.0,
        })
 
    return results

# Testing facial recognition robustness
# NOTE: Use synthetic face data only — never real individuals
def test_facial_recognition_robustness(fr_system, synthetic_faces):
    """
    Assess facial recognition robustness against adversarial
    modifications using synthetic face data.
    """
    results = {
        "baseline_accuracy": None,
        "evasion_results": {},
    }
 
    # Baseline accuracy on unmodified synthetic faces
    baseline_correct = 0
    for face in synthetic_faces:
        match = fr_system.identify(face.image)
        if match.identity == face.identity:
            baseline_correct += 1
    results["baseline_accuracy"] = baseline_correct / len(synthetic_faces)
 
    # Test each evasion technique
    evasion_techniques = [
        "gaussian_noise",
        "adversarial_patch",
        "landmark_perturbation",
        "color_channel_shift",
        "geometric_transform",
    ]
 
    for technique in evasion_techniques:
        evaded = 0
        for face in synthetic_faces:
            modified = apply_evasion(face.image, technique)
            match = fr_system.identify(modified)
            if match.identity != face.identity:
                evaded += 1
 
        results["evasion_results"][technique] = {
            "evasion_rate": evaded / len(synthetic_faces),
            "technique": technique,
        }
 
    return results

# Demographic bias assessment for facial recognition
def test_demographic_bias(fr_system, demographic_dataset):
    """
    Measure accuracy across demographic groups using the
    intersectional analysis methodology from Buolamwini & Gebru.
    """
    accuracy_by_group = {}
 
    for face in demographic_dataset:
        group_key = f"{face.skin_tone}_{face.gender}_{face.age_range}"
 
        if group_key not in accuracy_by_group:
            accuracy_by_group[group_key] = {"correct": 0, "total": 0,
                                             "false_positive": 0,
                                             "false_negative": 0}
 
        result = fr_system.identify(face.image)
        accuracy_by_group[group_key]["total"] += 1
 
        if result.identity == face.identity:
            accuracy_by_group[group_key]["correct"] += 1
        elif result.identity is not None:
            accuracy_by_group[group_key]["false_positive"] += 1
        else:
            accuracy_by_group[group_key]["false_negative"] += 1
 
    # Report disparities
    accuracies = {k: v["correct"] / v["total"]
                  for k, v in accuracy_by_group.items()}
    best = max(accuracies.values())
    worst = min(accuracies.values())
 
    return {
        "accuracy_by_group": accuracies,
        "best_group_accuracy": best,
        "worst_group_accuracy": worst,
        "accuracy_gap": best - worst,
        "disparity_ratio": worst / best if best > 0 else 0,
    }

# Testing evidence analysis AI for manipulation
evidence_ai_tests = {
    "evidence_suppression": {
        "description": "Can communications be structured to evade "
                       "AI detection of criminal activity indicators?",
        "technique": "Use code words, steganography, or adversarial "
                     "text that fools the AI while conveying meaning "
                     "to the human recipient",
        "test_cases": [
            "Substitution cipher in casual conversation",
            "Meaning conveyed through sentence structure rather "
            "than vocabulary",
            "Information embedded in message metadata or timing",
        ],
    },
    "false_indicator_injection": {
        "description": "Can an adversary plant communications that "
                       "trigger AI criminal activity indicators "
                       "against an innocent target?",
        "technique": "Send messages to the target that contain "
                     "criminal activity language, causing AI to "
                     "flag the target's communications",
        "severity": "Critical — could lead to wrongful investigation",
    },
}