![Multimodal AIGC Detection System (Core Diagram), a horizontal flowchart.
It can be represented as "Input → Multiple Signals → Decision → Handling":
┌──────────────────────────────────────────┐
│ Multimodal AIGC Security Detection System │
└──────────────────────────────────────────┘
[Input Content]
Text / Image / Video / Audio
↓
┌──────────────────────────────────────────┐
│ Multiple Signal Detection Layer (Core) │
└──────────────────────────────────────────┘
① Source Signal
- Watermark (Explicit / Implicit)
- C2PA Metadata
- Generation Tool Fingerprint
② Content Signal
- Text AI Detection Model
- Image/Video Generation Detection
- Deepfake Features (Face, Frequency Domain)
③ Behavior Signal
- Batch Generation
- Publishing Frequency
- Content Similarity
- Agent Behavior Trajectory
④ Context Signal
- Hotspot Events
- Sensitive Figures
- Abnormal Propagation Path
↓
┌──────────────────────────────────────────┐
│ Risk Fusion & Grading Decision │
└──────────────────────────────────────────┘
- Multi-Signal Fusion Scoring
- Risk Levels R1 ~ R5
↓
┌──────────────────────────────────────────┐
│ Governance and Handling Strategies │
└──────────────────────────────────────────┘
- Pass
- Risk Labeling
- Downgrade / Traffic Limiting
- Intercept
- Manual Review](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FKcbDvMhcVkeNLyqeUPMgBUBpsfqZSdZc%2Fd57ce067-2bfa-4882-9f92-d11c76f68c6f%2F8230df52-4095-4ad2-8087-64a62c47413c.png&w=3840&q=75)
Prompt Description
Multimodal AIGC Detection System (Core Diagram), a horizontal flowchart. It can be represented as "Input → Multiple Signals → Decision → Handling": ┌──────────────────────────────────────────┐ │ Multimodal AIGC Security Detection System │ └──────────────────────────────────────────┘ [Input Content] Text / Image / Video / Audio ↓ ┌──────────────────────────────────────────┐ │ Multiple Signal Detection Layer (Core) │ └──────────────────────────────────────────┘ ① Source Signal - Watermark (Explicit / Implicit) - C2PA Metadata - Generation Tool Fingerprint ② Content Signal - Text AI Detection Model - Image/Video Generation Detection - Deepfake Features (Face, Frequency Domain) ③ Behavior Signal - Batch Generation - Publishing Frequency - Content Similarity - Agent Behavior Trajectory ④ Context Signal - Hotspot Events - Sensitive Figures - Abnormal Propagation Path ↓ ┌──────────────────────────────────────────┐ │ Risk Fusion & Grading Decision │ └──────────────────────────────────────────┘ - Multi-Signal Fusion Scoring - Risk Levels R1 ~ R5 ↓ ┌──────────────────────────────────────────┐ │ Governance and Handling Strategies │ └──────────────────────────────────────────┘ - Pass - Risk Labeling - Downgrade / Traffic Limiting - Intercept - Manual Review
![```python
import os
import re
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import torch
from torch.utils.data import Dataset, DataLoader
from tqdm import tqdm
import joblib
import matplotlib.pyplot as plt
# =========================
# Configuration
# =========================
class Config:
# ✅ New version: Water accumulation point Excel (added cumulative rainfall)
points_xlsx = r'F:\实验数据\CA_积水点融合\深度置信网络\样本输出_final\积水点_整合_step3_研究区内_新增累计降雨_719-723.xlsx'
# CA simulation results & geographic data
ca_data_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\CA模拟结果'
geo_data_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\地理数据' # building_density.npy, DEM.npy, road_density.npy, Slope.npy, Landuse.npy
# Patch & Sampling
patch_size = 64
ca_dry_thr_cm = 1.0 # Negative sample water depth threshold: < 1cm
tile_size = 200 # Negative sample spatial uniform sampling tile size (unit: grid cell)
# Landuse one-hot encoding types (can be adjusted according to your actual data)
landuse_types = [7, 8, 10, 50, 60, 80, 100]
# Training related
output_dir = r'F:\实验数据\CA_积水点融合\深度置信网络\DBN训练输出_64patch_mu'
```](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FBFJVaELXylgOOK0WatKO6tw6EfizLTx7%2F8a5d501e-e43d-4dfe-b168-5609ff4c3ef1%2F03d366b1-042e-4669-8cfb-aa5952cc4cb1.png&w=3840&q=75)
