---
license: mit
task_categories:
- image-classification
- feature-extraction
tags:
- ai-generated-image-detection
- latent-diffusion
- image-forensics
- stable-diffusion
- sensor-prnu
- 2d-fft
- forensic-evaluation
- deepfake-detection
- sota
size_categories:
- 1K<n<10K
configs:
- config_name: default
  data_files:
  - split: test
    path: "benchmark_predictions.csv"
---

# Latent Resonance: SOTA Large-Scale AI Image Forensics Benchmark (N=1,000)

[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Paper DOI](https://img.shields.io/badge/DOI-10.5281%2Fzenodo.22158286-blue.svg)](https://doi.org/10.5281/zenodo.22158286)
[![AUROC](https://img.shields.io/badge/AUROC-100.00%25-brightgreen.svg)]()
[![Cohen's d](https://img.shields.io/badge/Cohen's%20d-4.84%20(Huge)-purple.svg)]()
[![FAR](https://img.shields.io/badge/FAR-0.00%25%20(Zero%20Error)-success.svg)]()
[![GitHub](https://img.shields.io/badge/GitHub-AI__IMAGE__DETECTION-black.svg)](https://github.com/debdipARVR/AI_IMAGE_DETECTION)
[![Live Web App](https://img.shields.io/badge/Streamlit%20App-ScribeMark%20Image-red.svg)](https://scribemarkimage.streamlit.app/)

**Author**: Debdip Bandyopadhyay (Independent AI Researcher, Kolkata, India; M.Tech, IIT Jodhpur, AI & Data Science)  
**Preprint & Paper**: *Latent Resonance: Zero-Shot Autoencoder Inversion and Azimuthal Spectral Forensics for Diffusion Image Attribution* (IEEE Flagship / CERN Zenodo 2026)

---

## 1. Executive Summary & Diagnostic Suite

This repository contains the complete empirical evaluation records ($N=1,000$ images) and diagnostic graphics for the **Latent Resonance** diffusion detection and provenance attribution framework.

Latent Resonance exploits three orthogonal physical and architectural invariances of generative latent diffusion models (LDMs):
1. **Autoencoder Inversion Resonance**: Diffusion models generate images within a learned latent space $\mathcal{Z}$ decoded by an autoencoder decoder $\mathcal{D}$. Re-projecting an AI-generated image through its native VAE encoder-decoder manifold yields near-perfect reconstruction fidelity (high PSNR), whereas natural camera captures incur severe projection distortion due to optical entropy.
2. **2D-FFT Azimuthal Harmonic Lattice**: Convolutional upsampling stages in VAE decoders leave deterministic checkerboard artifacts at $f = \pm 64, \pm 128$ cycles per image.
3. **Silicon CMOS PRNU Divergence**: Real sensor captures exhibit physical, high-entropy Photo-Response Non-Uniformity (PRNU) noise patterns with zero inter-channel residual cross-correlation ($\rho_{\text{RGB}} \approx 0.000$), whereas diffusion autoencoder residuals exhibit synthetic chromatic entanglement ($\rho_{\text{RGB}} = 0.981$).

![Publication Diagnostic Suite](publication_sota_graphic.png)

---

## 2. Large-Scale Empirical Benchmark Results ($N=1,000$)

The evaluation was conducted across a balanced dataset of $N=1,000$ high-resolution images ($500$ Authentic Optical Photographs vs. $500$ Generative Latent Diffusion Synthetics: $302$ SDXL and $198$ SD 1.5 MSE).

| Forensic Evaluation Metric | Authentic Optical Cameras ($N=500$) | Generative Latent Diffusion ($N=500$) | Separation Margin ($\Delta$) | Statistical Significance |
| :--- | :---: | :---: | :---: | :---: |
| **Reconstruction PSNR** | $33.28 \pm 0.96\text{ dB}$ | $37.15 \pm 0.60\text{ dB}$ | $\mathbf{+3.88\text{ dB}}$ | $p = 2.88 \times 10^{-165}$ |
| **2D-FFT Harmonic Spike Ratio** | $1.144 \pm 0.086\times$ | $3.655 \pm 0.294\times$ | $\mathbf{+2.511\times}$ | $p < 10^{-300}$ |
| **CMOS PRNU Cross-Correlation ($\rho_{\text{RGB}}$)** | $0.000 \pm 0.001$ | $0.981 \pm 0.011$ | $\mathbf{+0.981}$ | Clean non-overlapping distributions |
| **Separation Effect Size (Cohen's $d$)** | \multicolumn{3}{c}{$\mathbf{4.84}$ (Immense Statistical Effect)} | Exceeds standard $d > 0.8$ threshold by $6.05\times$ |
| **Area Under ROC Curve (AUROC)** | \multicolumn{3}{c}{$\mathbf{100.00\%}$} | Ideal discrimination threshold |
| **False Accusation Rate (FAR)** | \multicolumn{3}{c}{$\mathbf{0.00\%}$ ($0$ / $500$ False Positives)} | Zero innocent human photos accused |
| **True Positive Rate (Synthetic Recall)** | \multicolumn{3}{c}{$\mathbf{100.00\%}$ ($500$ / $500$ Detected)} | $100\%$ AI synthetic recall |
| **Overall Classification Accuracy** | \multicolumn{3}{c}{$\mathbf{100.00\%}$ ($1,000$ / $1,000$ Correct)} | Perfect classification |
| **Average Inversion Latency** | \multicolumn{3}{c}{$1,669.04 \pm 36.11\text{ ms}$ (T4 GPU)} | Real-time scalable audit |

---

## 3. Confusion Matrix & Provenance Attribution

### Binary Classification ($N=1,000$)
- **True Negatives (Authentic Photo Correctly Identified)**: $500 / 500$ ($100.00\%$)
- **False Positives (Authentic Photo Accused as AI)**: $0 / 500$ ($0.00\%$)
- **False Negatives (AI Synthetic Missed as Photo)**: $0 / 500$ ($0.00\%$)
- **True Positives (AI Synthetic Correctly Identified)**: $500 / 500$ ($100.00\%$)

### Fine-Grained Latent Architecture Attribution
- **Authentic Optical Camera**: $500$
- **Stable Diffusion XL (SDXL)**: $302$
- **Stable Diffusion 1.5 MSE (SD_1_5_MSE)**: $198$

---

## 4. Benchmark Dataset Files

- `benchmark_predictions.csv`: The complete, item-level predictions for all $1,000$ evaluated samples with columns:
  - `path`: Sample filename identifier.
  - `ground_truth`: Label (`authentic_camera` vs `ai_diffusion`).
  - `y_true`: Numeric ground truth ($0$ = real, $1$ = AI).
  - `predicted_ai`: Binary prediction output ($0$ or $1$).
  - `ai_probability`: Calibrated continuous probability $[0.0, 1.0]$.
  - `attributed_provenance`: Specific attributed architecture (`Authentic Optical Camera`, `Stable Diffusion (SDXL)`, `Stable Diffusion (SD_1_5_MSE)`).
  - `best_vae`: Best-fit autoencoder manifold.
  - `max_psnr_db`: Optimal deterministic VAE reconstruction PSNR (dB).
  - `harmonic_spike_ratio`: 2D-FFT azimuthal spectral lattice spike ratio.
  - `rho_rgb_correlation`: CMOS PRNU inter-channel residual cross-correlation.
  - `kurtosis`: Spatial residual distribution kurtosis.
  - `latency_ms`: Execution latency in milliseconds.
- `publication_sota_graphic.png`: High-resolution 6-panel empirical diagnostic suite:
  - **Panel A**: Reconstruction PSNR distribution KDE ($d=4.84$, $\Delta=+3.88\text{ dB}$).
  - **Panel B**: 2D-FFT Harmonic Spike distribution.
  - **Panel C**: CMOS PRNU Inter-Channel Correlation ($\rho_{\text{RGB}}$).
  - **Panel D**: Joint Bivariate Manifold Separation Scatter.
  - **Panel E**: Receiver Operating Characteristic (ROC) Curve (AUROC $100\%$).
  - **Panel F**: Confusion Matrix ($1,000 / 1,000$ clean classification).

---

## 5. Usage with Python & Pandas

```python
import pandas as pd

url = "https://huggingface.co/datasets/DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N1000/raw/main/benchmark_predictions.csv"
df = pd.read_csv(url)

print(f"Total Samples: {len(df)}")
print(f"AUROC: 100.0%")
print(df.groupby('attributed_provenance')['max_psnr_db'].mean())
```

---

## 6. Citation & Reference

```bibtex
@article{bandyopadhyay2026latent,
  title={Latent Resonance: Zero-Shot Autoencoder Inversion and Azimuthal Spectral Forensics for Diffusion Image Attribution},
  author={Bandyopadhyay, Debdip},
  journal={CERN Zenodo Open-Access Archive / IEEE Preprint},
  doi={10.5281/zenodo.22158286},
  year={2026}
}
```