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DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N100

Latent Resonance: SOTA Empirical AI Image Forensics Benchmark (N=100 & N=1,000 Scale) 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) Benchmark Overview This repository provides: The official verified $N=100$ ground-truth image… See the full description on the dataset page: https://huggingface.co/datasets/DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N100.

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Latent Resonance: SOTA Empirical AI Image Forensics Benchmark (N=100 & N=1,000 Scale)

![License: MIT](https://opensource.org/licenses/MIT) ![Paper DOI](https://doi.org/10.5281/zenodo.22158286) ![AUROC]() ![Cohen's d]() ![GitHub](https://github.com/debdipARVR/AIIMAGEDETECTION) ![Live Web App](https://scribemarkimage.streamlit.app/) ![N=1000 Benchmark](https://huggingface.co/datasets/DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N1000)

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)

Benchmark Overview

This repository provides:

  1. 1.The official verified $N=100$ ground-truth image dataset (PNG image pairs and metadata).
  2. 2.The complete $N=1,000$ large-scale empirical audit results (benchmark_predictions.csv and publication_sota_graphic.png).

For the standalone $N=1,000$ benchmark dataset repository, visit: 👉 [DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N1000](https://huggingface.co/datasets/DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N1000)

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Large-Scale Benchmark Results ($N=1,000$)

Forensic MetricAuthentic 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}}$$p = 2.88 \times 10^{-165}$
2D-FFT Harmonic Spike$1.144 \pm 0.086\times$$3.655 \pm 0.294\times$$\mathbf{+2.511\times}$$p < 10^{-300}$
CMOS PRNU ($\rho_{\text{RGB}}$)$0.000 \pm 0.001$$0.981 \pm 0.011$$\mathbf{+0.981}$Clean non-overlapping
Effect Size (Cohen's $d$)\multicolumn{3}{c}{$\mathbf{4.84}$ (Immense Effect Separation)}Huge separation
Clean AUROC\multicolumn{3}{c}{$\mathbf{100.00\%}$}Zero Accusation Error
False Accusation Rate (FAR)\multicolumn{3}{c}{$\mathbf{0.00\%}$}Zero False Positives
Synthetic Recall (TPR)\multicolumn{3}{c}{$\mathbf{100.00\%}$}$500$ / $500$ Detected

Dataset Structure

├── real_photos/             # 50 Authentic Optical Camera PNGs (512x512)
├── ai_synthetic/            # 50 Native Latent Diffusion PNGs (512x512)
├── metadata.jsonl           # N=100 JSONL records with labels and metrics
├── benchmark_predictions.csv # N=1,000 complete item-level empirical audit records
└── publication_sota_graphic.png # 6-panel publication diagnostic graphic

Quick Usage with Hugging Face datasets

python
from datasets import load_dataset

dataset = load_dataset("DebdipCS/Latent-Resonance-AI-Image-Forensics-Benchmark-N100")
print(dataset)

Citation

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}
}