deepfake-detector

Dual-Stream Deepfake Detector & Spatiotemporal Forensics

A PyTorch deepfake detection architecture fusing a ConvNeXt-Small spatial backbone with a 4-stage ResSE-Spectral Tower (Steganographic Rich Model + Bayar-Stamm 2D Real FFT decomposition), High-Frequency SNR-Adaptive Gating, Platt probability calibration, and a Dual-Path (Attention + Global Max-Pooling) Bi-GRU temporal video sequence head.

• GitHub Repository: yyouretoast/deepfake-detection
• Live Hugging Face Space: yyouretoast/deepfake-detector

---

Model Description

• Developed by: Yassin
• Model Type: Dual-Stream Spatial + Frequency Hybrid Deepfake Detector
• Spatial Backbone: ConvNeXt-Small (512-d feature projection)
• Frequency Backbone: ResSE-Spectral Tower (4 stages, 20-channel SRM/Bayar 2D Real FFT input, Squeeze-and-Excitation channel attention)
• Video Temporal Head: 2-Layer Bidirectional GRU with Dual-Path Pooling (Attention + Max-Pooling)
• Calibration: Temperature Scaling ($T^* = 4.2880$) and Bayesian 3-Zone Bands ($\tau_{\text{real}}=0.40, \tau_{\text{fake}}=0.60$)

---

Hosted Checkpoints

---

How to Use

1. Automated Hub Download & Inference

import torch
from huggingface_hub import hf_hub_download

# Download calibrated weights
ckpt_path = hf_hub_download(
    repo_id="yyouretoast/deepfake-detector",
    filename="dual_stream_calibrated.pth"
)

checkpoint = torch.load(ckpt_path, map_location="cpu", weights_only=False)
optimal_threshold = float(checkpoint.get("optimal_threshold", 0.4200))
temperature = float(checkpoint.get("temperature", 4.2880))
tau_real = float(checkpoint.get("tau_real", 0.40))
tau_fake = float(checkpoint.get("tau_fake", 0.60))

print(f"Loaded model calibrated with T*={temperature:.3f}, optimal threshold τ*={optimal_threshold:.2f}")

2. Forensic Single Image Analysis

Using the official repository engine:

from src.services.video_engine import process_single_image

# Runs YuNet 5-point face alignment, dual-stream inference, and telemetry
report = process_single_image("suspect_face.jpg")

print("Verdict:", report["three_zone"]["verdict"])
print("Calibrated Probability:", report["prob"])
print("Spectral vs Spatial Gate:", f"{report['spectral_gate']*100:.1f}% / {report['spatial_gate']*100:.1f}%")
print("SNR Attenuation (γ):", report["snr_attenuator"])

---

Empirical Benchmark Performance

Evaluated across 13,444 facial crops (756 authentic real faces, 12,688 deepfakes across 5 generator families) and 1,120 video sequences:

In-Distribution Per-Generator AUC:

• FF++ Face2Face: 0.9975
• FF++ NeuralTextures: 0.9749
• FF++ Deepfakes: 0.9625
• FF++ FaceSwap: 0.9091
• Celeb-DF v2: 0.8166

---

Citation

@misc{deepfake_forensics_2026,
  author = {Yassin},
  title = {Dual-Stream Deepfake Forensics Engine: Spatial ConvNeXt and ResSE-Spectral Gated Fusion with Spatiotemporal Sequence Modeling},
  year = {2026},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/yyouretoast/deepfake-detector}}
}