v0.2.0: asian data

🛡️ DeepGuard v0.2.0 — 2nd-gen Asian Release

Deployment-ready DeepFake detection weights built for high-traffic inference.
The 2nd generation extends coverage to Korean data via KoDF — a Large-Scale Korean DeepFake Detection Dataset.

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📦 Available Weights

Architecture	Variant	kodf
MS-EffGCViT	b0	✅
MS-EffGCViT	b5	✅

• b0 → Fast variant (lightweight, CPU-friendly)
• b5 → Pro variant (high precision)

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🚀 Usage

Option A. timm API

Install the package and import deepguard to register the models into the timm registry.

!pip install -q git+https://github.com/HanMoonSub/DeepGuard.git
 
import timm
import deepguard  # registers models into timm
 
model = timm.create_model("ms_eff_gcvit_b0", pretrained=True, dataset="kodf")
model = timm.create_model("ms_eff_gcvit_b5", pretrained=True, dataset="kodf")

Option B. Direct deepguard import ✨ (recommended)

Import the model builders directly — no timm dependency required.

!pip install -q git+https://github.com/HanMoonSub/DeepGuard.git
 
from deepguard import ms_eff_gcvit_b0, ms_eff_gcvit_b5
 
model = ms_eff_gcvit_b0(pretrained=True, dataset="kodf")
model = ms_eff_gcvit_b5(pretrained=True, dataset="kodf")

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🔧 Arguments

Arg	Type	Options	Description
pretrained	bool	True / False	Load pretrained weights
dataset	str	"kodf"	Training dataset of the weights to load

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🔁 Changelog

• ➕ Added kodf weights for ms_eff_gcvit_b0 and ms_eff_gcvit_b5
• 🌏 Expanded dataset coverage from Western (celeb_df_v2, ff++) to Korean (kodf)

v0.1.0: 1st-gen western data

🛡️ DeepGuard v0.1.0 — 1st-gen Western Release

Deployment-ready DeepFake detection weights built for high-traffic inference.
The 1st generation is trained on Western datasets (Celeb-DF-v2, FaceForensics++).

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📦 Available Weights

Architecture	Variant	celeb_df_v2	ff++
MS-EffViT	b0	✅	✅
MS-EffViT	b5	✅	✅
MS-EffGCViT	b0	✅	✅
MS-EffGCViT	b5	✅	✅

• b0 → Fast variant (lightweight, CPU-friendly)
• b5 → Pro variant (high precision)

---

🚀 Usage

Option A. timm API

Clone the repo and import deepguard to register the models into the timm registry.

!git clone https://github.com/HanMoonSub/DeepGuard.git
%cd DeepGuard
 
import timm
import deepguard  # registers models into timm
 
model = timm.create_model("ms_eff_gcvit_b0", pretrained=True, dataset="celeb_df_v2")
model = timm.create_model("ms_eff_gcvit_b5", pretrained=True, dataset="ff++")

Option B. Direct deepguard import ✨ (recommended)

Install the package and import the model builders directly — no timm dependency required.

!pip install deepguard
# latest dev build: pip install -U git+https://github.com/HanMoonSub/DeepGuard.git
 
from deepguard import ms_eff_gcvit_b0, ms_eff_gcvit_b5
 
model = ms_eff_gcvit_b0(pretrained=True, dataset="celeb_df_v2")
model = ms_eff_gcvit_b5(pretrained=True, dataset="ff++")

---

🔧 Arguments

Arg	Type	Options	Description
pretrained	bool	True / False	Load pretrained weights
dataset	str	"celeb_df_v2", "ff++"	Training dataset of the weights to load

---

📋 Full Model List

from deepguard import (
    ms_eff_vit_b0,
    ms_eff_vit_b5,
    ms_eff_gcvit_b0,
    ms_eff_gcvit_b5,
)
 
# each supports: dataset="celeb_df_v2" | "ff++"