--- license: mit tags: - deepfake-detection - image-classification - computer-vision - pytorch - resnext - face-forgery datasets: - custom metrics: - accuracy - auc ---

> ### 🔍 Detect deepfakes with state-of-the-art accuracy > A **ResNeXt-101 32×8d** backbone — initialised from Instagram's weakly-supervised pretrained weights — fine-tuned to expose AI-generated and manipulated faces with high confidence.
``` ✅ Real Face 🚨 Deepfake ────────────── ────────────── Confidence: 97.3% Confidence: 99.1% ```

---

## 🗺️ Navigation [📊 Overview](#-model-overview) · [🏗️ Architecture](#️-architecture) · [🚀 Quick Start](#-quick-start) · [🖼️ Examples](#️-inference-examples) · [🏋️ Training](#️-training-details) · [⚠️ Limitations](#️-limitations) · [📜 Cite](#-license--citation)

--- ## 📊 Model Overview

| 🔖 Property | 📋 Value | |:---|:---| | 🧱 **Architecture** | ResNeXt-101 32×8d | | 🖼️ **Input** | RGB · 224 × 224 px | | 🎯 **Task** | Binary — Real vs Fake | | 🔢 **Parameters** | ~88 Million | | 💾 **File size** | 741.62 MB | | 🔬 **Precision** | float32 | | ⚙️ **Framework** | PyTorch | | 🌐 **Backbone** | Instagram WSL pretrained |

``` Model Pipeline ══════════════ 📷 Input Image ↓ 🔲 Face Crop & Align ↓ 🧮 ResNeXt-101 ├─ 88M parameters ├─ 4 residual stages └─ 32× grouped convs ↓ 🎯 Classifier Head ↓ ┌──────┬──────┐ │ ✅ │ 🚨 │ │ REAL │ FAKE │ └──────┴──────┘ ```

--- ## 🏗️ Architecture > The backbone uses **grouped convolutions with cardinality 32** — each layer splits into 32 parallel transformation paths, then aggregates. This lets the network learn diverse artefact patterns (blending seams, frequency inconsistencies, unnatural textures) simultaneously. ``` ┌─────────────────────────────────────────────────────────────┐ │ ResNeXt-101 32×8d │ ├─────────────────────────────────────────────────────────────┤ │ │ │ 📷 Input ──▶ 🌱 STEM │ │ Conv 7×7 │ BN │ ReLU │ MaxPool │ │ 3 → 64 channels │ │ │ │ │ ┌────────▼────────┐ │ │ │ 🧩 LAYER 1 │ ×3 blocks · ch 256 │ │ └────────┬────────┘ │ │ ┌────────▼────────┐ │ │ │ 🧩 LAYER 2 │ ×4 blocks · ch 512 │ │ └────────┬────────┘ │ │ ┌────────▼────────┐ │ │ │ 🧩 LAYER 3 │ ×23 blocks · ch 1024 ◀── deepest │ │ └────────┬────────┘ │ │ ┌────────▼────────┐ │ │ │ 🧩 LAYER 4 │ ×3 blocks · ch 2048 │ │ └────────┬────────┘ │ │ Global Avg Pool │ │ │ │ │ ┌─────────▼─────────┐ │ │ │ 🎯 FC HEAD │ 2048 → num_classes │ │ └───────────────────┘ │ └─────────────────────────────────────────────────────────────┘ Each bottleneck block: ┌──────────────────────────────────────────────────┐ │ 1×1 Conv (expand) → 3×3 GroupConv (groups=32) │ │ → 1×1 Conv (compress) + Skip Connection │ └──────────────────────────────────────────────────┘ ``` --- ## 🚀 Quick Start ### 1️⃣ Install dependencies ```bash # Clone the repo git clone https://github.com/accel-reg/deepfake-detection.git cd deepfake-detection # Install requirements pip install -r requirements.txt ```

📦 What's in requirements.txt?

``` torch>=1.13 torchvision>=0.14 Pillow opencv-python huggingface_hub ```

--- ### 2️⃣ Load the model ```python import torch from model import DeepfakeDetector # from the repo # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ # Option A — load local file # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ model = DeepfakeDetector() model.load_state_dict(torch.load("ig.bin", map_location="cpu")) model.eval() # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ # Option B — pull from HuggingFace 🤗 # ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ from huggingface_hub import hf_hub_download path = hf_hub_download(repo_id="accel69/depfake-detection", filename="ig.bin") model = DeepfakeDetector() model.load_state_dict(torch.load(path, map_location="cpu")) model.eval() ``` --- ### 3️⃣ Preprocess & predict ```python from torchvision import transforms from PIL import Image # ── Standard ImageNet preprocessing ─────────────────────────── transform = transforms.Compose([ transforms.Resize(256), transforms.CenterCrop(224), transforms.ToTensor(), transforms.Normalize( mean=[0.485, 0.456, 0.406], std =[0.229, 0.224, 0.225] ), ]) # ── Predict ──────────────────────────────────────────────────── img = Image.open("face.jpg").convert("RGB") x = transform(img).unsqueeze(0) # → (1, 3, 224, 224) with torch.no_grad(): probs = torch.softmax(model(x), dim=1) pred = probs.argmax(dim=1).item() label = "🚨 FAKE" if pred == 1 else "✅ REAL" confidence = probs[0, pred].item() print(f" Result : {label}") print(f" Confidence : {confidence:.2%}") ``` --- ## 🖼️ Inference Examples

📁 Batch inference on a folder

```python from pathlib import Path image_dir = Path("frames/") results = {"real": 0, "fake": 0} for img_path in sorted(image_dir.glob("*.jpg")): img = Image.open(img_path).convert("RGB") x = transform(img).unsqueeze(0) with torch.no_grad(): probs = torch.softmax(model(x), dim=1) is_fake = probs.argmax().item() == 1 confidence = probs.max().item() label = "🚨 FAKE" if is_fake else "✅ REAL" results["fake" if is_fake else "real"] += 1 print(f" {img_path.name:<35} {label} ({confidence:.2%})") print(f"\n 📊 Summary — ✅ Real: {results['real']} | 🚨 Fake: {results['fake']}") ```

⚡ GPU acceleration

```python device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model = model.to(device) print(f" 🔥 Running on : {device}") print(f" ⚡ CUDA cores : {torch.cuda.get_device_name(0) if torch.cuda.is_available() else 'N/A'}") # Move input to same device x = x.to(device) with torch.no_grad(): probs = torch.softmax(model(x), dim=1) ```

🎥 Video frame-by-frame analysis

```python import cv2 cap = cv2.VideoCapture("video.mp4") fake_frames = 0 total_frames = 0 print(" 🎬 Analysing video...") while cap.isOpened(): ret, frame = cap.read() if not ret: break img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)) x = transform(img).unsqueeze(0) with torch.no_grad(): pred = model(x).argmax(dim=1).item() fake_frames += pred total_frames += 1 cap.release() fake_pct = fake_frames / total_frames verdict = "🚨 LIKELY DEEPFAKE" if fake_pct > 0.5 else "✅ LIKELY REAL" print(f"\n ┌─────────────────────────────────┐") print(f" │ 🎬 Total frames : {total_frames:<12}│") print(f" │ 🚨 Fake frames : {fake_frames:<12}│") print(f" │ ✅ Real frames : {total_frames-fake_frames:<12}│") print(f" │ 📊 Fake ratio : {fake_pct:<11.1%} │") print(f" │ 🏁 Verdict : {verdict:<12}│") print(f" └─────────────────────────────────┘") ```

--- ## 🏋️ Training Details ``` Training Pipeline ══════════════════════════════════════════════════════ 📦 Backbone Instagram WSL ResNeXt-101 32×8d 🖼️ Resolution 224 × 224 RGB 📐 Normalisation ImageNet mean [0.485 0.456 0.406] std [0.229 0.224 0.225] 📉 Loss function Cross-Entropy 🔄 Augmentation Horizontal flip · Colour jitter Random crop · Rotation ``` | ⚙️ Hyperparameter | 📋 Value | |:---|:---| | 🧱 Backbone init | Instagram WSL pretrained (WSL-Images) | | 📷 Input resolution | 224 × 224 | | 📐 Normalisation | ImageNet mean / std | | 📉 Loss | Cross-Entropy | | 🔄 Augmentations | Flip, colour jitter, random crop | > 📖 Full configs, dataset prep scripts and training logs → **[GitHub Repository](https://github.com/accel-reg/deepfake-detection)** --- ## ⚠️ Limitations > 🚧 Read before deploying in any production or real-world system. | ⚠️ Risk | 📋 Details | |:---|:---| | 🆕 **Novel forgery methods** | May not detect unseen GAN/diffusion techniques | | 📐 **Alignment sensitivity** | Poor face crop → lower accuracy. Use a dedicated face detector first | | 🌍 **Distribution shift** | Different cameras, compression, or lighting may degrade results | | ⚖️ **Demographic bias** | Not audited across demographic groups — evaluate independently | | 🔁 **No temporal context** | Frame-level only — no multi-frame consistency modelling | --- ## 🎯 Intended Use

### ✅ Good uses - 🎓 Academic deepfake research - 📰 Media integrity & journalism tools - 🏆 Benchmarking forgery detectors - 🔬 CV/ML research pipelines

### ❌ Not intended for - 🚫 Automated moderation without human review - 🚫 Surveillance or individual profiling - 🚫 Legal evidence without expert validation - 🚫 Any application that could harm individuals

--- ## 📜 License & Citation

Released under the **MIT License** — free to use, modify, and distribute with attribution.

```bibtex @misc{ig-deepfake-detection-2025, author = {accel69}, title = {ig.bin — Deepfake Face Detection with ResNeXt-101 32x8d}, year = {2025}, publisher = {HuggingFace}, url = {https://huggingface.co/accel69/depfake-detection} } ``` ---

**[🐙 GitHub](https://github.com/accel-reg/deepfake-detection)** · **[🤗 HuggingFace](https://huggingface.co/accel69/depfake-detection)** · **MIT License** _{⭐ Star the repo if this helped you!}