abhijithjadhav/Deepfake_detection_using_deep_learning：该项目旨在使用RestNext和LSTM等深度学习技术检测视

abhijithjadhav

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GNU General Public License v3.0

技术解读

以下内容由 GitRead 中文社区编辑团队基于项目 README 与官方资料整理，结合中文开发者的常见使用场景补充技术选型建议。具体以项目官方文档为准。

深度分析：Deepfake检测项目

项目背景与痛点

随着技术的发展，深度伪造（Deepfake）视频技术已经被广泛应用于网络世界的各个角落。不仅有娱乐用途，更引发了信任危机，挑战现实世界的真理来源。为此，Deepfake检测的项目旨在通过深度学习技术识别伪造的视频内容，确保信息安全。使用诸如ResNet和LSTM等深度学习模型，该项目实现了深度伪造视频的检测，有助于建立一个更加可信的网络环境。

核心技术揭秘

该项目采用先进的深度学习技术来探测视频中的伪造痕迹。其核心架构设计如下：

graph TD A[视频流输入] --> B[ResNext特征提取] B --> C{"特征向量处理"} C -->|"训练模式"| D[LSTM层训练] D --> E[分布预测] C -->|"检测模式"| F[特征比对] F --> G[检测结果输出]

技术栈和细节分析：

ResNet预训练模型：通过使用ResNet提取视频帧的特征，实现高效的特征向量获取。
特征向量处理：进一步利用LSTM对提取的特征向量进行处理，通过训练模式，优化模型以识别深伪特性。
LSTM层训练：训练过程是核心环节之一，使用提取出的特征进行深度学习，以生成可靠的预测。

该模型的关键在于有效利用预训练模型进行特征提取，并通过后续的LSTM模型训练确保高质量的数据处理和检测效果。

功能亮点与差异

在这个项目中，项目的独特之处在于结合了先进的CNN（如ResNet）与LSTM模型来处理数据，为视频内容的真实性检测提供了一种新的解决方案。这不同于仅使用传统的机器学习或者单独使用某种深度学习模型的方法。通过结合这两种技术，它能够在实现高精度检测的同时，确保模型的灵活性和适应性。

应用场景与落地建议

该项目对于应用于安全监控的场景尤其有意义，比如视频新闻验证、社交媒体内容审查等。不过，在具体的落地应用中，需要注意的是，训练模型所需的硬件资源较高，包括对内存和计算能力的需求。同时，数据的质量和多样性也是取得良好检测效果的关键，数据集需涵盖广泛的真实与伪造视频，以便模型能够准确理解不同类型的伪造技术。

综合评价

项目的优点在于系统化整合了现代深度学习技术，并且应用场景广泛且实际可行。然而，其缺点也很明显，包括训练资源消耗大，依赖高质量的数据集，以及在某些特定场景下的适应性和泛化能力可能还需要进一步优化。总之，该方案为解决Deepfake检测的问题提供了一个强有力的工具，这在当前的技术背景下显得尤为宝贵。

关于

GitHub 原始描述（英文）

This projects aims in detection of video deepfakes using deep learning techniques like RestNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained RestNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.

描述取自 GitHub 官方 API，保持仓库作者书写的原文。

简要分析

热度分

1382

价值分

608

活跃状态

活跃

deep-learning deepfake-detection deepfake-detection-challenge deepfakes

GitHub

语言占比

CSS

Dockerfile

HTML

Jupyter Notebook

Python

Shell

贡献者

abhijithjadhav

allcontributors[bot]

vthonte

README

Deepfake detection using Deep Learning (ResNext and LSTM)

Give a Star⭐ to repo and Don’t forget to

Latest Update

We have dockerised the Django Application now you can spin up a container within seconds without worring about dependencies

1. Introduction

This projects aims in detection of video deepfakes using deep learning techniques like ResNext and LSTM. We have achived deepfake detection by using transfer learning where the pretrained ResNext CNN is used to obtain a feature vector, further the LSTM layer is trained using the features. For more details follow the documentaion.

You can also watch this Youtube video to get a better intuition about the project. You can watch this playList for step by step installation.

You can read more about the project on

2. Directory Structure

For ease of understanding the project is structured in below format

Deepfake_detection_using_deep_learning
    |
    |--- Django Application
    |--- Model Creation
    |--- Documentaion

Django Application
- This directory consists of the django made application of our work. Where a user can upload the video and submit it to the model for prediction. The trained model performs the prediction and the result is displayed on the screen.
Model Creation
- This directory consists of the step by step process of creating and training a deepfake detection model using our approach.
Documentation
- This directory consists of all the documentation done during the project

3. System Architecture

4. Demo

You can watch the youtube video for demo

5. Our Results

Model Name	No of videos	No of Frames	Accuracy
model_84_acc_10_frames_final_data.pt	6000	10	84.21461
model_87_acc_20_frames_final_data.pt	6000	20	87.79160
model_89_acc_40_frames_final_data.pt	6000	40	89.34681
model_90_acc_60_frames_final_data.pt	6000	60	90.59097
model_91_acc_80_frames_final_data.pt	6000	80	91.49818
model_93_acc_100_frames_final_data.pt	6000	100	93.58794

6. Contributors

_{Abhijit Jadhav}
📆

_{Vishwesh Thonte}
🚧

6.1 Offline Contributors

Jay Patel
Hitendra Patil
Abhishek Patange

7. License

8. We welcome Open Source Contribution.

Below are the some changes that can be applied to the project. New Ideas will be appreciated.

Deploying the applications in free cloud
Creating open source API for detection
Batch processing of entire video instead of processing first ‘x’ frames.
Optimizing the code for faster execution.

Completed

Dockerizing the app
Enabling working of project on Non Cuda Computers. i.e on normal or AMD GPUs

Deepfake_detection_using_deep_learning

技术解读

深度分析：Deepfake检测项目

项目背景与痛点

核心技术揭秘

功能亮点与差异

应用场景与落地建议

综合评价

关于

简要分析

语言占比

贡献者

README

Deepfake detection using Deep Learning (ResNext and LSTM)

Give a Star⭐ to repo and Don’t forget to

Latest Update

We have dockerised the Django Application now you can spin up a container within seconds without worring about dependencies

1. Introduction

2. Directory Structure

3. System Architecture

4. Demo

You can watch the youtube video for demo

5. Our Results

6. Contributors

6.1 Offline Contributors

7. License

8. We welcome Open Source Contribution.

Below are the some changes that can be applied to the project. New Ideas will be appreciated.

Completed

9. Dont forget to Star⭐ this repo 😉 and

评论

Deepfake_detection_using_deep_learning

技术解读

深度分析：Deepfake检测项目

项目背景与痛点

核心技术揭秘

功能亮点与差异

应用场景与落地建议

综合评价

相关仓库

关于

简要分析

语言占比

贡献者

README

Deepfake detection using Deep Learning (ResNext and LSTM)

Give a Star⭐ to repo and Don’t forget to

Latest Update

We have dockerised the Django Application now you can spin up a container within seconds without worring about dependencies

1. Introduction

2. Directory Structure

3. System Architecture

4. Demo

You can watch the youtube video for demo

5. Our Results

6. Contributors

6.1 Offline Contributors

7. License

8. We welcome Open Source Contribution.

Below are the some changes that can be applied to the project. New Ideas will be appreciated.

Completed

9. Dont forget to Star⭐ this repo 😉 and

评论