Deepfake Detection System: An End-to-End Solution for Multimodal Forgery Content Detection

Introducing a multimodal Deepfake detection system based on PyTorch and TensorFlow, supporting forgery recognition for three modalities (audio, image, and text), using various deep learning architectures such as BiLSTM, CNN, and Transformer, and providing a Streamlit interactive interface. This project is suitable for learning reference and prototype verification, offering a complete end-to-end example for detection technologies in the AI security field.

With the rapid development of generative AI technology, the threshold for Deepfake content production has dropped sharply, and the authenticity of digital content is facing unprecedented challenges. To address this demand, this project provides a unified detection framework covering three major modalities (audio, image, text), integrating multiple mature technical routes. Developed in Python, the project is built on both PyTorch and TensorFlow/Keras frameworks, and uses Streamlit to lower the barrier to use. It should be noted that this project is more suitable as a learning reference and prototype verification tool rather than a production-level deployment solution.

Audio detection is the most mature part of the project, implementing three neural network architectures:

1. BiLSTM temporal modeling: Takes 20-dimensional MFCC features as input, uses bidirectional LSTM to learn temporal dependencies before and after, suitable for detecting speech synthesis forgeries;
2. CNN spectral feature extraction: Uses a three-layer convolution structure to extract hierarchical spectral features, excels at capturing local patterns, and works well for vocoder or waveform splicing forgeries;
3. Transformer self-attention: Models global dependencies through positional encoding and multi-layer encoders, balancing model capacity and efficiency. All three models support a 16kHz sampling rate and process 150-frame segments (padded/truncated to a uniform size).

• Image detection: Based on a CNN classifier (three layers of convolution + ReLU + pooling), lightweight and suitable for fast inference; reserves interfaces for pre-trained models (ResNet/VGG, etc.) to support transfer learning;
• Text detection: Based on TensorFlow/Keras, targeting AI-generated text (fake news, phishing emails), uses word embedding + recurrent/full connection layers for classification; adapts to Keras version differences to ensure code robustness.

The project uses a modular structure (separation of model, preprocessing, and inference), relying on libraries such as librosa (audio), Pillow/torchvision (image), and Keras (text). A Dev Container is configured to avoid dependency conflicts. The Streamlit interface supports zero-code interaction (upload files for real-time detection). Deployment recommendations: Use a Python 3.8+ environment; after installing requirements, run streamlit run main.py; to run on GPU, modify DEVICE to CUDA.

The project has the following limitations:

1. The model architecture is relatively basic, without introducing cutting-edge technologies (such as wav2vec2.0, BERT, etc.);
2. Lack of training data and pre-trained weights; users need to prepare them on their own;
3. The three modalities are detected independently, without cross-modal joint analysis (e.g., audio-video consistency detection). Improvement suggestions: Replace with advanced models, add pre-trained weights, and implement multimodal fusion.

Despite its limitations, the project is of high value to beginners, fully demonstrating an end-to-end process (preprocessing → model → deployment → interface). It is suitable for developers in the AI security field to get started; they can gradually replace it with advanced architectures, add data augmentation, etc. The open-source project promotes the popularization of defense technologies and contributes to the "arms race" in the AI security field. Project address: https://github.com/Dhruba2004/deepfake_detection_system.