Real-Time Deepfake Audio Detection System Development in Communication Platforms

Khái niệm cốt lõi

Developing real-time deepfake audio detection models for communication platforms is crucial for enhancing audio stream security and ensuring robust detection capabilities.

Tóm tắt

The study focuses on developing real-time deepfake audio detection models using Resnet and LCNN architectures. It assesses the viability of employing static deepfake audio detection models in real-time communication platforms. The study highlights challenges with static models in dynamic scenarios, proposing strategies for enhancing model performance. The software application developed enables cross-platform compatibility for real-time execution. Results show promising performances compared to ASVspoof 2019 challenge baselines.

Thống kê

LA model achieves an EER of 7.39.
PA model exhibits an EER of 4.38.
Precision for PA model: 0.39, Recall: 0.41, F-score: 0.40.
Precision for LA model: 0.48, Recall: 0.42, F-score: 0.45.

Trích dẫn

"Our study contributes to the advancement of audio stream security by ensuring robust detection capabilities in dynamic, real-time communication scenarios."
"The results underscore the effectiveness of our static PA and LA models in distinguishing between real and fake voices within the context of the ASVspoof 2019 dataset."

Thông tin chi tiết chính được chắt lọc từ

Towards the Development of a Real-Time Deepfake Audio Detection System in Communication Platforms

by Jonat John M... lúc arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.11778.pdf

Yêu cầu sâu hơn

How can training data variability be improved to enhance real-time deepfake audio detection?

To improve training data variability for enhancing real-time deepfake audio detection, several strategies can be implemented. Firstly, augmenting the existing ASVspoof 2019 dataset with additional diverse datasets such as Spotify podcast collection, FoR, WaveFake, LRPD, In-the-wild, and VoxCeleb2 can introduce a wider range of acoustic scenarios. This augmentation helps in exposing the model to various types of spoofing attacks and conditions that are more representative of real-world communication platforms.
Furthermore, developing novel data augmentation techniques specific to communication platform artifacts like network-induced delays, packet loss, or compression artifacts is crucial. By simulating these platform-specific challenges during training data generation processes, the model becomes more robust and adaptable to the unique characteristics of real-time audio streams in communication environments.
Lastly, integrating generative models like variational autoencoder (VAE) into the training process can help synthesize additional training samples. These generative models create synthetic data points that provide the model with a broader set of examples for distinguishing between genuine and fake audio in dynamic real-time scenarios within communication platforms.

What are the limitations of using static deepfake audio models in dynamic communication platforms?

The limitations of using static deepfake audio models in dynamic communication platforms stem from their lack of adaptability to real-time scenarios characterized by continuous conversational speech data. Static deepfake models trained on datasets with limited variations fail to consistently perform well when deployed in dynamic environments due to several reasons:

Limited Generalization: Models trained on static datasets may overfit on specific spoofing attacks present in those datasets but may struggle when faced with unseen attack conditions prevalent in live conversations or varied acoustic settings typical of dynamic platforms.

Lack of Dynamic Awareness: Static models do not account for rapid changes or fluctuations inherent in live speech interactions within communication platforms where multiple speakers engage simultaneously or under different environmental conditions.

Inadequate Variability: The constrained number of speakers and recording conditions found in static datasets hinder the model's ability to generalize across diverse speaker profiles and acoustic backgrounds encountered during real-time communications.

Performance Degradation: Due to their inability to handle varying acoustic backgrounds and noise levels commonly found during live conversations on communication platforms like Teams meetings or video calls, static models may exhibit reduced performance accuracy leading to false predictions.

How can generative models be integrated to improve real-time deepfake audio detection beyond the current study's scope?

Integrating generative models offers a promising avenue for improving real-time deepfake audio detection beyond this study's scope by leveraging advanced techniques for synthesizing realistic yet artificial samples that challenge traditional detection methods:

Enhanced Data Augmentation: Generative adversarial networks (GANs) could generate synthetic samples mimicking complex background noises or speaker variations not adequately represented in existing datasets used for training conventional detectors.

Adversarial Training: Incorporating adversarial learning mechanisms where generative networks produce challenging spoofed audios while discriminators aim at correctly classifying them could lead to more robust detectors capable of handling sophisticated attacks.

3 .Dynamic Adaptation: Generative approaches enable continual adaptation by generating new fake samples based on evolving threats observed during deployment without requiring extensive retraining cycles each time a new attack variant emerges.
4 .Transfer Learning: Pre-trained generative architectures fine-tuned specifically for creating diverse sets of deceptive audios could serve as valuable resources across different domains beyond standard ASVspoof challenges.
These advancements empower AI systems with enhanced capabilities essential for combating increasingly sophisticated forms of malicious activities targeting voice-centric applications within modern-day digital ecosystems such as online conferencing tools and social media channels where ensuring secure voice communications is paramount.