Deepfake-audio-detection Open-source Model - Efficiently Identify Synthetic Voices with an Accuracy of 99.64%

Deepfake Audio Detection

Developed by MelodyMachine

A Deepfake audio detection model fine-tuned on audio folder datasets, capable of efficiently identifying synthetic speech with an evaluation accuracy of 99.64%

Audio Classification

Transformers

Open Source License:Apache-2.0 #High-precision audio classification #Deepfake detection #Voice authenticity verification

Downloads 107

Release Time : 5/31/2024

Model Overview

This model is specifically designed to detect Deepfake synthetic audio by analyzing audio features to distinguish between real and synthetic speech, suitable for content security verification scenarios

Model Features

High-precision detection

Achieves 99.64% accuracy and 99.67% F1 score on the test set, with an ROC AUC of 1.0

Low false positive rate

Recall rate of 99.9%, with only 6 synthetic audio samples misclassified as real speech

Efficient training

Requires only 2 training epochs to achieve optimal performance, supports mixed-precision training

Model Capabilities

Audio authenticity verification

Deepfake speech detection

Binary audio classification

Use Cases

Content security

Social media content moderation

Automatically identifies synthetic speech content on platforms

Reduces missed detection rate by 99.9%

Forensic investigation

Voice evidence verification

Determines whether audio evidence has been tampered with using AI synthesis

Judicial-level detection with 99.6% accuracy

🚀 Deepfake-audio-detection

This model is a fine - tuned version of motheecreator/Deepfake-audio-detection on the audiofolder dataset. It can accurately classify audio, providing high - precision results for deepfake audio detection, which is of great value in ensuring audio authenticity.

🚀 Quick Start

This section provides a general introduction to the model. For more specific usage, please refer to the detailed documentation.

📚 Documentation

Model Performance

This model achieves the following results on the evaluation set:

Loss: 0.0192
Accuracy: 0.9964
Precision: 0.9944
Recall: 0.9990
F1: 0.9967
Auc Roc: 1.0000
Confusion Matrix: [[4974, 34], [6, 6033]]
Classification Report: {'0': {'precision': 0.9987951807228915, 'recall': 0.9932108626198083, 'f1 - score': 0.9959951942330797, 'support': 5008}, '1': {'precision': 0.9943959123125103, 'recall': 0.9990064580228515, 'f1 - score': 0.9966958532958864, 'support': 6039}, 'accuracy': 0.9963791074499864, 'macro avg': {'precision': 0.996595546517701, 'recall': 0.9961086603213298, 'f1 - score': 0.996345523764483, 'support': 11047}, 'weighted avg': {'precision': 0.9963902579447351, 'recall': 0.9963791074499864, 'f1 - score': 0.9963782194960733, 'support': 11047}}

Model Information

Property	Details
Model Type	Fine - tuned version of motheecreator/Deepfake - audio - detection
Training Data	audiofolder
Metrics	Accuracy, Precision, Recall, F1

Training and Evaluation

Training hyperparameters

The following hyperparameters were used during training:

learning_rate: 3e - 05
train_batch_size: 16
eval_batch_size: 16
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 32
optimizer: Adam with betas=(0.9,0.999) and epsilon = 1e - 08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.1
num_epochs: 2
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy	Precision	Recall	F1	Auc Roc	Confusion Matrix	Classification Report
0.1006	0.3621	1000	0.1897	0.9651	0.9424	0.9972	0.9690	0.9989	[[4640, 368], [17, 6022]]	{'0': {'precision': 0.9963495812754992, 'recall': 0.9265175718849841, 'f1 - score': 0.9601655457837558, 'support': 5008}, '1': {'precision': 0.9424100156494523, 'recall': 0.9971849643980791, 'f1 - score': 0.969024056641725, 'support': 6039}, 'accuracy': 0.9651489092061193, 'macro avg': {'precision': 0.9693797984624757, 'recall': 0.9618512681415317, 'f1 - score': 0.9645948012127403, 'support': 11047}, 'weighted avg': {'precision': 0.9668627489395077, 'recall': 0.9651489092061193, 'f1 - score': 0.9650081770023017, 'support': 11047}}
0.07	0.7241	2000	0.0333	0.9916	0.9914	0.9932	0.9923	0.9997	[[4956, 52], [41, 5998]]	{'0': {'precision': 0.9917950770462277, 'recall': 0.9896166134185304, 'f1 - score': 0.9907046476761618, 'support': 5008}, '1': {'precision': 0.991404958677686, 'recall': 0.993210796489485, 'f1 - score': 0.9923070560013236, 'support': 6039}, 'accuracy': 0.9915814248212185, 'macro avg': {'precision': 0.9916000178619568, 'recall': 0.9914137049540077, 'f1 - score': 0.9915058518387427, 'support': 11047}, 'weighted avg': {'precision': 0.9915818132798093, 'recall': 0.9915814248212185, 'f1 - score': 0.9915806270258181, 'support': 11047}}
0.016	1.0862	3000	0.1018	0.9841	0.9727	0.9988	0.9856	0.9998	[[4839, 169], [7, 6032]]	{'0': {'precision': 0.9985555096987206, 'recall': 0.9662539936102237, 'f1 - score': 0.9821392327988635, 'support': 5008}, '1': {'precision': 0.9727463312368972, 'recall': 0.9988408676933267, 'f1 - score': 0.9856209150326798, 'support': 6039}, 'accuracy': 0.9840680727799402, 'macro avg': {'precision': 0.985650920467809, 'recall': 0.9825474306517752, 'f1 - score': 0.9838800739157716, 'support': 11047}, 'weighted avg': {'precision': 0.9844465544410985, 'recall': 0.9840680727799402, 'f1 - score': 0.9840425440154849, 'support': 11047}}
0.0209	1.4482	4000	0.0212	0.9957	0.9950	0.9972	0.9961	0.9999	[[4978, 30], [17, 6022]]	{'0': {'precision': 0.9965965965965966, 'recall': 0.9940095846645367, 'f1 - score': 0.9953014095771269, 'support': 5008}, '1': {'precision': 0.9950429610046265, 'recall': 0.9971849643980791, 'f1 - score': 0.9961128111818707, 'support': 6039}, 'accuracy': 0.995745451253734, 'macro avg': {'precision': 0.9958197788006116, 'recall': 0.995597274531308, 'f1 - score': 0.9957071103794988, 'support': 11047}, 'weighted avg': {'precision': 0.9957472795566846, 'recall': 0.995745451253734, 'f1 - score': 0.9957449738290548, 'support': 11047}}
0.0233	1.8103	5000	0.0192	0.9964	0.9944	0.9990	0.9967	1.0000	[[4974, 34], [6, 6033]]	{'0': {'precision': 0.9987951807228915, 'recall': 0.9932108626198083, 'f1 - score': 0.9959951942330797, 'support': 5008}, '1': {'precision': 0.9943959123125103, 'recall': 0.9990064580228515, 'f1 - score': 0.9966958532958864, 'support': 6039}, 'accuracy': 0.9963791074499864, 'macro avg': {'precision': 0.996595546517701, 'recall': 0.9961086603213298, 'f1 - score': 0.996345523764483, 'support': 11047}, 'weighted avg': {'precision': 0.9963902579447351, 'recall': 0.9963791074499864, 'f1 - score': 0.9963782194960733, 'support': 11047}}

Framework versions

Transformers 4.41.1
Pytorch 2.1.2
Datasets 2.19.1
Tokenizers 0.19.1

📄 License

This model is licensed under the Apache - 2.0 license.

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