cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13853860
title: Codecfake dataset - training set (part 3 of 3)

description: This dataset is the training set (part 3 of 3) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.15468/dl.a4bzmm
title: Occurrence Download

description: A dataset containing 38583 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Triturus marmoratus (Latreille, 1800))",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 38583 records from 726 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033710-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.13854082
title: Corporate ESG Performance, Green Innovation, and Financial Performance: The Moderating Role of Financial Risk
DOI: 10.5281/zenodo.13854083
title: Corporate ESG Performance, Green Innovation, and Financial Performance: The Moderating Role of Financial Risk
DOI: 10.5281/zenodo.11169871
title: Codecfake dataset - development set

description: This dataset is the development set of the Codecfake dataset, corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.5281/zenodo.13841216
title: Codecfake dataset - development set

description: This dataset is the development set of the Codecfake dataset, corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.15468/dl.ekf9n7
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Scutiger wanglangensis Ye & Fei, 2007)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033711-240906103802322/datasets/export for details.
DOI: 10.5281/zenodo.11169780
title: Codecfake dataset - test set (part 1 of 2)

description: This dataset is the test set (part 1 of 2) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.5281/zenodo.13838823
title: Codecfake dataset - test set (part 1 of 2)

description: This dataset is the test set (part 1 of 2) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.5281/zenodo.11125028
title: Codecfake dataset - test set (part 2 of 2)

description: This dataset is the test set (part 2 of 2) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.5281/zenodo.11125029
title: Codecfake dataset - test set (part 2 of 2)

description: This dataset is the test set (part 2 of 2) of the Codecfake dataset , corresponding to the manuscript "The Codecfake Dataset and Countermeasures for Universal Deepfake Audio Detection".

Abstract

With the proliferation of Audio Language Model (ALM) based deepfake audio, there is an urgent need for effective detection methods. Unlike traditional deepfake audio generation, which often involves multi-step processes culminating in vocoder usage, ALM directly utilizes neural codec methods to decode discrete codes into audio. Moreover, driven by large-scale data, ALMs exhibit remarkable robustness and versatility, posing a significant challenge to current audio deepfake detection (ADD)models. To effectively detect ALM-based deepfake audio, we focus on the mechanism of the ALM-based audio generation method, the conversion from neural codec to waveform. We initially construct the Codecfake dataset, an open-source large-scale dataset, including two languages, millions of audio samples, and various test conditions, tailored for ALM-based audio detection. Additionally, to achieve universal detection of deepfake audio and tackle domain ascent bias issue of original SAM, we proposethe CSAM strategy to learn a domain balanced and generalized minima. Experiment results demonstrate that co-training on Codecfake dataset and vocoded dataset with CSAM strategy yield the lowest average Equal Error Rate (EER) of 0.616% across all test conditions compared to baseline models.

Codecfake Dataset

Due to platform restrictions on the size of zenodo repositories, we have divided the Codecfake dataset into various subsets as shown in the table below:




Codecfake dataset
description
link


training set (part 1 of 3) & label
train_split.zip & train_split.z01 - train_split.z05
https://zenodo.org/records/13838106


training set (part 2 of 3)
train_split.z06 - train_split.z10
https://zenodo.org/records/13841652


training set (part 3 of 3)
train_split.z11 - train_split.z16
https://zenodo.org/records/13853860


development set
dev_split.zip & dev_split.z01 - dev_split.z02
https://zenodo.org/records/13841216


test set (part 1 of 2)
Codec test: C1.zip - C6.cip & ALM test: A1.zip - A3.zip
https://zenodo.org/records/13838823


test set (part 2 of 2)
Codec unseen test: C7.zip
https://zenodo.org/records/11125029




Countermeasure

The source code of the countermeasure and pre-trained model are available on GitHub https://github.com/xieyuankun/Codecfake.

 

The Codecfake dataset and pre-trained model are licensed with CC BY-NC-ND 4.0 license.
DOI: 10.15468/dl.8cadat
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Hynobius glacialis Lai & Lue, 2008)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033712-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cre8c7
title: Occurrence Download

description: A dataset containing 158 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Daimio tethys (Ménétriés, 1857)",
 "Year 2000-2024"
 ]
}

 The dataset includes 158 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033713-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pu3zb3
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Nototriton picucha Townsend, Medina-Flores, Murillo & Austin, 2011)",
 "BasisOfRecord is one of (Specimen, Human Observation, Observation, Machine Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 4 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033714-240906103802322/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.