Deepfakes and Audio Processing
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Questions and Answers

What does the term 'deepfake' refer to?

  • Digitally altered videos (correct)
  • Digitally altered text
  • Digitally altered images
  • Digitally altered audio

    • Audio deepfake detection aims to distinguish genuine utterances from fake ones via machine learning techniques.

    True

    What is the main purpose of text-to-speech (TTS) models?

    synthesize intelligible and natural speech from any arbitrary text

    Voice conversion (VC) technologies aim to alter the _______ of a speaker.

    <p>identity</p> Signup and view all the answers

    What are the two main categories that previous studies have divided features used for detecting fake attacks into?

    <p>Short-term spectral features and long-term spectral features</p> Signup and view all the answers

    Which category of features is mainly inadequate in capturing temporal characteristics of speech feature trajectories?

    <p>Short-term spectral features</p> Signup and view all the answers

    Short-term spectral features are typically extracted from short frames with durations of 20-30 ms and describe the short-term _____ involving an acoustic correlate of voice timbre.

    <p>spectral envelope</p> Signup and view all the answers

    Long-term spectral features capture short-range information from speech signals.

    <p>False</p> Signup and view all the answers

    What is voice conversion (VC)?

    <p>Cloning a person's voice digitally to change the timbre and prosody to that of another speaker while keeping the content of the speech the same.</p> Signup and view all the answers

    Which type of deepfake audio involves changing the emotion of the speech while keeping other information the same?

    <p>Emotion fake</p> Signup and view all the answers

    Partially fake audio focuses on changing the speaker's identity in an utterance.

    <p>False</p> Signup and view all the answers

    Voice conversion technologies include statistical parametric ____, frequency warping, and unit-selection.

    <p>model</p> Signup and view all the answers

    What are some examples of prosodic features used to detect fake speech?

    <p>Fundamental Frequency</p> Signup and view all the answers

    What type of features are mainly composed of short-term magnitude and phase based features?

    <p>Short-term spectral features</p> Signup and view all the answers

    Short-term spectral features are mainly computed by applying the short-time Fourier transform (STFT) on a speech signal assumed to be quasi-stationary within a short period of ____. (Fill in the blank)

    <p>25ms</p> Signup and view all the answers

    Match the following phase-based features with their descriptions:

    <p>Baseband Phase Difference (BPD) = Provides stable time-derivative phase information Relative Phase Shift (RPS) = Reflects the 'phase shift' of harmonic components Pitch Synchronous Phase (PSP) = Extracted from phase spectrum using cosine function and DCT</p> Signup and view all the answers

    What is CQCC obtained from?

    <p>DCT of the log power magnitude spectrum derived by CQT</p> Signup and view all the answers

    F0 is also known as pitch. (True/False)

    <p>True</p> Signup and view all the answers

    What did Xue et al. propose for fake speech detection in 2022?

    <p>Discriminative features of the F0 subband</p> Signup and view all the answers

    Tomilov et al. obtained promising results using ___ features for detecting replay attacks of ASVspoof 2021.

    <p>LEAF</p> Signup and view all the answers

    What motivates researchers to extract deep embedding features from self-supervised speech models?

    <p>obtaining annotated speech data or fake utterances is costly and technically demanding</p> Signup and view all the answers

    Which classic pattern classification approaches have been employed to detect fake speech?

    <p>Random forest (RF)</p> Signup and view all the answers

    SVM classifies are not robust to artificial signal spoofing attacks.

    <p>True</p> Signup and view all the answers

    XLS-R based features are extracted from the pre-trained XLS-R models, which is a variant of ________.

    <p>Wav2vec2.0</p> Signup and view all the answers

    What is the back-end classification mainly based on in the latest fake audio detection systems?

    <p>deep learning methods</p> Signup and view all the answers

    Which architectural feature is generally used in the back-end classification of fake audio detection systems?

    <p>Convolutional Neural Network (CNN)</p> Signup and view all the answers

    LCNN stands for Light CNN which is used as a baseline model for fake audio detection in ASVspoof ___.

    <p>2017</p> Signup and view all the answers

    In the Res2Net based classifiers, the feature maps within one ResNet block are not split into multiple channel groups connected by a residual-like connection.

    <p>False</p> Signup and view all the answers

    Match the following innovative models with their description:

    <p>RawNet2 = Convolutional neural network with residual blocks and SincNet SENet = Focuses on adaptively modeling inter-dependencies between channels PC-DARTS = Variant of DARTS with partial channel connections Res2Net = Incorporates ResNet blocks where feature maps are split into multiple channel groups</p> Signup and view all the answers

    What is the name of the squeeze-and-excitation Rawformer proposed by Liu et al.?

    <p>SE-Rawformer</p> Signup and view all the answers

    What is the core idea of SAMO proposed by Ding et al.?

    <p>Clustering real utterances around speaker attractors</p> Signup and view all the answers

    RawBoost is a model based on RawGAT-ST and RawNet2 systems.

    <p>False</p> Signup and view all the answers

    Which method proposes to make the model learn new fake attacks incrementally without accessing old data? Detecting Fake Without ____ (DFWF)

    <p>Forgetting</p> Signup and view all the answers

    In the year 2017, which feature(s) were used for the LA task with an EER (%) of 6.73?

    <p>LPCC</p> Signup and view all the answers

    In 2019, the EER (%) for the ASVspoof task was 0.59 using the ResNet classifier?

    <p>False</p> Signup and view all the answers

    What was the EER (%) for the LF task in the year 2021?

    <p>21.70</p> Signup and view all the answers

    In 2015, the EER (%) for the LA task was 1.21 using ______ as the classifier.

    <p>GMM</p> Signup and view all the answers

    Study Notes

    Audio Deepfake Detection: A Survey

    • Audio deepfake detection is an emerging topic, and despite promising performance, it remains an open problem.
    • The survey aims to provide a systematic overview of developments in audio deepfake detection, including competitions, datasets, features, classifiers, and evaluation.

    Types of Deepfake Audio

    • There are five kinds of deepfake audio:
      • Text-to-speech (TTS): aims to synthesize natural speech from text using machine learning models.
      • Voice conversion (VC): aims to change the timbre and prosody of a speaker's speech to another speaker.
      • Emotion fake: aims to change the emotion of the speech while keeping other information intact.
      • Scene fake: aims to change the acoustic scene of an original utterance with another scene.
      • Partially fake: aims to distinguish between partially fake and real audio.

    Competitions and Datasets

    • Competitions and datasets include:
      • ASVspoof 2015, 2017, 2019, and 2021: evaluation of audio deepfake detection systems.
      • ADD 2022: includes three tasks: low-quality fake audio detection, partially fake audio detection, and audio fake game.
      • ASVspoof challenges: LA (logical access) task, PA (physical access) task, and speech deepfake detection task.
      • ADD 2022 challenges: LF (low-quality fake) task, PF (partially fake) task, and FG (audio fake game) task.

    Features and Classifiers

    • Discriminative features for audio deepfake detection include:
      • CQCC (constant-Q cepstral coefficients)
      • LFCC (linear frequency cepstral coefficients)
      • Raw audio features
      • Wav2vec2.0
    • Representative classifiers include:
      • GMM (Gaussian mixture model)
      • LCNN (light convolutional neural network)
      • RawNet2
      • ResNet + Openmax

    Challenges and Future Directions

    • Remaining challenges in audio deepfake detection include:
      • Lack of large-scale datasets in the wild
      • Poor generalization of existing detection methods to unknown fake attacks
      • Interpretability of detection results
    • Future research should focus on addressing these challenges and developing more effective detection methods.### Partially Fake Utterances
    • Partially fake utterances are generated by manipulating the original utterances with genuine or synthesized audio clips.
    • The speaker of the original utterance and fake clips is the same person.
    • The synthesized audio clips, while keeping the speaker identity unchanged, are used to generate partially fake utterances.

    Competitions

    • A series of competitions have played a key role in accelerating the development of audio deepfake detection.
    • The ASVspoof and ADD challenges have been designed to protect ASV systems or human listeners from spoofing or deceiving.

    Benchmark Datasets

    • Many early studies designed spoofed datasets to develop spoofing countermeasures for ASV systems.
    • The ASVspoof 2015 involves logical access to detect spoofed audio from the perspective of protecting ASV systems.
    • The ADD 2022 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL), and deepfake algorithm recognition (AR).

    Characteristics of Representative Datasets

    • The characteristics of representative datasets on audio deepfake detection include:
      • Language (English, Chinese, etc.)
      • Goal (Detection, Game fake, Forensics, etc.)
      • Fake types (VC, TTS, partially fake, etc.)
      • Condition (Clean, Noisy, etc.)
      • Format (FLAC, WAV, etc.)
      • Sampling rate (SR, Hz)
      • Average length of utterances (SL, s)
      • Number of hours
      • Number of real/fake utterances
      • Number of real/fake speakers

    Evaluation Metrics

    • Equal Error Rate (EER) is used as the evaluation metric for audio deepfake detection tasks.
    • EER is defined as the error rate at the threshold θEER, where the two detection error rates are equal.
    • The final ranking is in terms of the weighted EER (WEER), which is defined as the weighted average of EER of the two rounds.

    Discriminative Features

    • The feature extraction module is a key component of the pipeline detector.
    • The goal of feature extraction is to learn discriminative features that capture audio fake artifacts from speech signals.
    • Features are divided into four categories: short-term spectral features, long-term spectral features, prosodic features, and deep features.
    • Short-term spectral features are computed mainly by applying the short-time Fourier transform (STFT) on a speech signal.
    • Magnitude-based features are directly derived from the magnitude spectrum, while phase-based features are derived from the phase spectrum.
    • Long-term spectral features capture long-range information from speech signals.
    • Prosodic features span over longer segments, such as phones, syllables, words, and utterances.
    • Deep features are extracted via deep neural network-based models.

    Short-term Spectral Features

    • Short-term spectral features are mainly composed of short-term magnitude and phase-based features.
    • Magnitude-based features include:
      • Magnitude spectrum
      • Log magnitude spectrum (LMS)
      • Power spectrum
      • Log power spectrum (LPS)
      • Cepstrum (Cep)
      • Filter bank-based cepstral coefficients (FBCC)
      • All-pole modeling-based cepstral coefficients (APCC)
      • Subband spectral (SS) features
    • Phase-based features include:
      • Instantaneous frequency (IF) spectrum
      • Group delay (GD) spectrum### Phase Features
    • Phase Spectrum does not have stable patterns for fake audio detection due to phase warping
    • Post-processing methods are used to generate short-term phase-based features including:
    • Group Delay (GD) based features: GD, Modified Group Delay (MGD), MGD cepstral coefficients (MGDCC), and All-Pole Group Delay (APGD)
    • Cosine-Phase (CosPhase) features
    • Instantaneous Frequency (IF)
    • Baseband Phase Difference (BPD)
    • Relative Phase Shift (RPS)
    • Pitch Synchronous Phase (PSP)

    Long-term Spectral Features

    • Short-term spectral features are not good at capturing temporal characteristics of speech feature trajectories
    • Long-term spectral features are used to capture long-range information from speech signals
    • Four types of long-term spectral features:
    • STFT based features: Modulation features, Shifted Delta Coefficients (SDC), Frequency Domain Linear Prediction (FDLP), and Local Binary Pattern (LBP) features
    • CQT based features: CQT spectrum, CQT cepstral coefficients (CQCC), extended CQCC (eCQCC), and inverted CQCC (ICQCC)
    • HT based features: Mean Hilbert Envelope Coefficients (MHEC)
    • WT based features: Mel Wavelet Packet Coefficients (MWPC), Cochlear Filter Cepstral Coefficients (CFCC), and CFCC plus Instantaneous Frequency (CFCCIF)

    Prosodic Features

    • Prosody refers to non-segmental information of speech signals, including:
    • Syllable stress
    • Intonation patterns
    • Speaking rate
    • Rhythm
    • Important prosodic parameters:
    • Fundamental frequency (F0)
    • Duration (e.g. phone duration, pause statistics)
    • Energy distribution
    • F0 is also known as pitch, and its pattern is different between synthetic speech and natural speech

    Deep Features

    • Learnable spectral features:
    • Partially learnable spectral features: extracted by training a filterbank matrix with a spectrogram
    • Fully learnable spectral features: learned directly from raw waveforms
    • Supervised embedding features:
    • Spoof embeddings
    • Emotion embeddings
    • Speaker embeddings
    • Pronunciation embeddings
    • Self-supervised embedding features: learned from self-supervised speech models using unannotated speech data

    Classification Algorithms

    • Traditional classification algorithms
    • Deep learning classification algorithms
    • The backend classifier is important for audio deepfake detection, aiming to learn high-level feature representation and model excellent discrimination capabilities

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    Related Documents

    Audio_Survey.pdf

    Description

    Test your knowledge on deepfakes, audio deepfake detection, and speech processing technologies like text-to-speech models and voice conversion. Learn about the main purposes and categories of features used for detecting fake attacks.

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