mlsanityData to 7 Oct 2026

Partially-Connected Differentiable Architecture Search for Deepfake and Spoofing Detection

arXiv 2104.03123 ↗First posted 7 Apr 2021cs.LG cs.SD eess.AScited by 9

Not on the map. Why?

Abstract (arXiv)

This paper reports the first successful application of a differentiable architecture search (DARTS) approach to the deepfake and spoofing detection problems. An example of neural architecture search, DARTS operates upon a continuous, differentiable search space which enables both the architecture and parameters to be optimised via gradient descent. Solutions based on partially-connected DARTS use random channel masking in the search space to reduce GPU time and automatically learn and optimise complex neural architectures composed of convolutional operations and residual blocks. Despite being learned quickly with little human effort, the resulting networks are competitive with the best performing systems reported in the literature. Some are also far less complex, containing 85% fewer parameters than a Res2Net competitor.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 18 of its 29 references that are in the map. First posted on arXiv 7 Apr 2021.

Builds on

18 of 29 references.

References · 18

  1. 2018DARTS: Differentiable Architecture Searchcited by 1,782
  2. 2016Neural Architecture Search with Reinforcement Learningcited by 1,489
  3. 2019PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Searchcited by 265
  4. 2019ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detectioncited by 173
  5. 2020Searching Central Difference Convolutional Networks for Face Anti-Spoofingcited by 173
  6. 2018Speaker Recognition from Raw Waveform with SincNetcited by 166
  7. 2020End-to-end anti-spoofing with RawNet2cited by 160
  8. 2019STC Antispoofing Systems for the ASVspoof2019 Challengecited by 51
  9. 2020Replay and Synthetic Speech Detection with Res2net Architecturecited by 38
  10. 2019RawNet: Advanced end-to-end deep neural network using raw waveforms for text-independent speaker verificationcited by 33
  11. 2019Deep Residual Neural Networks for Audio Spoofing Detectioncited by 33
  12. 2019ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworkscited by 23
6 more
  1. 2019Adversarial Attacks on Spoofing Countermeasures of automatic speaker verificationcited by 23
  2. 2018t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verificationcited by 19
  3. 2020AutoSpeech: Neural Architecture Search for Speaker Recognitioncited by 17
  4. 2020DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptationcited by 15
  5. 2020Neural Architecture Search For Keyword Spottingcited by 11
  6. 2019Introduction to Voice Presentation Attack Detection and Recent Advancescited by 8

Built on by

Cited by year

Citing papers per year: 2021: 1; 2022: 2; 2023: 1; 2024: 4; 2025: 1.

9 papers.

  1. 2021AASIST: Audio Anti-Spoofing using Integrated Spectro-Temporal Graph Attention Networkscited by 173
  2. 2022Fully Automated End-to-End Fake Audio Detectioncited by 10
  3. 2024VoiceWukong: Benchmarking Deepfake Voice Detectioncited by 5
  4. 2024Lightweight Model Attribution and Detection of Synthetic Speech via Audio Residual Fingerprintscited by 3
  5. 2024Targeted Augmented Data for Audio Deepfake Detectioncited by 2
  6. 2022ConvNeXt Based Neural Network for Audio Anti-Spoofingcited by 1
  7. 2023Betray Oneself: A Novel Audio DeepFake Detection Model via Mono-to-Stereo Conversioncited by 1
  8. 2024Toward Improving Synthetic Audio Spoofing Detection Robustness via Meta-Learning and Disentangled Training With Adversarial Examplescited by 1
  9. 2025AUDETER: A Large-scale Dataset for Deepfake Audio Detection in Open Worldscited by 0