# Partially Fake Audio Detection by Self-attention-based Fake Span Discovery

arXiv: https://arxiv.org/abs/2202.06684 · HTML: https://mlsanity.com/paper/2202.06684/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 38 references that are in the map.
- First posted on arXiv 14 Feb 2022.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection](https://mlsanity.com/paper/1904.05441/) (2019)
- [Speaker Recognition from Raw Waveform with SincNet](https://mlsanity.com/paper/1808.00158/) (2018)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Attentive Statistics Pooling for Deep Speaker Embedding](https://mlsanity.com/paper/1803.10963/) (2018)
- [AISHELL-3: A Multi-speaker Mandarin TTS Corpus and the Baselines](https://mlsanity.com/paper/2010.11567/) (2020)
- [A Comparative Study on Recent Neural Spoofing Countermeasures for Synthetic Speech Detection](https://mlsanity.com/paper/2103.11326/) (2021)
- [Half-Truth: A Partially Fake Audio Detection Dataset](https://mlsanity.com/paper/2104.03617/) (2021)
- [Replay and Synthetic Speech Detection with Res2net Architecture](https://mlsanity.com/paper/2010.15006/) (2020)
- [Continual Learning for Fake Audio Detection](https://mlsanity.com/paper/2104.07286/) (2021)
- [ASSERT: Anti-Spoofing with Squeeze-Excitation and Residual neTworks](https://mlsanity.com/paper/1904.01120/) (2019)
- [Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification](https://mlsanity.com/paper/1910.08716/) (2019)
- [Defense against adversarial attacks on spoofing countermeasures of ASV](https://mlsanity.com/paper/2003.03065/) (2020)
- [Channel-wise Gated Res2Net: Towards Robust Detection of Synthetic Speech Attacks](https://mlsanity.com/paper/2107.08803/) (2021)
- [Adversarial defense for automatic speaker verification by cascaded self-supervised learning models](https://mlsanity.com/paper/2102.07047/) (2021)
- [Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning](https://mlsanity.com/paper/2106.00273/) (2021)
- [Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning](https://mlsanity.com/paper/2006.03214/) (2020)
- [Voting for the right answer: Adversarial defense for speaker verification](https://mlsanity.com/paper/2106.07868/) (2021)

## Built on by (most cited first, 14 of 14)

- [How Do Neural Spoofing Countermeasures Detect Partially Spoofed Audio?](https://mlsanity.com/paper/2406.02483/) (2024)
- [The defender's perspective on automatic speaker verification: An overview](https://mlsanity.com/paper/2305.12804/) (2023)
- [Enhancing Partially Spoofed Audio Localization with Boundary-aware Attention Mechanism](https://mlsanity.com/paper/2407.21611/) (2024)
- [How Good is ChatGPT at Audiovisual Deepfake Detection: A Comparative Study of ChatGPT, AI Models and Human Perception](https://mlsanity.com/paper/2411.09266/) (2024)
- [Spoof Diarization: "What Spoofed When" in Partially Spoofed Audio](https://mlsanity.com/paper/2406.07816/) (2024)
- [Pindrop it! Audio and Visual Deepfake Countermeasures for Robust Detection and Fine Grained-Localization](https://mlsanity.com/paper/2508.08141/) (2025)
- [Spoofing-Aware Speaker Verification by Multi-Level Fusion](https://mlsanity.com/paper/2203.15377/) (2022)
- [Adversarial Speaker Distillation for Countermeasure Model on Automatic Speaker Verification](https://mlsanity.com/paper/2203.17031/) (2022)
- [Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion](https://mlsanity.com/paper/2206.09131/) (2022)
- [The Tug-of-War Between Deepfake Generation and Detection](https://mlsanity.com/paper/2407.06174/) (2024)
- [TranssionADD: A multi-frame reinforcement based sequence tagging model for audio deepfake detection](https://mlsanity.com/paper/2306.15212/) (2023)
- [An Unsupervised Domain Adaptation Method for Locating Manipulated Region in partially fake Audio](https://mlsanity.com/paper/2407.08239/) (2024)
- [3-Tracer: A Tri-level Temporal-Aware Framework for Audio Forgery Detection and Localization](https://mlsanity.com/paper/2511.21237/) (2025)
- [Robust Spoofed Speech Detection via Temporal Pyramid Modeling](https://mlsanity.com/paper/2606.16837/) (2026)

## Abstract (arXiv)

The past few years have witnessed the significant advances of speech synthesis and voice conversion technologies. However, such technologies can undermine the robustness of broadly implemented biometric identification models and can be harnessed by in-the-wild attackers for illegal uses. The ASVspoof challenge mainly focuses on synthesized audios by advanced speech synthesis and voice conversion models, and replay attacks. Recently, the first Audio Deep Synthesis Detection challenge (ADD 2022) extends the attack scenarios into more aspects. Also ADD 2022 is the first challenge to propose the partially fake audio detection task. Such brand new attacks are dangerous and how to tackle such attacks remains an open question. Thus, we propose a novel framework by introducing the question-answering (fake span discovery) strategy with the self-attention mechanism to detect partially fake audios. The proposed fake span detection module tasks the anti-spoofing model to predict the start and end positions of the fake clip within the partially fake audio, address the model's attention into discovering the fake spans rather than other shortcuts with less generalization, and finally equips the model with the discrimination capacity between real and partially fake audios. Our submission ranked second in the partially fake audio detection track of ADD 2022.