# Defense Against Adversarial Attacks on Audio DeepFake Detection

arXiv: https://arxiv.org/abs/2212.14597 · HTML: https://mlsanity.com/paper/2212.14597/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 34 references that are in the map.
- First posted on arXiv 30 Dec 2022.

## Builds on (most cited first, 25 of 25)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization](https://mlsanity.com/paper/2006.16241/) (2020)
- [Ensemble Adversarial Training: Attacks and Defenses](https://mlsanity.com/paper/1705.07204/) (2017)
- [Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack](https://mlsanity.com/paper/1907.02044/) (2019)
- [Recent Advances in Adversarial Training for Adversarial Robustness](https://mlsanity.com/paper/2102.01356/) (2021)
- [Are Transformers More Robust Than CNNs?](https://mlsanity.com/paper/2111.05464/) (2021)
- [FakeAVCeleb: A Novel Audio-Video Multimodal Deepfake Dataset](https://mlsanity.com/paper/2108.05080/) (2021)
- [End-to-end anti-spoofing with RawNet2](https://mlsanity.com/paper/2011.01108/) (2020)
- [ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection](https://mlsanity.com/paper/2109.00537/) (2021)
- [Does Audio Deepfake Detection Generalize?](https://mlsanity.com/paper/2203.16263/) (2022)
- [A Light CNN for Deep Face Representation with Noisy Labels](https://mlsanity.com/paper/1511.02683/) (2015)
- [Adversarial Attacks Against Automatic Speech Recognition Systems via Psychoacoustic Hiding](https://mlsanity.com/paper/1808.05665/) (2018)
- [Deepfakes Generation and Detection: State-of-the-art, open challenges, countermeasures, and way forward](https://mlsanity.com/paper/2103.00484/) (2021)
- [WaveFake: A Data Set to Facilitate Audio Deepfake Detection](https://mlsanity.com/paper/2111.02813/) (2021)
- [Pushing the limits of raw waveform speaker recognition](https://mlsanity.com/paper/2203.08488/) (2022)
- [Investigating self-supervised front ends for speech spoofing countermeasures](https://mlsanity.com/paper/2111.07725/) (2021)
- [Practical Evaluation of Adversarial Robustness via Adaptive Auto Attack](https://mlsanity.com/paper/2203.05154/) (2022)
- [Adversarial Attacks on Spoofing Countermeasures of automatic speaker verification](https://mlsanity.com/paper/1910.08716/) (2019)
- [Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification: Fundamentals](https://mlsanity.com/paper/2007.05979/) (2020)
- [Defense against adversarial attacks on spoofing countermeasures of ASV](https://mlsanity.com/paper/2003.03065/) (2020)
- [Attack Agnostic Dataset: Towards Generalization and Stabilization of Audio DeepFake Detection](https://mlsanity.com/paper/2206.13979/) (2022)
- [SpecRNet: Towards Faster and More Accessible Audio DeepFake Detection](https://mlsanity.com/paper/2210.06105/) (2022)

## Built on by (most cited first, 12 of 12)

- [Detect All-Type Deepfake Audio: Wavelet Prompt Tuning for Enhanced Auditory Perception](https://mlsanity.com/paper/2504.06753/) (2025)
- [Adversarial Attacks on Audio Deepfake Detection: A Benchmark and Comparative Study](https://mlsanity.com/paper/2509.07132/) (2025)
- [Interpretable All-Type Audio Deepfake Detection with Audio LLMs via Frequency-Time Reinforcement Learning](https://mlsanity.com/paper/2601.02983/) (2026)
- [Passive Deepfake Detection Across Multi-modalities: A Comprehensive Survey](https://mlsanity.com/paper/2411.17911/) (2024)
- [SHIELD: A Secure and Highly Enhanced Integrated Learning for Robust Deepfake Detection against Adversarial Attacks](https://mlsanity.com/paper/2507.13170/) (2025)
- [Reliable Audio Deepfake Detection in Variable Conditions via Quantum-Kernel SVMs](https://mlsanity.com/paper/2512.18797/) (2025)
- [AT-ADD: All-Type Audio Deepfake Detection Challenge Evaluation Plan](https://mlsanity.com/paper/2604.08184/) (2026)
- [Adversarially Robust Deepfake Detection via Adversarial Feature Similarity Learning](https://mlsanity.com/paper/2403.08806/) (2024)
- [D-CAPTCHA++: A Study of Resilience of Deepfake CAPTCHA under Transferable Imperceptible Adversarial Attack](https://mlsanity.com/paper/2409.07390/) (2024)
- [Unmasking Synthetic Realities in Generative AI: A Comprehensive Review of Adversarially Robust Deepfake Detection Systems](https://mlsanity.com/paper/2507.21157/) (2025)
- [Zero-Shot to Zero-Lies: Detecting Bengali Deepfake Audio through Transfer Learning](https://mlsanity.com/paper/2512.21702/) (2025)
- [Half-Truth Audio Detection and Localisation: A Lightweight Cross-Attentive Architecture and a Cross-Corpus Diagnostic Study](https://mlsanity.com/paper/2605.29531/) (2026)

## Abstract (arXiv)

Audio DeepFakes (DF) are artificially generated utterances created using deep learning, with the primary aim of fooling the listeners in a highly convincing manner. Their quality is sufficient to pose a severe threat in terms of security and privacy, including the reliability of news or defamation. Multiple neural network-based methods to detect generated speech have been proposed to prevent the threats. In this work, we cover the topic of adversarial attacks, which decrease the performance of detectors by adding superficial (difficult to spot by a human) changes to input data. Our contribution contains evaluating the robustness of 3 detection architectures against adversarial attacks in two scenarios (white-box and using transferability) and enhancing it later by using adversarial training performed by our novel adaptive training. Moreover, one of the investigated architectures is RawNet3, which, to the best of our knowledge, we adapted for the first time to DeepFake detection.