SafeEar: Content Privacy-Preserving Audio Deepfake Detection

• URL: http://arxiv.org/abs/2409.09272v1
• Date: Sat, 14 Sep 2024 02:45:09 GMT
• Title: SafeEar: Content Privacy-Preserving Audio Deepfake Detection
• Authors: Xinfeng Li, Kai Li, Yifan Zheng, Chen Yan, Xiaoyu Ji, Wenyuan Xu,
• Abstract summary: We propose SafeEar, a novel framework that aims to detect deepfake audios without relying on accessing the speech content within. Our key idea is to devise a neural audio into a novel decoupling model that well separates the semantic and acoustic information from audio samples. In this way, no semantic content will be exposed to the detector.
• Score: 17.859275594843965
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Text-to-Speech (TTS) and Voice Conversion (VC) models have exhibited remarkable performance in generating realistic and natural audio. However, their dark side, audio deepfake poses a significant threat to both society and individuals. Existing countermeasures largely focus on determining the genuineness of speech based on complete original audio recordings, which however often contain private content. This oversight may refrain deepfake detection from many applications, particularly in scenarios involving sensitive information like business secrets. In this paper, we propose SafeEar, a novel framework that aims to detect deepfake audios without relying on accessing the speech content within. Our key idea is to devise a neural audio codec into a novel decoupling model that well separates the semantic and acoustic information from audio samples, and only use the acoustic information (e.g., prosody and timbre) for deepfake detection. In this way, no semantic content will be exposed to the detector. To overcome the challenge of identifying diverse deepfake audio without semantic clues, we enhance our deepfake detector with real-world codec augmentation. Extensive experiments conducted on four benchmark datasets demonstrate SafeEar's effectiveness in detecting various deepfake techniques with an equal error rate (EER) down to 2.02%. Simultaneously, it shields five-language speech content from being deciphered by both machine and human auditory analysis, demonstrated by word error rates (WERs) all above 93.93% and our user study. Furthermore, our benchmark constructed for anti-deepfake and anti-content recovery evaluation helps provide a basis for future research in the realms of audio privacy preservation and deepfake detection.

Related papers

• Vulnerability of Automatic Identity Recognition to Audio-Visual Deepfakes [13.042731289687918]
  We present the first realistic audio-visual database of deepfakes SWAN-DF, where lips and speech are well synchronized. We demonstrate the vulnerability of a state of the art speaker recognition system, such as ECAPA-TDNN-based model from SpeechBrain.
  arXiv  Detail & Related papers  (2023-11-29T14:18:04Z)
• AVTENet: Audio-Visual Transformer-based Ensemble Network Exploiting Multiple Experts for Video Deepfake Detection [53.448283629898214]
  The recent proliferation of hyper-realistic deepfake videos has drawn attention to the threat of audio and visual forgeries. Most previous work on detecting AI-generated fake videos only utilize visual modality or audio modality. We propose an Audio-Visual Transformer-based Ensemble Network (AVTENet) framework that considers both acoustic manipulation and visual manipulation.
  arXiv  Detail & Related papers  (2023-10-19T19:01:26Z)
• SceneFake: An Initial Dataset and Benchmarks for Scene Fake Audio Detection [54.74467470358476]
  This paper proposes a dataset for scene fake audio detection named SceneFake. A manipulated audio is generated by only tampering with the acoustic scene of an original audio. Some scene fake audio detection benchmark results on the SceneFake dataset are reported in this paper.
  arXiv  Detail & Related papers  (2022-11-11T09:05:50Z)
• System Fingerprint Recognition for Deepfake Audio: An Initial Dataset and Investigation [51.06875680387692]
  We present the first deepfake audio dataset for system fingerprint recognition (SFR) We collected the dataset from the speech synthesis systems of seven Chinese vendors that use the latest state-of-the-art deep learning technologies.
  arXiv  Detail & Related papers  (2022-08-21T05:15:40Z)
• Do You Really Mean That? Content Driven Audio-Visual Deepfake Dataset and Multimodal Method for Temporal Forgery Localization [19.490174583625862]
  We introduce a content-driven audio-visual deepfake dataset, termed Localized Audio Visual DeepFake (LAV-DF) Specifically, the content-driven audio-visual manipulations are performed strategically to change the sentiment polarity of the whole video. Our extensive quantitative and qualitative analysis demonstrates the proposed method's strong performance for temporal forgery localization and deepfake detection tasks.
  arXiv  Detail & Related papers  (2022-04-13T08:02:11Z)
• Audio-Visual Person-of-Interest DeepFake Detection [77.04789677645682]
  The aim of this work is to propose a deepfake detector that can cope with the wide variety of manipulation methods and scenarios encountered in the real world. We leverage a contrastive learning paradigm to learn the moving-face and audio segment embeddings that are most discriminative for each identity. Our method can detect both single-modality (audio-only, video-only) and multi-modality (audio-video) attacks, and is robust to low-quality or corrupted videos.
  arXiv  Detail & Related papers  (2022-04-06T20:51:40Z)
• Audio-Visual Speech Codecs: Rethinking Audio-Visual Speech Enhancement by Re-Synthesis [67.73554826428762]
  We propose a novel audio-visual speech enhancement framework for high-fidelity telecommunications in AR/VR. Our approach leverages audio-visual speech cues to generate the codes of a neural speech, enabling efficient synthesis of clean, realistic speech from noisy signals.
  arXiv  Detail & Related papers  (2022-03-31T17:57:10Z)
• Evaluation of an Audio-Video Multimodal Deepfake Dataset using Unimodal and Multimodal Detectors [18.862258543488355]
  Deepfakes can cause security and privacy issues. New domain of cloning human voices using deep-learning technologies is also emerging. To develop a good deepfake detector, we need a detector that can detect deepfakes of multiple modalities.
  arXiv  Detail & Related papers  (2021-09-07T11:00:20Z)
• Emotions Don't Lie: An Audio-Visual Deepfake Detection Method Using Affective Cues [75.1731999380562]
  We present a learning-based method for detecting real and fake deepfake multimedia content. We extract and analyze the similarity between the two audio and visual modalities from within the same video. We compare our approach with several SOTA deepfake detection methods and report per-video AUC of 84.4% on the DFDC and 96.6% on the DF-TIMIT datasets.
  arXiv  Detail & Related papers  (2020-03-14T22:07:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.