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Researchers explore quantum and deep learning for audio deepfake detection

By PulseAugur Editorial · [3 sources] ·

Two research papers submitted to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) in 2026 propose novel deep-learning frameworks for detecting manipulated audio. The first paper introduces a dual-branch system using pretrained models XLS-R and BEATs to separately analyze speech and environmental sounds, achieving a 70.20% F1-score. The second paper explores various deep-learning architectures and pretrained models, finding that fine-tuning WavLM with a three-stage strategy yields superior results, with an F1 score of 0.95 on one benchmark dataset. AI

IMPACT Advances in deepfake audio detection could lead to more robust content moderation and security systems.

RANK_REASON Two arXiv papers present new methods for deepfake audio detection, including specific model architectures and performance metrics.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 3 sources. How we write summaries →

Researchers explore quantum and deep learning for audio deepfake detection

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Two arXiv papers present new methods for deepfake audio detection, including specific model architectures and performance metrics.
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COVERAGE [3]

  1. arXiv cs.AI TIER_1 English(EN) · Lisan Al Amin, Rakib Hossain, Mahbubul Islam, Faisal Quader, Thanh Thi Nguyen ·

    Quantum Kernels for Audio Deepfake Detection Using Spectrogram Patch Features

    arXiv:2605.06035v1 Announce Type: cross Abstract: Quantum machine learning has emerged as a promising tool for pattern recognition, yet many audio-focused approaches still treat spectrograms as generic images and do not explicitly exploit their time-frequency structure. We propos…

  2. arXiv cs.AI TIER_1 English(EN) · Khalid Zaman, Qixuan Huang, Muhammad Uzair, Masashi Unoki ·

    Deepfake Audio Detection Using Self-supervised Fusion Representations

    arXiv:2605.03420v1 Announce Type: cross Abstract: This paper describes a submission to the Environment-Aware Speech and Sound Deepfake Detection Challenge (ESDD2) 2026, which addresses component-level deepfake detection using the CompSpoofV2 dataset, where speech and environmenta…

  3. arXiv cs.AI TIER_1 English(EN) · Lam Pham, Khoi Vu, Dat Tran, Phat Lam, Vu Nguyen, David Fischinger, Son Le ·

    Environmental Sound Deepfake Detection Using Deep-Learning Framework

    arXiv:2604.19652v2 Announce Type: replace-cross Abstract: In this paper, we propose a deep-learning framework for environmental sound deepfake detection (ESDD) -- the task of identifying whether the sound scene and sound event in an input audio recording is fake or not. To this e…

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