TY  - JOUR
AU  - Munawwara, Aysha
AU  - Dashtipour, Kia
AU  - Saleem, Nasir
AU  - Gogate, Mandar
AU  - Hussain, Adeel
AU  - Hussain, Amir
PY  - 2026
DA  - 2026/04/28
TI  - Multi-Attack Audio-Visual Spoof Detection for Secure Hearing-Assistive Systems Using Transformer Fusion
JO  - ICCK Transactions on Information Security and Cryptography
T2  - ICCK Transactions on Information Security and Cryptography
JF  - ICCK Transactions on Information Security and Cryptography
VL  - 2
IS  - 2
SP  - 101
EP  - 108
DO  - 10.62762/TISC.2026.221187
UR  - https://www.icck.org/article/abs/TISC.2026.221187
KW  - hearing systems
KW  - audio-visual spoofing
KW  - deepfake detection
KW  - transformer fusion
KW  - multi-attack protocol
AB  - Audio-visual spoofing attacks have emerged as a serious threat to modern hearing-assistive systems due to rapid advances in text-to-speech synthesis, neural vocoders, and lip-sync deepfake generation. Advanced hearing aids and cochlear implants increasingly incorporate AI-based speech enhancement and multimodal perception modules, which makes them vulnerable to manipulated or synthetic inputs. Traditional spoof detection approaches are often limited to binary classification between bonafide and spoofed speech, failing to capture the diversity of emerging multi-modal attack types.In this paper, we propose a multi-attack audio-visual spoof detection framework designed that explicitly models four spoof categories: real speech, text-to-speech (TTS) spoofing, vocoder-based spoofing, and lip-sync manipulation attacks. A multi-attack protocol is introduced to enable fine-grained supervision across both audio and video modalities. The proposed system employs convolutional feature extractors for each stream, followed by multimodal fusion for robust classification. Experimental results demonstrate reliable performance under in-dataset evaluation settings. Confusion matrix analysis further highlights the effectiveness of audio-visual fusion, particularly in detecting visually driven spoofing attacks. Overall, this work provides a strong foundation for next-generation secure hearing-assistive systems operating in real-world acoustic environments.
SN  - 3070-2429
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Munawwara2026MultiAttac,
  author = {Aysha Munawwara and Kia Dashtipour and Nasir Saleem and Mandar Gogate and Adeel Hussain and Amir Hussain},
  title = {Multi-Attack Audio-Visual Spoof Detection for Secure Hearing-Assistive Systems Using Transformer Fusion},
  journal = {ICCK Transactions on Information Security and Cryptography},
  year = {2026},
  volume = {2},
  number = {2},
  pages = {101-108},
  doi = {10.62762/TISC.2026.221187},
  url = {https://www.icck.org/article/abs/TISC.2026.221187},
  abstract = {Audio-visual spoofing attacks have emerged as a serious threat to modern hearing-assistive systems due to rapid advances in text-to-speech synthesis, neural vocoders, and lip-sync deepfake generation. Advanced hearing aids and cochlear implants increasingly incorporate AI-based speech enhancement and multimodal perception modules, which makes them vulnerable to manipulated or synthetic inputs. Traditional spoof detection approaches are often limited to binary classification between bonafide and spoofed speech, failing to capture the diversity of emerging multi-modal attack types.In this paper, we propose a multi-attack audio-visual spoof detection framework designed that explicitly models four spoof categories: real speech, text-to-speech (TTS) spoofing, vocoder-based spoofing, and lip-sync manipulation attacks. A multi-attack protocol is introduced to enable fine-grained supervision across both audio and video modalities. The proposed system employs convolutional feature extractors for each stream, followed by multimodal fusion for robust classification. Experimental results demonstrate reliable performance under in-dataset evaluation settings. Confusion matrix analysis further highlights the effectiveness of audio-visual fusion, particularly in detecting visually driven spoofing attacks. Overall, this work provides a strong foundation for next-generation secure hearing-assistive systems operating in real-world acoustic environments.},
  keywords = {hearing systems, audio-visual spoofing, deepfake detection, transformer fusion, multi-attack protocol},
  issn = {3070-2429},
  publisher = {Institute of Central Computation and Knowledge}
}