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 fo... More >