Heterogeneous audio splicing tampering, which combines audio recordings from different scenarios or devices, has posed a significant challenge to audio authenticity. Most of the existing work is not good at the detection and localization for multiple splicing points, especially when the signal-to-noise ratios (SNRs) of recordings involved in splicing are close. In this work, we propose an audio splicing detection and localization method on the basis of multistage filterbank spectral sketches (MFBSS) and decision fusion. More specifically, we first remove the silent segments to reduce the redundant information and estimate the background noise of the combined voice-only segments. Next, to obtain more audio details, we propose a feature fusion strategy to extract the MFBSS feature from the background noise. Then, we develop a decision fusion strategy to detect and localize all the possible splicing points. Finally, we evaluate our method against the state-of-the-art splicing detection approaches on public datasets with various noises and SNR differences. Experimental results demonstrate that the proposed approach is effective for various noise scenarios with small SNR differences and is also robust against anti-forensics attacks.

异构音频拼接篡改将不同场景或设备的音频录音组合在一起，对音频真实性提出了重大挑战。大多数现有工作并不擅长对多个拼接点进行检测和定位，特别是当拼接所涉及的录音的信噪比（SNR）接近时。在这项工作中，我们提出了一种基于多级滤波器组频谱草图（MFBSS）和决策融合的音频拼接检测和定位方法。更具体地说，我们首先删除无声片段以减少冗余信息并估计组合的纯语音片段的背景噪声。接下来，为了获得更多的音频细节，我们提出了一种特征融合策略，从背景噪声中提取 MFBSS 特征。然后，我们开发决策融合策略来检测和定位所有可能的拼接点。最后，我们根据具有各种噪声和信噪比差异的公共数据集上最先进的拼接检测方法来评估我们的方法。实验结果表明，所提出的方法对于信噪比差异较小的各种噪声场景都是有效的，并且对于反取证攻击也具有鲁棒性。