# Pyramid Feature Attention Network for Speech Resampling Detection
## 1 Introduction
[18] calculated the logarithm ratio of band energy between original and resampled speech according to the characteristics that the bandwidth of the resampled speech is inconsistent with its sampling rate, so as to distinguish true and false speech. The algorithm can detect resampled speech in a variety of situations and has a certain degree of anti-compressibility, but it fails to show its effectiveness for resampled speech with a small tampering amplitude. [PERSON] et al. [19] proposed an algorithm that uses the local binary pattern (LBP) operator to model the statistical change characteristics of spectrograms and uses support vector machines (SVMs) for resampling detection, which has better detection performance and better robustness against MP3 compression, but the accuracy of detection for resampling factors, ranging from 0.8 to 1.2, still needs to be improved. Although the methods discussed above can achieve good accuracy, most require artificial feature engineering, which involves massive human professional knowledge and is usually a very time-consuming process. In recent years, deep learning has made significant progress in machine learning and has been widely applied in various fields such as digital image recognition, speech recognition, and steganography analysis [20; 21; 22; 23; 24]. Among them, [PERSON] et al. [20] proposed a multiscale attention network for splicing tampering forensics in image recognition, which utilizes the integration of residual attention and multiscale information in order to improve the detection accuracy. [PERSON] et al. [21] applied deep learning technology to the field of industrial defect detection, aiming to improve the accuracy of magnetic flux leakage (MFL) image recognition of pipeline corrosion defects, and achieved remarkable results. [PERSON] et al. [22] proposed a stride-based convolutional neural network (SCNN) model for speech emotion recognition. [PERSON] et al. [23] applied deep learning in bio-signal steganography to provide robust, undetectable and trustworthy information security techniques. [PERSON] et al. [24] built an attention-guided robust image watermarking model based on a generative adversarial network (GAN) to solve the problem of insufficient features and failure to highlight essential features in the existing deep learning watermarking models. Traditional features have certain advantages in some aspects. But if the scene used is changed, the detection effect of manual features may be reduced [25; 26; 27]. In contrast, deep learning has great advantages at the level of extracting features, and it also has impressive classification capabilities. However, less work has been performed on applying deep learning to speech-tampering forensics. As can be seen in Refs. [28; 29; 30], some studies exist on speech copy-move detection, speech splicing detection, and speech deletion detection using deep learning. In these studies, the two-dimensional graph generated by speech signals is used as the input of a neural network and its graphical characteristics are utilized to achieve different speech tasks. The two-dimensional graph in Figure 1 is also known as the spectrogram, which is a simple but informative speech visualization map. In addition to being easier to interpret than the speech signal itself, it preserves time, frequency and amplitude information in a single plot. Therefore, using deep learning methods to extract time-frequency features of spectrograms for speech resampling detection will to some extent help us improve the detection accuracy of forged speech. Based on the existing research, this paper conducts experiments using a resampling-tampered corpus with multiple factors produced by [PERSON] et al. [19] utilizing the TIMIT dataset.