# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 1 Introduction
When the similarity of corresponding features between segments exceeds these thresholds, the segments are identified as copy-move segments. Furthermore, to better determine thresholds, [PERSON] et al. [14] proposed a method based on Constant Q Spectrogram Sketch (CQSS) combined with a customized Genetic Algorithm (GA) and Support Vector Machine (SVM). For short audio segments, [PERSON] et al. [15] combined CQCC with PCC and designed two sliding window (SW) strategies. However, this method is influenced by the window length and cannot adapt to changes in the duration of forgery. With the rise in deep learning, there has been significant progress in image and video forensics based on deep learning techniques. However, the development of audio forensics based on deep learning has been relatively slow, and there are relatively few methods for detecting audio copy-move forgery. [PERSON] et al. [16] used an artificial neural network (ANN) to achieve binary classification by learning Mel frequency cepstral coefficients and linear prediction coefficients. [PERSON] et al. [17] used a classical convolutional neural  
Figure 1: Waveform of copy-move forged audio. The waveform of copy-move segments is highlighted in red. network (CNN) to extract audio spectrum features and then performed binary classification through fully connected layers. Both methods can only determine the presence of copy-move forgery in the audio without pinpointing the specific forged locations. In summary, although existing methods can detect audio copy-move forgery, there are still some issues or limitations that deserve attention and improvement. Existing passive forensics methods overly rely on segmentation techniques to segment audio into silent and voiced segments, but audio collected in noisy environments is often difficult to segment [18; 19]. These methods require repeatedly adjusting the similarity threshold to make feature similarity decisions in order to determine whether there are copy-move forged segments in the audio. Existing methods based on deep learning can only determine whether the audio has undergone copy-paste forgery without the ability to locate the forged segments. Building upon the principles of previous passive evidence-gathering methods, we have improved the deep learning network to propose a localization model for identifying audio copy-move forgery. Our main contributions are as follows:  
1. By leveraging insights from traditional audio copy-move forgery forensic methods to refine deep learning networks, a localization model is proposed to locate audio copy-move forged segments. The localization model consists of three modules: Feature Extraction Module, Correlation Detection Module, and Mask Decoding Module, corresponding to the three steps of passive forensics;
2. The localization model detects and locates forged segments by analyzing the Mel-spectrograms generated from the entire audio without relying on segmentation algorithms. Due to the incorporation of the designed Correlation Detection Module and Mask Decoding Module, the localization model attains the capability for feature-correlation-based autonomous decision making and precise localization of forged segments;
3. The forgery detection results of our model can be visualized. The localization model precisely and visually locates audio copy-move forged segments by generating a similarity region mask.