# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 1 Introduction  
Nowadays, people are accustomed to using various communication tools for voice communication, including important scenarios such as transactions and negotiations. The digital audio records generated during communication often become an important basis for resolving disputes. However, with the popularity of various audio editing software, ordinary users can easily edit or even maliciously tamper with digital audio files, which poses great challenges to digital audio forensics. The common types of audio forgery [1] currently include resampling, splicing, copy-move, deep forgery, etc. Among them, copy-move forgery, as shown in Figure 1, refers to copying a segment of an audio and moving it onto another location of the same audio in order to achieve semantic tampering of the audio. Due to the fact that the tampered source and target segments come from the same audio file, many basic characteristics of the tampered segments, such as frequency, amplitude, noise, tone, etc., can be well matched with the original audio, making detection extremely difficult. The active forensics methods of audio copy-move forgery involve embedding watermarks, fingerprints, hash bits, etc., into the audio through steganography and relying on this hidden information to detect various types of forgery. Although these active forensics methods are effective, they are difficult to use in real-world forensics as it is impossible to embed all files with hidden information beforehand. The passive forensic methods of audio copy-move forgery rely on assessing the similarity between audio segments to identify authenticity, making them more practical. The existing passive evidence-gathering methods typically involve three steps:The first step is to segment the audio into word or phrase segments, remove silent segments, and then extract various features from each segment. For audio segmentation, more researchers [2; 3; 4; 5; 6; 7; 8; 9; 10] use Voice Activity Detection (VAD) to divide the audio into silent and audio segments and [11; 12] use Normalized Low-Frequency Energy Ratio (NLFER) to divide the audio into silent and audio segments and [13] to divide the audio into equal time periods. Then, various features are extracted from the segmented audio segments, such as singular feature vector [2], Mel scale frequency cepstral coefficient (MFCC) [3; 7], fundamental sequence [4], discrete Fourier transform (DFT) coefficient [5; 6; 7], gamma filter [7], histogram [8; 9], color autocorrelation map [10], tone similarity [11], tone sequence [12], format sequence [12], etc. The second step is to use the similarity calculation method to calculate the similarity between any two segments. For the different features extracted from speech segments, researchers have adopted different similarity measurement methods, such as Euclidean distance [2], Mean Square Error (MSE) [9], Energy Ratio (ENR) [9], cosine distance [10], Dynamic Time Warping (DTW) distance [10; 12], Average Difference (AD) [11], Pearson correlation coefficient [11], fast convolution algorithm [13], etc. Most of these methods compute the feature similarity between all audio segments. However, [PERSON] et al. [5; 6] adopt a different approach by rapidly sorting the features of all segments. They then only calculate the similarity between one voiced segment and its adjacent voiced segments, reducing the time complexity of computing similarity between audio segments from O(n2) to O(nlog2n), with no significant degradation in detection performance. The third step is to adjust the similarity threshold of individual features or make multi-feature decisions to obtain the detection results. The earlier method only judged the similarity of a single feature by debugging the threshold, resulting in lower accuracy. [PERSON] et al. [7] used C4.5 Tree to make decisions on four features: gamma filter, Mel cepstral coefficient, pitch frequency, and DFT coefficient. [PERSON] et al. [10] set similarity thresholds for pitch frequency, color autocorrelation map, and short-term energy.