# An Audio Copy-Move Forgery Localization Model by CNN-Based Spectral Analysis
## 3 Experiments
### Binary Classification of Audio Copy-Move Forgery  
The primary objective of the binary classification task is to determine whether an audio has undergone copy-move forgery. Additionally, another objective was to better extract forged features by determining the ability of different backbone networks to extract features from Mel-spectrograms through binary classification experiments. In theory, existing high-performance deep learning backbone networks could be directly used for the Feature Extraction Module. However, Mel-spectrograms differ from traditional images as they are colorized representations of two-dimensional matrices based on amplitude mapping, where the colors are derived from amplitude values. Therefore, the three channels of Mel-spectrograms do not have physical significance. Currently, various backbone networks used for deep learning tasks are applied to traditional images, so binary classification experiments are designed to evaluate the performance of each backbone network in extracting features from Mel-spectrograms. Finally, the experimental results are compared with other deep learning methods that also perform binary classification tasks. This experiment utilized different backbone networks with added fully connected layers for binary classification. The selected backbone networks include VGG, ResNet, Res2 Net [22], MPIF-Res2 Net [23] based on CNN, and Vision Transformer [24] based on the attention mechanism. In these CNN backbone networks, the Mel-spectrograms were processed into deep feature maps of size 1024 \(\times\) 8 \(\times\) 16 (while the Vision Transformer remained at 3 \(\times\) 128 \(\times\) 256). Subsequently, fully connected layers of sizes 1024, 128, and 1 were connected, with a classification threshold set at 0.5. The experimental results are presented in Table 2. Existing deep learning-based methods for audio copy-move forgery detection are all based on binary classification of audio, including the ANN method proposed by [PERSON] et al. [16] and the CNN method proposed by [PERSON] et al. [17]. The results of these two methods are also listed in Table 2.  
Based on the experimental results in Table 2, it is evident that, in the binary classification task, compared to the other backbone networks, MPIF-Res2 Net achieves higher \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **VGG** & **ResNet** & **Res2 Net** & **Vision** & **MPIF-Res2 Net** & **[PERSON] et al.** & **[PERSON] et al.** \\  & & & & **Transfomer** & **ANN** [16] & **CNN** [17] \\ \hline Accuracy & 89.7 & 92.8 & 93.3 & 67.6 & 95.4 & 76.5 & 94.9 \\ Precision & 92.2 & 91.6 & 94.1 & 65.1 & 94.6 & — & 91.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The binary classification results of different backbone networks or methods (%). accuracy and precision, enabling better extraction of features reflecting tampering in the Mel-spectrogram. It is noteworthy that the performance of VGG, ResNet, and Res2 Net is as expected. However, when using the Vision Transformer as the backbone network, various image-slicing methods were attempted (ranging from 16 \(\times\) 16 to slicing along the time axis \(x\)), along with changes in the number of stacked self-attention layers. Nonetheless, the accuracy and precision could not be improved. Upon analysis, this may be partly due to the dataset still being insufficiently large and partly because the model structure of convolutional neural networks assumes spatial invariance. Thus, convolutional kernels can be used to process the entire feature map through window sliding. However, the self-attention mechanism in the Vision Transformer does not possess this characteristic. The ANN method [15] exhibits lower accuracy, as it relies solely on neuron-connected ANNs, resulting in poor fitting of various features such as MFCC and LPC. This limitation makes the model insufficient for determining whether an audio has undergone copy-move forgery.