## 5 Conclusions  
A system for audio integrity based on mobile applications and cloud storage is proposed. In the mobile device, a hash function specifically designed for audio recordings is executed. Once the hash code is obtained, it is transmitted to a database in the cloud.  
The performance evaluation of the proposed hash function was focused on collision resistance and sensitivity. After 9730 tests, no collisions were found, not even between two pairs of very similar recordings (for example, an audio recording and its altered version with only one modified sample). Among all the recordings, the lowest \(HD\) (Hamming Distance) was 0.4; in the case of a very similar  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Recording/ Modification** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Modification 1 & \(-0.05\) & \(-0.02\) & \(0.05\) & \(0.09\) & \(0.06\) & \(0.80\) & \(0.03\) & \(0.14\) & \(0.29\) \\ Modification 2 & \(0.03\) & \(0.06\) & \(-0.07\) & \(0.07\) & \(0.13\) & \(-0.07\) & \(0.11\) & \(0.04\) & \(0.08\) \\ Modification 3 & \(-0.05\) & \(-0.05\) & \(-0.01\) & \(0.06\) & \(-0.03\) & \(0.02\) & \(-0.02\) & \(-0.01\) & \(0.04\) \\ Modification 4 & \(-0.02\) & \(0.02\) & \(0.04\) & \(-0.08\) & \(-0.05\) & \(0.06\) & \(0.14\) & \(0.02\) & \(0.07\) \\ Modification 5 & \(0.09\) & \(0.03\) & \(-0.01\) & \(0.03\) & \(0.00\) & \(-0.05\) & \(0.07\) & \(0.02\) & \(0.05\) \\ Modification 6 & \(0.02\) & \(0.03\) & \(0.04\) & \(-0.05\) & \(0.02\) & \(0.02\) & \(0.00\) & \(0.01\) & \(0.03\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results obtained in the \(PCC\) comparison between the hash codes of the original recording and the modified recording.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Recording/ Modification** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **Mean** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} \\ \hline Modification 1 & \(0.52\) & \(0.51\) & \(0.48\) & \(0.46\) & \(0.47\) & \(0.10\) & \(0.48\) & \(0.43\) & \(0.15\) \\ Modification 2 & \(0.48\) & \(0.47\) & \(0.54\) & \(0.46\) & \(0.43\) & \(0.53\) & \(0.45\) & \(0.48\) & \(0.04\) \\ Modification 3 & \(0.52\) & \(0.53 signal, the lowest \(HD\) was 0.1. This means that our proposed hash function is very sensitive to the input signal.  
On the other hand, the computational cost to calculate the hash code was measured. With two different mobile devices, a nonlinear relationship was obtained between the duration of the recording and the execution time. However, in both cases, it takes up to 2 s for each minute of the audio recording.  
As a main conclusion, our proposed system is feasible to be used as a useful tool for audio integrity within a forensic field. A user can record a conversation with the app and use it as evidence within a legal process. Legal authority can verify the integrity of the evidence with the hash code stored in the cloud.  
Conceptualization, D.R.; Formal analysis, D.M.B.; Investigation, J.A.A.; Methodology, D.R.; Software, J.A.A.; Validation, D.M.B.; Writing--original draft, D.R. and J.A.A.; and Writing--review and editing, D.M.B.  
This research was funded by Universidad Militar Nueva Granada-Vicerrectoria de Investigaciones grant number IMP-ING-2136.  
The authors would like to thank the reviewers for their comments.  
The authors declare no conflict of interest.