A Pre-Training Framework Based on Multi-Order Acoustic Simulation for Replay Voice Spoofing Detection.

Voice spoofing attempts to break into a specific automatic speaker verification (ASV) system by forging the user's voice and can be used through methods such as text-to-speech (TTS), voice conversion (VC), and replay attacks. Recently, deep learning-based voice spoofing countermeasures have been developed. However, the problem with replay is that it is difficult to construct a large number of datasets because it requires a physical recording process. To overcome these problems, this study proposes a pre-training framework based on multi-order acoustic simulation for replay voice spoofing detection. Multi-order acoustic simulation utilizes existing clean signal and room impulse response (RIR) datasets to generate audios, which simulate the various acoustic configurations of the original and replayed audios. The acoustic configuration refers to factors such as the microphone type, reverberation, time delay, and noise that may occur between a speaker and microphone during the recording process. We assume that a deep learning model trained on an audio that simulates the various acoustic configurations of the original and replayed audios can classify the acoustic configurations of the original and replay audios well. To validate this, we performed pre-training to classify the audio generated by the multi-order acoustic simulation into three classes: clean signal, audio simulating the acoustic configuration of the original audio, and audio simulating the acoustic configuration of the replay audio. We also set the weights of the pre-training model to the initial weights of the replay voice spoofing detection model using the existing replay voice spoofing dataset and then performed fine-tuning. To validate the effectiveness of the proposed method, we evaluated the performance of the conventional method without pre-training and proposed method using an objective metric, i.e., the accuracy and F1-score. As a result, the conventional method achieved an accuracy of 92.94%, F1-score of 86.92% and the proposed method achieved an accuracy of 98.16%, F1-score of 95.08%.

Forensic integrity verification of video recordings based on MTS files.

Digital video is used in criminal trials as evidence with legal responsibility because video content vividly depicts events occurring at a crime scene. However, using sophisticated video editing software, assailants can easily manipulate visible clues for their own benefit. Therefore, the integrity of digital video files acquired or submitted as evidence must be ensured. Forensic analysis of digital video is key to ensuring the integrity of links with individual cameras. In this study, we analyzed whether it is possible to ensure the integrity of MTS video files. Herein, we propose a method to verify the integrity of MTS files encoded by advanced video coding high definition (AVCHD), which is frequently used for video recording. To verify MTS file integrity, we propose five features. Codec information, picture timing, and camera manufacture/model are modified AVI and MP4-like format video verification features. Group of pictures and Universally Unique Identifier patterns were specifically developed for MTS streams. We analyzed the features of 44 standard files recorded using all recording options of seven cameras. We checked whether integrity can be validated on unmanipulated videos recorded in various environments. In addition, we considered whether manipulated MTS files edited in video editing software could be validated. Experimental results show that all unmanipulated and manipulated MTS files with known recording devices were discriminated only when all five features were checked. These results show that the proposed method verifies the integrity of MTS files, strengthening the validity of MTS file-based evidence in trials.

Forensic authentication method for audio recordings generated by Voice Recorder application on Samsung Galaxy Watch4 series.

The number of smartwatch users has been rapidly increasing in recent years. A smartwatch is a wearable device that collects various types of data using sensors and provides basic functions, such as healthcare-related measurements and audio recording. In this study, we proposed the forensic authentication method for audio recordings from the Voice Recording application in the Samsung Galaxy Watch4 series. First, a total of 240 audio recordings from each of the four different models, paired with four different smartphones for synchronization via Bluetooth, were collected and verified. To analyze the characteristics of smartwatch audio recordings, we examined the transition of the audio latency, writable audio bandwidth, timestamps, and file structure between those generated in the smartwatches and those edited using the Voice Recording application of the paired smartphones. In addition, the devices with the audio recordings were examined via the Android Debug Bridge (ADB) tool and compared with the timestamps stored in the file system. The experimental results showed that the audio latency, writable audio bandwidth, and file structure of audio recordings generated by smartwatches differed from those generated by smartphones. Additionally, by analyzing the file structure, audio recordings can be classified as unmanipulated, manipulation has been attempted, or manipulated. Finally, we can forensically authenticate the audio recordings generated by the Voice Recorder application in the Samsung Galaxy Watch4 series by accessing the smartwatches and analyzing the timestamps related to the audio recordings in the file system.

An Efficient Compression Method of Underwater Acoustic Sensor Signals for Underwater Surveillance.

In this paper, we propose a new compression method using underwater acoustic sensor signals for underwater surveillance. Generally, sonar applications that are used for surveillance or ocean monitoring are composed of many underwater acoustic sensors to detect significant sources of sound. It is necessary to apply compression methods to the acquired sensor signals due to data processing and storage resource limitations. In addition, depending on the purposes of the operation and the characteristics of the operating environment, it may also be necessary to apply compression methods of low complexity. Accordingly, in this research, a low-complexity and nearly lossless compression method for underwater acoustic sensor signals is proposed. In the design of the proposed method, we adopt the concepts of quadrature mirror filter (QMF)-based sub-band splitting and linear predictive coding, and we attempt to analyze an entropy coding technique suitable for underwater sensor signals. The experiments show that the proposed method achieves better performance in terms of compression ratio and processing time than popular or standardized lossless compression techniques. It is also shown that the compression ratio of the proposed method is almost the same as that of SHORTEN with a 10-bit maximum mode, and both methods achieve a similar peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) index on average.

Advanced forensic procedure for the authentication of audio recordings generated by Voice Memos application of iOS14.

In this study, we propose an advanced forensic examination procedure for audio recordings generated by the Voice Memos application with iPhone Operation System (iOS)14, to verify that these are the original recordings and have not been manipulated. The proposed examination procedure consists of an analysis of the characteristics of audio recordings and of the file system of the device storing the audio recordings. To analyze the characteristics of audio recordings, we compare the encoding parameters (bitrate, sampling rate, timestamps, etc.) and the file structure to determine whether audio recordings were manipulated. Next, in the device examination step, we analyze the media-log history and temporary files of the file system obtained by mobile forensic tools. For comparative analysis, a total of 100 audio recording samples were obtained through the Voice Memos application from five iPhone mobile handsets of different models with iOS14 installed using Advanced audio coding (AAC) or Apple lossless audio codec (ALAC). As a result of analyzing the encoding parameters between the original and manipulated audio recordings, as well as the temporary files contained in the device file system, the difference in the encoding parameters and the very unique trace of the original audio recordings in the temporary files were confirmed when manipulating the audio recordings. In particular, the primary advantage of our proposed method is its potential ability to recover original audio recordings that were subsequently manipulated via the temporary files examined in the device file system analysis.

A method of forensic authentication of audio recordings generated using the Voice Memos application in the iPhone.

Considering the widespread use of mobile phones, audio recordings of crime scenes are widely used as digital evidence; however, it is important to authenticate the audio recordings before consideration as legal evidence. This study aimed to develop a method to authenticate audio recordings generated using the iPhone through three steps: 1) bitrate/audio latency time analysis of audio recordings, 2) comparison of the file structure/timestamp on audio recordings, and 3) device-based log history examinations for the provenance of audio recordings. Herein, we analyzed audio recording samples from ten different models of mobile handsets of the iPhone with Advanced Audio Coding (AAC) or Apple Lossless Audio Codec (ALAC), through the Voice Memos application depending on the iPhone Operating System (iOS). To analyze the characteristics of these audio recordings, we compared features including audio latency, file format/structure, and timestamps between the audio recordings generated in the iPhone and those edited through the built-in audio editing function. Furthermore, we investigated the log history registered in devices during the generation of the audio recordings. Differences in the audio latency, file size, timestamps, bitrate, and log history were confirmed on the iPhone when manipulating the audio recordings. The present results show that it is possible to verify the authentication of audio recordings generated using the Voice Memos application on iPhone.

Burst packet loss concealment using multiple codebooks and comfort noise for CELP-type speech coders in wireless sensor networks.

In this paper, a packet loss concealment (PLC) algorithm for CELP-type speech coders is proposed in order to improve the quality of decoded speech under burst packet loss conditions in a wireless sensor network. Conventional receiver-based PLC algorithms in the G.729 speech codec are usually based on speech correlation to reconstruct the decoded speech of lost frames by using parameter information obtained from the previous correctly received frames. However, this approach has difficulty in reconstructing voice onset signals since the parameters such as pitch, linear predictive coding coefficient, and adaptive/fixed codebooks of the previous frames are mostly related to silence frames. Thus, in order to reconstruct speech signals in the voice onset intervals, we propose a multiple codebook-based approach that includes a traditional adaptive codebook and a new random codebook composed of comfort noise. The proposed PLC algorithm is designed as a PLC algorithm for G.729 and its performance is then compared with that of the PLC algorithm currently employed in G.729 via a perceptual evaluation of speech quality, a waveform comparison, and a preference test under different random and burst packet loss conditions. It is shown from the experiments that the proposed PLC algorithm provides significantly better speech quality than the PLC algorithm employed in G.729 under all the test conditions.