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 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.

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.

Broadband optical parametric amplification at the communication band with periodically poled lithium niobate.

We report broadband optical parametric generation (OPG) in a single periodically poled lithium niobate crystal with a picosecond pump pulse at a fixed wavelength. We also demonstrate efficient optical parametric amplification of a broadband seed pulse within the quasi-phase-matched OPG band. The broad parametric gain band is attributed to group-velocity matching and degeneracy between the signal and idler, and the broad spectral width of the pumping source.

Invisible ink mark detection in the visible spectrum using absorption difference.

One of popular techniques in gambling fraud involves the use of invisible ink marks printed on the back surface of playing cards. Such covert patterns are transparent in the visible spectrum and therefore invisible to unaided human eyes. Invisible patterns can be made visible with ultraviolet (UV) illumination or a CCD camera installed with an infrared (IR) filter depending on the type of ink materials used. Cheating gamers often wear contact lenses or eyeglasses made of IR or UV filters to recognize the secret marks on the playing cards. This paper presents an image processing technique to reveal invisible ink patterns in the visible spectrum without the aid of special equipment such as UV lighting or IR filters. A printed invisible ink pattern leaves a thin coating on the surface with different refractive index for different wavelengths of light, which results in color dispersion or absorption difference. The proposed method finds the differences of color components caused by absorption difference to detect invisible ink patterns on the surface. Experiment results show that the proposed scheme is effective for both UV-active and IR-active invisible ink materials.

Forged seal detection based on the seal overlay metric.

This paper describes a method for verifying the authenticity of a seal impression imprinted on a document based on the seal overlay metric, which refers to the ratio of an effective seal impression pattern and the noise in the neighborhood of the reference impression region. A reference seal pattern is obtained by taking the average of a number of high-quality impressions of a genuine seal. A target seal impression to be examined, often on paper with some background texts and lines, is segmented out from the background by an adaptive threshold applied to the histogram of color components. The segmented target seal impression is then spatially aligned with the reference by maximizing the count of matching pixels. Then the seal overlay metric is computed for the reference and the target. If the overlay metric of a target seal is below a predetermined limit for the similarity to the genuine, then the target is classified as a forged seal. To further reduce the misclassification rate, the seal overlay metric is adjusted by the filling rate, which reflects the quality of inked pattern of the target seal. Experiment results demonstrate that the proposed method can detect elaborate seal impressions created by advanced forgery techniques such as lithography and computer-aided manufacturing.

Nondestructive imaging of hidden figures on license plates by X-ray radiograph.

In this case, we investigated the modified license plates. The evidences had new embossing pressed serial numbers after erasing the original numbers on the license plates by hammering. The X-ray radiograph could visualize the hidden figures; those were virtually unseen by naked eyes or undetectable by ordinary photography. To reveal the erased figures, we performed image processing with computer software after X-ray radiographs. It proved to be an efficient nondestructive way to visualize the hidden original figures on metals.