scholarly journals RANCANG BANGUN APLIKASI DETEKSI KEASLIAN FILE AUDIO MENERAPKAN METODE CRC 32

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Dian Paramita Br Perangin-angin
Keyword(s):  
Matlab Software ◽  
Audio File ◽  
Digital File ◽  
Safety Technique ◽  
Audio Files ◽  
Previous Paragraph ◽  
Hash Codes

The authenticity of a digital file is something that must be able to be guaranteed its existence, considering that there are so many devices that can be used to carry out manipulation of the digital file. One of the digital files discussed in this study is an audio file with MP3 file extension. Please note that MP3 audio files are now very easy to obtain, even very easy to manipulate and insert objects in, so we need a safety technique to maintain the authenticity of the audio file. Overcoming the problem that has been explained in the previous paragraph, the appropriate security technique used is the hash cryptographic technique, by applying the CRC 32 algorithm. The application of CRC 32 aims to generate hash codes from MP3 audio files that can be used as key codes for authentication (key authenticity) of MP3 audio files. The results of this study are a representation of the technical explanation of the application of the CRC 32 algorithm in generating MP3 audio file hash codes, which the CRC 32 algorithm is applied to applications that have been designed and built using the help of MATLAB software version 6.1.Keywords: File Authenticity, Audio File, CRC 32, MATLAB 6.1

Implementasi Penyembunyian Pesan Pada Audio Dengan Metode Bit-Plane Complexity Segmentation (BPCS)

2020 ◽  
Vol 7 (2) ◽  
pp. 285
Author(s):  
Ira Sarifah Rangkuti ◽  
Edward Robinson Siagian
Keyword(s):  
Audio File ◽  
Plane Method ◽  
Bit Plane ◽  
Audio Files ◽  
Application Design

Cryptography is the science used to maintain the confidentiality of messages, by scrambling messages that are illegible. However, the results of randomization can raise suspicions that confidential communications are being carried out. Steganography can be used to overcome these problems. The trick is the message is inserted in the audio file by the Bit-Plane method. then add a message behind the file. To prevent messages from being read, the message is encrypted first with the Bit-Plane method before inserting. Application design results can be used to hide secret messages that have been encrypted with the Bit-Plane method to audio files, so as to avoid suspicion of confidential communications

scholarly journals An Optimized Approach for Secure Data Transmission Using Spread Spectrum Audio Steganography, Chaos Theory, and Social Impact Theory Optimizer

2019 ◽  
Vol 2019 ◽  
pp. 1-10
Author(s):  
Rohit Tanwar ◽  
Kulvinder Singh ◽  
Mazdak Zamani ◽  
Amit Verma ◽  
Prashant Kumar
Keyword(s):  
Spread Spectrum ◽  
Social Impact ◽  
Random Noise ◽  
Research Problem ◽  
Masking Effect ◽  
Audio File ◽  
Audio Steganography ◽  
Impact Theory ◽  
Secure Data Transmission ◽  
Audio Files

Being easy to understand and simple to implement, substitution technique of performing steganography has gain wide popularity among users as well as attackers. Steganography is categorized into different types based on the carrier file being used for embedding data. The audio file is focused on hiding data in this paper. Human has associated an acute degree of sensitivity to additive random noise. An individual is able to detect noise in an audio file as low as one part in 10 million. Given this limitation, it seems that concealing information within audio files would be a pointless exercise. Human auditory system (HAS) experiences an interesting behavior known as masking effect, which says that the threshold of hearing of one type of sound is affected by the presence of another type of sound. Because of this property, it is possible to hide some data inside an audio file without being noticed. In this paper, the research problem for optimizing the audio steganography technique is laid down. In the end, a methodology is proposed that effectively resolves the stated research problem and finally the implementation results are analyzed to ensure the effectiveness of the given solution.

scholarly journals Ternary coded melody as blind audio watermark

2020 ◽  
Vol 12 (1) ◽  
pp. 28-33
Author(s):  
Rustam Latypov ◽  
Evgeni Stolov
Keyword(s):  
Time Domain ◽  
Amplitude Modulation ◽  
Strong Correlation ◽  
Extended Version ◽  
Conference Paper ◽  
Audio File ◽  
B Splines ◽  
A New Technique ◽  
Audio Files ◽  
The Time Domain

In this paper, we developed a new technique for blind embedding of ternary coded watermarks into audio files. Usage of ternary coding increases payload of the method that can be considered as an advantage against binary-coded watermarks. A well-known melody is presented as a sequence of ternary digits (trits) and is used as a watermark. This sequence is embedded into the time domain of a host audio file through amplitude modulation and B-splines. There is a version of that procedure where the clean copy of the container is necessary for extraction watermark [1]. In our approach, we exclude that container and convert the method into a blind one. The strong correlation between neighbor samples in the container is used to this end. A procedure based on neuron net is suggested for enhancement perception of ternary coded music. In this case, we exploit the correlation between samples in the watermark melody. It is supposed that a person checks the mark's existence, and he/she can recognize the melody even after significant distortions. The resistance of the technique to the most successful attacks is investigated. The paper is an extended version of the conference paper [1].

scholarly journals Perancangan Aplikasi Penyisipan Pesan pada File Mp3 menggunakan Metode Parity Coding dan Enkripsi Caesar Cipher

2018 ◽  
Author(s):  
Citra Kurniawan ◽  
Hery Setyo Putranto
Keyword(s):  
Theoretical Data ◽  
Text Messages ◽  
Primary Data ◽  
Literature Study ◽  
Audio File ◽  
Original Message ◽  
Audio Files

The science of hiding messages in a media is an interesting science to learn. Inserting messages either in text, image, sound and video messages requires input in the form of digital files that will be inserted messages, messages to be inserted (message), and keys (keys). More MP3 are chosen for use than using image files. In hiding messages, a format that is more common is used will be chosen so as not to cause excessive suspicion. Encryption is very important in cryptography, is the security of the data sent to maintain its confidentiality. The type of data in writing this report is the data obtained from the literature study to obtain theoretical data that is used as supporting literature to support the research conducted. The source of the data obtained is primary data as a reference, in this case the author gets data from previous researchers that are related to the research that the author did. The steganography method used in this research is parity coding, which is inserting messages / information into the carrier media in the form of MP3 audio files by first being encrypted using cipher caesarean cryptography encryption technique, which is an algorithm with substitution techniques where each letter in the bright text (plaintext ) replaced by another letter that has a certain position difference in the alphabet. This research was made using Visual Basic.Net software. The results of this study in the form of data that has been encrypted using caesar cipher method and hidden into MP3 Audio File then extracted to get text messages and decrypted to get the original message contained in the mp3 file.

scholarly journals Dementia risks identified by vocal features via telephone conversations: A novel machine learning prediction model

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0253988
Author(s):  
Akihiro Shimoda ◽  
Yue Li ◽  
Hana Hayashi ◽  
Naoki Kondo
Keyword(s):  
Machine Learning ◽  
Prediction Model ◽  
Predictive Performance ◽  
Gradient Boosting ◽  
Validation Data ◽  
Audio File ◽  
Extreme Gradient Boosting ◽  
Audio Data ◽  
Data Files ◽  
Audio Files

Due to difficulty in early diagnosis of Alzheimer’s disease (AD) related to cost and differentiated capability, it is necessary to identify low-cost, accessible, and reliable tools for identifying AD risk in the preclinical stage. We hypothesized that cognitive ability, as expressed in the vocal features in daily conversation, is associated with AD progression. Thus, we have developed a novel machine learning prediction model to identify AD risk by using the rich voice data collected from daily conversations, and evaluated its predictive performance in comparison with a classification method based on the Japanese version of the Telephone Interview for Cognitive Status (TICS-J). We used 1,465 audio data files from 99 Healthy controls (HC) and 151 audio data files recorded from 24 AD patients derived from a dementia prevention program conducted by Hachioji City, Tokyo, between March and May 2020. After extracting vocal features from each audio file, we developed machine-learning models based on extreme gradient boosting (XGBoost), random forest (RF), and logistic regression (LR), using each audio file as one observation. We evaluated the predictive performance of the developed models by describing the receiver operating characteristic (ROC) curve, calculating the areas under the curve (AUCs), sensitivity, and specificity. Further, we conducted classifications by considering each participant as one observation, computing the average of their audio files’ predictive value, and making comparisons with the predictive performance of the TICS-J based questionnaire. Of 1,616 audio files in total, 1,308 (81.0%) were randomly allocated to the training data and 308 (19.1%) to the validation data. For audio file-based prediction, the AUCs for XGboost, RF, and LR were 0.863 (95% confidence interval [CI]: 0.794–0.931), 0.882 (95% CI: 0.840–0.924), and 0.893 (95%CI: 0.832–0.954), respectively. For participant-based prediction, the AUC for XGboost, RF, LR, and TICS-J were 1.000 (95%CI: 1.000–1.000), 1.000 (95%CI: 1.000–1.000), 0.972 (95%CI: 0.918–1.000) and 0.917 (95%CI: 0.918–1.000), respectively. There was difference in predictive accuracy of XGBoost and TICS-J with almost approached significance (p = 0.065). Our novel prediction model using the vocal features of daily conversations demonstrated the potential to be useful for the AD risk assessment.

LSBR Speech Steganalysis Based on Percent of Equal Adjacent Samples

2021 ◽  
Author(s):  
Saeid Yazdanpanah ◽  
Mohammad Kheyrandish ◽  
Mohammad Mosleh
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Statistical Characteristics ◽  
Least Significant Bit ◽  
Implementation Cost ◽  
Audio File ◽  
Audio Files ◽  
Mlp Classifier ◽  
Processing Techniques ◽  
Signal Processing Techniques

Wide utilization of audio files has attracted the attention of cyber-criminals to employ this media as a cover for their concealed communications. As a countermeasure and to protect cyberspace, several techniques have been introduced for steganalysis of various audio formats, such as MP3, VoIP, etc. The combination of machine learning and signal processing techniques has helped steganalyzers to obtain higher accuracies. However, as the statistical characteristics of a normal audio file differ from the speech ones, the current methods cannot discriminate clean and stego speech instances efficiently. Another problem is the high numbers of extracted features and analysis dimensions that drastically increase the implementation cost. To tackle these, this paper proposes the Percent of Equal Adjacent Samples (PEAS) feature for single-dimension least-significant-bit replacement (LSBR) speech steganalysis. The model first classifies the samples into speech and silence groups according to a threshold which has been determined through extensive experiments. It then uses an MLP classifier to detect stego instances and determine the embedding ratio. PEAS steganalysis detects 99.8% of stego instances in the lowest analyzed embedding ratio — 12.5% — and its sensitivity increases to 100% for the ratios of 37.5% and above.

A NEW DATA EMBEDDING METHOD FOR MPEG LAYER III AUDIO STEGANOGRAPHY

2016 ◽  
Vol 78 (7-5) ◽  
Author(s):  
Mohsen Bazyar ◽  
Rubita Sudirman
Keyword(s):  
Computational Complexity ◽  
Experimental Results ◽  
Huffman Coding ◽  
Data Embedding ◽  
Embedding Capacity ◽  
Carrier Medium ◽  
Audio File ◽  
Huffman Codes ◽  
Information Embedding ◽  
Audio Files

A new method of MP3 steganography is proposed with emphasis on increasing the steganography capacity of the carrier medium. This paper proposes a data embedding algorithm to hide more information for compressed bitstream of MP3 audio files. The sign bits of Huffman codes are selected as the stego-object according to the Huffman coding characteristic in region of Count1. Embedding process does not require the main MP3 audio file during the extraction of hidden message and the size of MP3 file cannot be changed in this step. Our proposed method caused much higher information embedding capacity with lower computational complexity compared with MP3Stego tools. Experimental results show an excellent imperceptibility for the new algorithm.  

scholarly journals Dataset for file fragment classification of audio file formats

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Atieh Khodadadi ◽  
Mehdi Teimouri
Keyword(s):  
File Format ◽  
Network Forensics ◽  
Audio File ◽  
Data Description ◽  
File Formats ◽  
Public Dataset ◽  
Audio Files ◽  
Research Challenge

Abstract Objectives File fragment classification of audio file formats is a topic of interest in network forensics. There are a few publicly available datasets of files with audio formats. Therewith, there is no public dataset for file fragments of audio file formats. So, a big research challenge in file fragment classification of audio file formats is to compare the performance of the developed methods over the same datasets. Data description In this study, we present a dataset that contains file fragments of 20 audio file formats: AMR, AMR-WB, AAC, AIFF, CVSD, FLAC, GSM-FR, iLBC, Microsoft ADPCM, MP3, PCM, WMA, A-Law, µ-Law, G.726, G.729, Microsoft GSM, OGG Vorbis, OPUS, and SPEEX. Corresponding to each format, the dataset contains the file fragments of audio files with different compression settings. For each pair of file format and compression setting, 210 file fragments are provided. Totally, the dataset contains 20,160 file fragments.

IMPLEMENTASI METODE MD5 UNTUK MENDETEKSI ORISINALITAS FILE AUDIO

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
Sapria Ulandari Lubis
Keyword(s):  
Data Transmission ◽  
Object File ◽  
Audio File ◽  
Cryptographic Algorithm ◽  
Encryption Decryption ◽  
Hash Codes

The problem of the originality of the file is about how the file can be maintained its authenticity and ensures that the file has never been changed at all. In this study the object file used is the audio file. The process of manipulating files often occurs during data transmission activities from the sender to the recipient. The solution that can be done to handle the crime of manipulation is to apply a hash type cryptographic technique in which the hash value is encrypted from an audio file that can be used as a keyword to ensure the audio file has never been manipulated or has been manipulated. The hash cryptographic algorithm used is the type of MD5 algorithm. The results obtained from the process of implementing the MD5 algorithm using MATLAB R2010a software and the HASH Pro application are audio file hash codes. Keywords: Implementation, Encryption, Decryption, MD5 Method, Cryptography.

scholarly journals A Deep Learning Based System to Predict the Noise (Disturbance) in Audio Files

2020 ◽  
Author(s):  
Kartik P V S M S ◽  
Jeyakumar G
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Prediction Accuracy ◽  
Automated Systems ◽  
Audio File ◽  
Audio Processing ◽  
Proposed Model ◽  
Working Principle ◽  
Audio Files ◽  
Deep Learning Model

Generally, people prefer their audio to be with very good clarity. They want no disturbances during any interaction and while listening audio files. Automated systems to remove disturbances in an audio file to bring good clarity real time audio communications are in high demand. In this paper a deep learning model to detect the noises in a given audio file is proposed and it working principle is explained. The proposed model was trained, first, to predict the places of noise in the audio file by a well-defined training set which consists of set of audio files with the interval of clear audio and noise. After training, the proposed model predicts the area of disturbance (noise) in any given audio file using the integrated techniques of deep learning and audio processing, and the results are reported. The prediction accuracy of the model was found 90.50 %.

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