Objective measures, sensors and computational techniques for stress recognition and classification

Conveying information secretly and establishing hidden relationship has been of interest since long past. Text documents have been widely used since very long time ago. Therefore, we have witnessed different method of hiding information in texts (text steganography) since past to the present. In this paper we introduce a new approach for steganography in Persian and Arabic texts. Considering the existence of too many points in Persian and Arabic phrases, in this approach, by vertical displacement of the points, we hide information in the texts. This approach can be categorized under feature coding methods. This method can be used for Persian/Arabic Watermarking. Our method has been implemented by JAVA programming language.

/pdf/a-new-approach-to-persian-arabic-text-steganography-3cebkg7z5q.pdf

A New Approach to Persian/Arabic Text Steganography

The rapid spread in digital data usage in many real life applications have urged new and effective ways to ensure their security. Efficient secrecy can be achieved, at least in part, by implementing steganograhy techniques. Novel and versatile audio steganographic methods have been proposed. The goal of steganographic systems is to obtain secure and robust way to conceal high rate of secret data. We focus in this paper on digital audio steganography, which has emerged as a prominent source of data hiding across novel telecommunication technologies such as covered voice-over-IP, audio conferencing, etc. The multitude of steganographic criteria has led to a great diversity in these system design techniques. In this paper, we review current digital audio steganographic techniques and we evaluate their performance based on robustness, security and hiding capacity indicators. Another contribution of this paper is the provision of a robustness-based classification of steganographic models depending on their occurrence in the embedding process. A survey of major trends of audio steganography applications is also discussed in this paper.

https://link.springer.com/content/pdf/10.1186%2F1687-4722-2012-25.pdf

Comparative study of digital audio steganography techniques

This paper presents a new steganography approach suitable for Arabic texts. It can be classified under steganography feature coding methods. The approach hides secret information bits within the letters benefiting from their inherited points. To note the specific letters holding secret bits, the scheme considers the two features, the existence of the points in the letters and the redundant Arabic extension character. We use the pointed letters with extension to hold the secret bit ‘one’ and the un-pointed letters with extension to hold ‘zero’. This steganography technique is found attractive to other languages having similar texts to Arabic such as Persian and Urdu. Keywords — Arabic text, Cryptography, Feature coding, Information security, Text steganography, Text watermarking.

/pdf/a-novel-arabic-text-steganography-method-using-letter-points-qto4kumr0b.pdf

A Novel Arabic Text Steganography Method Using Letter Points and Extensions

Increased use of electronic communication has given birth to new ways of transmitting information securely. Audio steganography is the science of hiding some secret text or audio information in a host message. The host message before steganography and stego message after steganography have the same characteristics. Least Significant Bit (LSB) modification technique is the most simple and efficient technique used for audio steganography. The conventional LSB modification technique is vulnerable to steganalysis. This paper proposes two ways to improve the conventional LSB modification technique. The first way is to randomize bit number of host message used for embedding secret message while the second way is to randomize sample number containing next secret message bit. The improvised proposed technique works against steganalysis and decreases the probability of secret message being extracted by an intruder. Advanced Encryption Standard (AES) with 256 bits key length is used to secure secret message in case the steganography technique breaks. Proposed technique has been tested successfully on a. wav file at a sampling frequency of 8000 samples/second with each sample containing 8 bits.

An enhanced least significant bit modification technique for audio steganography

A method of embedding a covert audio message in a cover utterance for secure communication is presented. The covert message is represented in a compressed form with possibly encryption and/or encoding for added security. One bit in each of the samples of a given cover utterance is altered in accordance with the data bits and a key. The same key is used to retrieve the embedded bits at the receiver. The results, based on cover signals from a clean TIMIT utterance and a noisy aircraft cockpit utterance, show that the technique meets several major criteria for successful covert communication.

Audio steganography using bit modification

A method of embedding information in the cepstral domain of a cover audio signal is described for audio steganography applications. The proposed technique combines the commonly employed psychoacoustical masking property of the human auditory system with the decorrelation property of the speech cepstrum, and achieves imperceptible embedding, large payload, and accurate data retrieval. Results of embedding using a clean and a noisy hot utterance show the embedded information is robust to additive noise and bandpass filtering.

Audio steganography by cepstrum modification

This paper reports the results of correlation between demodulated amplitude and frequency variations from the AM-FM speech model and the heart rate of a fighter aircraft flight controller. It has been found that the peak frequencies in the spectrum of the amplitude envelope of the model follow F0 regardless of the center frequency of analysis. This tracking of F0 gives a qualitative estimate of stress level-as measured by the heart rate-relative to a neutral state of stress, with no a priori knowledge of F0 or formants. Additionally, the mean values of F0 estimates at low and high heart rates increased significantly from those at a neutral state. Formant tracking showed an increase in F3 at both high and low heart rates while F4 generally varied directly with the heart rate.

On the effect of stress on certain modulation parameters of speech

Detection, classification, and line-of-sight range estimation of drones are vital for security, safety, and privacy reasons. Representation of the audio emissions of drones in a Fourier-Bessel (FB) series expansion is proposed for the identification of a drone and/or the prediction of its range from an observation point. A deep learning network employing the FB series coefficients as the preprocessed input has been shown to classify accurately each of seven drones flying in a controlled environment in about 84 % of cases. For the case of any one of three drones flying outdoors, presence of the drone—as opposed to background noise—was detected correctly with few false positive and false negative results. Additionally, the range of the drone—from 2.5 m to 935.6 m—was estimated to be within ±50 cm of actual line-of-sight distance in over 85 % of the available test cases.

Detection and classification of drones using Fourier-Bessel series representation of acoustic emissions

With increased use of drones in a variety of situations, it is imperative that efficient means of detecting the type of unmanned aerial vehicle and/or its range are developed from security, privacy, and safety perspective. This paper describes the application of an acoustic feature set in a deep learning network for the estimation of the line-of-sight range of drones. The set of spectral energy values over nonuniform bands within the range of audio recordings of open space drone noise has been shown to predict the range to a reasonable degree of accuracy. The energy feature set, when augmented with low frequency spectral components, raised prediction accuracy to within 85 cm of mean error and a standard deviation of 12 m for test cases ranging from 10 m to 935 m. Additionally, the spectral band energy applied to classify the range quantized into 1 m intervals resulted in better than 87% accuracy with a fixed error of ±50 cm over the entire range. Adding low frequency spectral components to the band energy set raised the correct range classification to 97%. For classification of seven tethered drones, the band energy feature set resulted in 99.9% accuracy.

K. Gopalan

Papers

Audio steganography using bit modification

Audio steganography by cepstrum modification

On the effect of stress on certain modulation parameters of speech

Detection and classification of drones using Fourier-Bessel series representation of acoustic emissions

Acoustic detection of drone range and type using nonuniform band energy features