&lt;title&gt;Audio steganography by amplitude or phase modification&lt;/title&gt;

Gopalan, K.; Wenndt, Stanley J.; Adams, Scott F.; Haddad, Darren

doi:10.1117/12.473138

Cited by 10 publications

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“…Many methods and techniques for embedding messages have been implemented in the past few years. Gopalan et al (Kaliappan Gopalan et al 2003) proposed audio steganography for embedding messages using amplitude or phase modification. (Q. Liu, Sung, and Qiao 2011) was noticed as close paper and steganalysis of QIM steganography in low bit-rate speech signals where the loss of quality of speech is less.…”

Section: Discussionmentioning

confidence: 99%

“…Gopalan (K. Gopalan 2003) presented a method of embedding a covert audio message into a cover file by altering one bit in each of the cover utterance samples. Gopalan et al (Kaliappan Gopalan et al 2003) provided two methods of secret message embedding by modifying the phase or amplitude of perceptually masked or significant regions of a host. A direct sequence spread-spectrum watermarking process with strong robustness against common audio editing procedures was proposed in (Kirovski and Malvar 2003).…”

Section: Introductionmentioning

confidence: 99%

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Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

Reddy¹

2021

revistageintec

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Aim: The main motive of this study is to perform Adaptive Multi Rate Wideband (AMR-WB) Speech Steganography in network security to produce the stego speech with less loss of quality while increasing embedding capacities. Materials and Methods: TIMIT Acoustic-Phonetic Continuous Speech Corpus dataset consists of about 16000 speech samples out of which 1000 samples are taken and 80% pretest power for analyzing the speech steganography. AMR-WB Speech steganography is performed by Diameter Neighbor codebook partition algorithm (Group 1) and Neighbor Index Division codebook division algorithm (Group 2). Results: The AMR-WB speech steganography using DN codebook partition obtained average quality rate of 2.8893 and NID codebook division algorithm obtained average quality rate of 2.4196 in the range of 300bps embedding capacity. Conclusion: The outcomes of this study proves that the decrease in quality in NID is twice more than the DN based steganography while increasing the embedding capacities.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

Reddy¹

2021

revistageintec

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“…Information of non-LSB methods are presented in Table 3. These methods include: Addition Method (AM) [10], Amplitude Modulation (AMod) [40], DWT-FFT [41], DWT fusion [42], Mp3Stego [43], Publimark [30], Quantization Index Method (QIM) [10], Stego wave [44], Stochastic Modulation (StMod) [45], WaSpStego [15], Wavelet LSB [10], Integer wavelet (I-Wavelet) [46], and DSSS in the frequency domain (DSSS + DCT) [47]. Table 4 presents the embedding algorithms that each paper has investigated.…”

Section: Discussionmentioning

confidence: 99%

Comprehensive review of audio steganalysis methods

Ghasemzadeh

Kayvanrad

2018

IET signal process.

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Recently, merging signal processing techniques with information security services has found a lot of attention. Steganography and steganalysis are among those trends. Like their counterparts in cryptology, steganography and steganalysis are in a constant battle. Steganography methods try to hide the presence of covert messages in innocuouslooking data, whereas steganalysis methods try to break steganography algorithms and reveal the existence of hidden messages. The stream nature of audio signals, their popularity, and their wide spread usage make them very good candidates for steganography. This has led to a very rich literature on both steganography and steganalysis of audio signals. This paper intends to conduct a comprehensive review of audio steganalysis methods aggregated over near fifteen years. To that end, both compressed and con-compressed methods are reviewed, and then their important details are presented in different tables. Furthermore, some of the most recent audio steganalysis methods (both non-compressed and compressed ones) are implemented and comparative analyses on their performances are conducted. Finally, the paper provides some possible directions for future researches on audio steganalysis.

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“…The stego signal has an outward appearance almost identical to that of the original signal.Therefore, whereas cryptography protects the contents of a message, steganography can be said to protect both messages and communicating parties. Several steganography methods by indirectly exploiting various limitations of HAS have been proposed with varying degrees of success [1][2][3][4][5]. These methods alter the audio signal by a small amount so that the change is imperceptible to HAS thereby, avoiding suspicion.…”

Section: Introductionmentioning

confidence: 99%

Audio Steganography Using Differential Phase Encoding

Parab

Nathan

Talele

2011

Communications in Computer and Information Science

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Abstract. This paper proposes a novel technique of camouflaging a block of binary data in an audio file for secured covert communication. The phase of the cover signal is used as a medium for steganography. Cover signal may be a song or a speech signal. Instead of modifying the absolute phase values of selected component frequencies, the difference between the phase values of the selected component frequencies and their adjacent frequencies of the cover signal are modified by a small amount to hide data bits .The change is so minute that it hardly alters the phase spectrum and is imperceptible to human auditory system(HAS). This technique provides robustness, noise insensitivity, high data capacity and almost accurate data recovery from the cover signal.

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<title>Audio steganography by amplitude or phase modification</title>

Cited by 10 publications

References 0 publications

Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

Optimized Multirate Wideband Speech Steganography for Improving Embedding Capacity Compared with Neighbor-Index-Division Codebook Division Algorithm

Comprehensive review of audio steganalysis methods

Audio Steganography Using Differential Phase Encoding

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