Steganalysis of MP3Stego with low embedding-rate using Markov feature

Jin, Chao; Wang, Rangding; Yan, Diqun

doi:10.1007/s11042-016-3264-y

Cited by 28 publications

(19 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Consequently, features extracted from other parts of mp3 bit stream (including MDCT coefficients) have no steganalysis significance. Therefore, methods of [17,25,31] were not applicable to steganalysis of Ump3c and were omitted from our comparison. Table IV compares accuracy of the proposed method with those works for different embedding capacities and in the single-encoder scenario.…”

Section: E Comparison With Previous Methodsmentioning

confidence: 99%

Multi-Layer Architecture for Efficient Steganalysis of UnderMp3Cover in Multi-Encoder Scenario

Ghasemzadeh¹

2019

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

Mp3 is a very popular audio format and hence it can be a good host for carrying hidden messages. Therefore, different steganography methods have been proposed for mp3 hosts. But, current literature has only focused on steganalysis of mp3stego. In this paper we mention some of the limitations of mp3stego and argue that UnderMp3Cover (Ump3c) does not have those limitations. Ump3c makes subtle changes only to the global gain of bitstream and keeps the rest of bitstream intact. Therefore, its detection is much harder than mp3stego. To address this, joint distributions between global gain and other fields of mp3 bit stream are used. The changes are detected by measuring the mutual information from those joint distributions. Furthermore, we show that different mp3 encoders have dissimilar performances. Consequently, a novel multi-layer architecture for steganalysis of Ump3c is proposed. In this manner, the first layer detects the encoder and the second layer performs the steganalysis job. One of advantages of this architecture is that feature extraction and feature selection can be optimized for each encoder separately. We show this multi-layer architecture outperforms the conventional single-layer methods. Comparing results of the proposed method with other works shows an improvement of 20.4% in the accuracy of steganalysis.

show abstract

Section: E Comparison With Previous Methodsmentioning

confidence: 99%

Multi-Layer Architecture for Efficient Steganalysis of UnderMp3Cover in Multi-Encoder Scenario

Ghasemzadeh¹

2019

IEEE Trans.Inform.Forensic Secur.

View full text Add to dashboard Cite

show abstract

“…Miao et al [69], in 2014, proposed two methods for detecting various types of compressed domain steganography (CDBS) in ACELP speech. The first is the Markov method, which divides the fixed codebook index into a list of subsequences; there are N indexes in every subsequence [70]. N represents the number of non-zero-amplitude pulses in every track; then, an N-1order Markov chain model is constructed to analyze it.…”

Section: Steganalysis Based On Fcbmentioning

confidence: 99%

Steganography and Steganalysis in Voice over IP: A Review

Guo

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

The rapid advance and popularization of VoIP (Voice over IP) has also brought security issues. VoIP-based secure voice communication has two sides: first, for legitimate users, the secret voice can be embedded in the carrier and transmitted safely in the channel to prevent privacy leakage and ensure data security; second, for illegal users, the use of VoIP Voice communication hides and transmits illegal information, leading to security incidents. Therefore, in recent years, steganography and steganography analysis based on VoIP have gradually become research hotspots in the field of information security. Steganography and steganalysis based on VoIP can be divided into two categories, depending on where the secret information is embedded: steganography and steganalysis based on voice payload or protocol. The former mainly regards voice payload as the carrier, and steganography or steganalysis is performed with respect to the payload. It can be subdivided into steganography and steganalysis based on FBC (fixed codebook), LPC (linear prediction coefficient), and ACB (adaptive codebook). The latter uses various protocols as the carrier and performs steganography or steganalysis with respect to some fields of the protocol header and the timing of the voice packet. It can be divided into steganography and steganalysis based on the network layer, the transport layer, and the application layer. Recent research results of steganography and steganalysis based on protocol and voice payload are classified in this paper, and the paper also summarizes their characteristics, advantages, and disadvantages. The development direction of future research is analyzed. Therefore, this research can provide good help and guidance for researchers in related fields.

show abstract

“…These features are chosen to reduce the dimensionality of the analysis. Although extracting features from an MP3 constitutes a destructive process, the extracted features are expected to be sufficiently descriptive, which means that the information relevant to the steganalysis is retained [20]. The third parameter is the model or classifier.…”

Section: Related Workmentioning

confidence: 99%

Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things

et al. 2020

View full text Add to dashboard Cite

With the unprecedented growing demand for communication between Internet of Things (IoT) devices, the upcoming 5G and 6G technologies will pave the path to a widespread use of ultra-reliable low-latency applications in such networks. However, with most of the sensitive data being transmitted over wireless links, security, privacy and trust management are emerging as big challenges to handle. IoT applications vary, from self-driving vehicles, drone deliveries, online shopping, IoT smart cities, e-healthcare and robotic assisted surgery, with many applications focused on Voice over IP (VoIP) and require securing data from potential eavesdroppers and attackers. One well-known technique is a hidden exchange of secret data between the devices for which security can be achieved with audio steganography. Audio steganography is an efficient, reliable and low-latency mechanism used for securely communicating sensitive data over wireless links. MPEG-1 Audio Layer 3’s (MP3’s) bit rate falls within the acceptable sound quality required for audio. Its low level of noise distortion does not affect its sound quality, which makes it a good carrier medium for steganography and watermarking. The strength of any embedding technique lies with its undetectability measure. Although there are many detection techniques available for both steganography and watermarking, the detection accuracy of secret data has been proven erroneous. It has yet to be confirmed whether different bit rates or a constant sampling rate for embedding eases detection. The accuracy of detecting hidden information in MP3 files drops with the influence of the compression rate or increases. This drop or increase is caused by either the increase in file track size, the sampling rate or the bit rate. This paper presents an experimental study that evaluates the detection accuracy of the secret data embedded in MP3. Training data were used for the embedding and detection of text messages in MP3 files. Several iterations were evaluated. The experimental results show that the used approach was effective in detecting the embedded data in MP3 files. An accuracy rate of 97.92% was recorded when detecting secret data in MP3 files under 128-kbps compression. This result outperformed the previous research work.

show abstract

Steganalysis of MP3Stego with low embedding-rate using Markov feature

Cited by 28 publications

References 10 publications

Multi-Layer Architecture for Efficient Steganalysis of UnderMp3Cover in Multi-Encoder Scenario

Multi-Layer Architecture for Efficient Steganalysis of UnderMp3Cover in Multi-Encoder Scenario

Steganography and Steganalysis in Voice over IP: A Review

Evolutionary Detection Accuracy of Secret Data in Audio Steganography for Securing 5G-Enabled Internet of Things

Contact Info

Product

Resources

About