105 research outputs found
Steganography integration into a low-bit rate speech codec
Low bit-rate speech codecs have been widely used in audio communications like VoIP and mobile communications, so that steganography in low bit-rate audio streams would have broad applications in practice. In this paper, the authors propose a new algorithm for steganography in low bit-rate VoIP audio streams by integrating information hiding into the process of speech encoding. The proposed algorithm performs data embedding while pitch period prediction is conducted during low bit-rate speech encoding, thus maintaining synchronization between information hiding and speech encoding. The steganography algorithm can achieve high quality of speech and prevent detection of steganalysis, but also has great compatibility with a standard low bit-rate speech codec without causing further delay by data embedding and extraction. Testing shows, with the proposed algorithm, the data embedding rate of the secret message can attain 4 bits / frame (133.3 bits / second)
Universal steganography model for low bit-rate speech codec
Low bit-rate speech codec offers so many advantages over other codecs that it has become increasingly popular in audio communications such as mobile and VoIP (Voice over Internet Protocol) communications, and thus researching steganography in low bit-rate speech codec is of important significance. In this study, we proposed a universal VoIP steganography model for low bit-rate speech codec that uses the PESQ deterioration rate and the decoding error to automatically choose a data embedding algorithm for each VoIP bitstream, which enables ones to achieve covert communications using a low bit-rate speech codec efficiently and securely. Since no or little attention has been paid to steganography in iSAC (Internet Speech Audio Codec), it was chosen as the test codec to verify the effectiveness, security, and practicability of the proposed steganography model. The experimental results show that, with the proposed steganography model, it achieved the average PESQ deterioration rate of 4.04% (less than 5%, indicating strong imperceptibility) and a high data hiding capacity up to 12 bits/frame (400 bits/second, three times larger than other methods), and the proposed steganography model could effectively resist the latest steganalysis
A Review on Edge Based Image Steganography
Security of the information has always been the interesting area for researchers. Integrity, Confidentiality, and Authentication are main security principles. There are so many techniques developed to achieve these security principles using cryptography. But all these techniques are unable to keep the communication secret. Although the attacker is unable to access the secret message but he knows about the existence of the message. To overcome this limitation of cryptography a new idea is proposed by researchers that is Steganography. Steganography is the art of concealing secret message in a carrier such as text, image, audio, video and protocol messages. Different Steganography techniques are used based on these carrier messages and way of hiding. The choice of these method is depends on the requirements of application. Some application requires huge data embedding and some require high secrecy. This paper gives a review on image Steganography based on edge detection and their comparative study. It also gives an overview of basic edge detection techniques
A Multi-Algorithm, High Reliability Steganalyzer Based on Services Oriented Architecture
In this prospectus we are proposing to develop a unified Steganalyzer that can not only work with different media types such as images and audio, but further is capable of providing improved accuracy in stego detection through the use of multiple algorithms running in parallel. Our proposed system integrates different steganalysis techniques in a reliable Steganalyzer with distributed and Services Oriented Architecture (SOA). The distributed architecture not only allows for concurrent processing to speed up the system, but also provides higher reliability than reported in the existing literature. The extendable nature of the SOA implementation allows for easy addition of new Steganalysis algorithms to the system in terms of services. The universal steganalysis technique proposed in this prospectus involves two processes; feature extraction and feature classification. Three methods are used for feature extraction; Mel-Cepstrum and Markov (for audio), and Intra-blocks for (JPEG images). The feature classification process is implemented using neural network classifier. The unified steganalyzer is tested for JPEG images and WAV audio files. The accuracy of classification ranges from 96.8% to 99.8% depending on the object type and the feature extraction method. In particular, an enhancement of Mel-Cepstrum technique is proposed that achieves an accuracy of 99.8%. This is significantly better than detection accuracy of 89.9% to 98.6% [Liu 2011] where even a much larger training dataset was used than ours
A Method to Detect AAC Audio Forgery
Advanced Audio Coding (AAC), a standardized lossy compression scheme for digital audio, which was designed to be the successor of the MP3 format, generally achieves better sound quality than MP3 at similar bit rates. While AAC is also the default or standard audio format for many devices and AAC audio files may be presented as important digital evidences, the authentication of the audio files is highly needed but relatively missing. In this paper, we propose a scheme to expose tampered AAC audio streams that are encoded at the same encoding bit-rate. Specifically, we design a shift-recompression based method to retrieve the differential features between the re-encoded audio stream at each shifting and original audio stream, learning classifier is employed to recognize different patterns of differential features of the doctored forgery files and original (untouched) audio files. Experimental results show that our approach is very promising and effective to detect the forgery of the same encoding bit-rate on AAC audio streams. Our study also shows that shift recompression-based differential analysis is very effective for detection of the MP3 forgery at the same bit rate
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