Audio Steganography Project

Project proposal EECS 452 Fall 2012

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Title of Project: Das Echtzeit-‐Audiosteganographiesystem

Team members: Roy Blankman, Adam Hug, Zhihao Liu, Paul Rigge

I. Introduction and overview of project

The psycho-‐acoustic model of the human auditory system (HAS) has been developed heavily in the last few years to develop high-‐quality music files and compression [1]. The proposed project would have a DSP device inject data into an audio signal such that when the music was played, a mobile receiver can detect the data through the sound waves but a human cannot. Audio steganography has many applications. There are clandestine applications that would allow spies to communicate secretly, but there are also more mainstream uses of this technology. This technique can be used to embed meta-‐data in audio; to add title, artist, and duration information to FM radio and encode weather or traffic information. Audio steganography can also be used to embed a unique token into music (also called watermarking). This can be used for a Digital Rights Management (DRM) scheme that would help limit piracy. The final prototype that we will produce to demonstrate at the Design Exposition will be a fully functional communications system for hidden communications. Both the transmitter and receiver will consist of a TI DSK DSP with appropriate peripherals. Specifically, the transmitter will use a TRS port as an audio input, a USB port for digital input, and a speaker as output while the receiver will use a microphone as input and write all decoded data to a text file. Our data-‐hiding algorithms will be based on those presented in [1]. We will primarily use frequency masking to hide our data. Our channel and source coding schemes will be based on those described in [4]. We will probably use some type of Lempel-‐Ziv compression and a convolutional code.


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II. Description of project

i. The goal of our project is to create two devices, an encoder and decoder. The encoder will sample an audio source from an iPod or computer, hide imperceptible data within those samples and play the modified audio signal through a speaker. The decoder samples the modified song and extracts the hidden message, using an error correcting code to fix any bits altered by the channel. For low data-‐rates, this is certainly feasible. A naïve but functional MATLAB prototype has already been written. Some of the more advanced techniques for improving bandwidth or robustness may be difficult to implement within the constraints of the DSP, but the core idea is definitely achievable.

ii. The system will consist of three major components: a transmitter, channel, and receiver.

The transmitter and receiver will consist of DSPs and the channel will be a speaker and a microphone connected to the transmitter and receiver, respectively. The transmitter has two inputs: audio and data. The output consists of an audio stream that contains both the audio and data inputs. When played through a speaker, a human will only hear the input audio, but the receiver will be able to decrypt the input data embedded in the transmitter’s output. Ideally, the receiver analyzes the audio stream and extracts the same data transmitter initially embedded.

Figure 1. Audio Steganography System

The input data is compressed and then encoded using an error correcting code (perhaps a convolutional code). The transmitter applies a windowed DFT to the input audio and analyzes the spectrum of fixed width time intervals. The data-‐hiding block will analyze this spectrum to find ideal places to hide binary data—these are referred to as masked frequencies. It will then encode the data by modifying the spectrum such that the change


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is imperceptible to the human auditory system. Once complete, an inverse DFT is taken and the modified audio stream is sent to the channel.

Figure 2. Transmitter Design

The receiver applies a windowed DFT to the audio from the channel. A detector identifies masked frequencies and looks for evidence that the transmitter has modified the spectrum. The detector makes its best guess as to what data has been hidden and then the Viterbi algorithm is used to correct errors. The data is decompressed and then displayed on an LCD screen.

Figure 3. Receiver Design

However, there are alternate data-‐hiding schemes our team is considering. Although using frequency masking seems easy to implement, we are concerned that we will not be able to achieve a high enough bitrate using this scheme. According to existing literature, higher-‐bandwidth communications can be achieved using spread spectrum audio steganography.


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Because the mathematics surrounding this scheme is complex, it might take us too long to write functional software implanting this scheme. Finally, if both of the above methods take too long to implement due to their complexity, a last scheme we could use is Echo Hiding [2]. This is a very simple scheme that consists of adding scaled, delayed copies of a signal to itself where a delay of some time t_1 encodes a 1 and a delay of time t_0 encodes a 0. This scheme will almost certainly work, but will unfortunately create audible distortion in the audio signal; therefore, this would be non-‐ideal steganography. iii. Our team foresees possible complications:

a) DSP chips turn out to be too slow to process audio data in real-‐time

Solution: Our project involves mainly software components (i.e. MATLAB/C coding), so we could perform such tasks on computers with more powerful processors. Although this would mean our project would not be running on proper DSP chips, we would still be demonstrating some interesting DSP applications.

b) Speakers and microphones too noisy to allow for reliable communication Solution: Ask for an increased budget in order to buy higher-‐quality sensors.

c) Synchronization more difficult than expected which causes unreliable communication. Solution: We can periodically add barely audible tones to help make synchronization easier for our algorithms.

d) Maximum bitrate of communications system very slow Solution: Use a more complex data hiding scheme that allows for increased bandwidth, perhaps by finding multiple masking frequencies per time interval, or by using some spread spectrum techniques.


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iv. Our preliminary parts list consists of the following: 1) TI C5515 eZDSP We will need two such chips. The reference documents could be found on course website [5].

2) NADY SP-‐4C Dynamic Microphone We need a microphone to input the audio signals into the system. There are no specific requirements on the microphone, so we could just purchase at [6]. The total cost for this item will be $ 15.99.

3) Logitech LS21 2.1 Stereo Speaker System We need a speaker to output the detected and decoded audio information on the user’s end of the system. We could purchase one at [7]. The total cost for this item will be $25.69.

III. Milestones a. Milestone 1: Finish programming data hiding, data recovery and coding

software. This milestone consists of devising and implementing an algorithm for source coding (compression), channel coding (assuming our channel will behave as a binary symmetric channel (BSC) or binary erasure channel (BEC)), data hiding, data recovery, and data decoding. Everything must first be written in MATLAB, and then ported to C for use on the DSP’s. This milestone will rely on our team effectively splitting up the programming so that all the code can be finished on time. We aim to at finish the MATLAB coding by the time school resumes after spring break. It should not take more than a week to port the algorithms over to C.

b. Milestone 2: Integrate software with hardware and demonstrate reliable data

transfer. All the C code written for milestone 1 will be placed on our two DSP’s and any functions such as the DFT (which have optimized implementations for the DSP) must be adjusted to function properly with the hardware. Next, we will have to adjust the code to use microphones, speakers and a serial line as inputs instead of stored digital data. Finally, we will test the system in the EECS 452 lab to


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ensure that it is a viable real-‐time communications system. We aim to finish all the integration before the Design Exposition.

c. A potential issue for milestone 1 might be discovering that devising a data-‐hiding algorithm is more mathematically complex than we expected, and thus the development of such an algorithm might take longer than is required by our project. A potential issue for milestone 2 would be discovering that our chosen hardware, especially the speakers and microphones, introduce excessive noise that precludes reliable communications. Additionally, it is possible that our algorithm implementation would be too complex to be performed in real-‐time on our hardware.

IV. Contributions of each member of team

Audio steganography can be implemented in many ways. At the start of the project, Adam and Zhihao will experiment with an Echo Hiding scheme while Roy and Paul experiment with a frequency masking scheme. Both implementations will initially be coded in MATLAB to see which one (or both) is feasible. Benchmarks that measure feasibility will include bit error rate, bit transfer rate, and how perceptible the modification is to a human observer.

If both turn out to be feasible, then it is possible to combine the two schemes to minimize bit error rate. Otherwise, the more robust implementation will be chosen.

Roy and Paul have a strong background in communications. As such, they will primarily work on the data decoding process. They will transfer the decoding algorithms from MATLAB to C. These algorithms will extract information hidden in sound files. When this is finished, they will modify their code so it works on the C5515 DSP chip. All decoding processes will be done in real time.

Zhihao and Adam both have a strong background in DSP. They will work on the data encoding process. This involves transferring encoding algorithms from MATLAB to C. These algorithms will listen to a sound file and add inaudible modifications. The hardware will utilize the C5515 DSP chip and all encoding processes will be done in real time.


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V. Logistics a. Meeting Schedule

i. Our team will meet every Wednesday afternoon to lay out weekly goals ii. Each team member will be expected to send out emails on Friday and

Monday detailing their progress in our project thread

b. Version control system Since our project requires lots of coding, it is convenient to set up a version control system so that each of us can read and write our code easily. Thus, we set up a Git repository stored in a private AFS space.

c. Planned demonstration for design expo Currently, we plan to have a working communications system to display at the design expo. Such a system would consist of an audio source piped through a DSP that is embedding data in the waveform all in real-‐time and then being played over a loudspeaker. There would then be another DSP taking input from a microphone that would analyze the audio it receives in order to recover the hidden signal. Observers will be allowed to enter in data by typing into a keyboard and see their input transmitted inaudibly over our communications system; the final message will be printed to an LCD screen attached to the receiver’s DSP.


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VI. References and citations

[1] Nedeljko Cvejic. Algorithms for Audio Watermarking and Steganography. [2] Daneil Gruhl, Walter Bender. Echo Hiding. Elsevier. [3] Stéphane Mallat. A Wavelet Tour of Signal Processing. [4] David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003. [5] http://www.eecs.umich.edu/courses/eecs452/refs.html [6] http://www.amazon.com/Nady-‐SP-‐4C-‐NADY-‐Dynamic-‐Microphone/dp/B00009W40D/ref=sr_1_5?s=musical-‐instruments&ie=UTF8&qid=1328468297&sr=1-‐5 [7] http://www.amazon.com/Logitech-‐LS21-‐Stereo-‐Speaker-‐System/dp/B0015C30J0/ref=sr_1_12?s=aht&ie=UTF8&qid=1328468465&sr=1-‐12

Documents

Audio Steganography Project