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Multimedia Security

This article provides an overview of audio watermarking schemes, including spread spectrum information embedding, implementation parameters, and application examples. It explores the challenges and benefits of transmitting information acoustically and discusses various data hiding procedures for acoustic data transmission.

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Multimedia Security

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  1. Introduction to Audio Watermarking SchemesN. Lazic and P. Aarabi, “Communication over an Acoustic Channel Using Data Hiding Techniques, ” IEEE Transactions on Multimedia, Vol. 8, No. 5, October 2006 Multimedia Security

  2. Outline • Problem model • Previous work • Spread spectrum information embedding • Implementation parameters • Simulation and experimental results • Comparison to other techniques • Application example: a localization and navigation system • Conclusions and future work

  3. Information Transmission • Transmitting information from one device to another • Common mediums • Radio frequency • Optical connection • Cable connection • Transmitting acoustically • Hiding the information in audio signals, using a speaker as a transmitter and a microphone as a receiver • Characteristics • Low rate, limited range, but benefit from backward compatibility

  4. Auxiliary information Data Hiding Procedures for Acoustic Data Transmission • Requirements • Fast • Capability to hide data into arbitrary signals, without prior knowledge of what they are • Why not using existing schemes? • Robust audio watermarking • Non-negligible delay between the host signal and the secret signal

  5. [Host-blind decoding scenario] Problem Model • x(t): host audio signal • m: message symbol • cm(t): a codeword indexed by m • f: encoding function • y(t) and y’(t): transmitted and received signal • h(t): room impulse response • w(t): noise

  6. Auditory Masking • The effect by which one sound becomes inaudible in the presence of another sound • Frequency masking • Frequency • Sound pressure level • Tone-like or noise-like characteristics • Critical bands • Human perception of frequency are modeled as a set of overlapping band-pass filters • Signals that lie within the same critical band are hard to separate for human ear

  7. Critical Bands Davis Yen Pan, “Digital Audio Compression, “ Digital Technical Journal, Vol. 5 No. 2, Spring 1993

  8. Critical Bands Davis Yen Pan, “Digital Audio Compression, “ Digital Technical Journal, Vol. 5 No. 2, Spring 1993

  9. Frequency-Domain Masking Davis Yen Pan, “Digital Audio Compression, “ Digital Technical Journal, Vol. 5 No. 2, Spring 1993

  10. Real-time Imperceptible Acoustic Data Transmission • Closely related to audio data hiding • Goal of both procedures is to imperceptibly add information to an audio signal • Differences lie in • Objectives • Transmitting arbitrary information vs. simply detecting a hidden signal • Attacks • Room reverberations + D/A and A/D conversions vs. deliberate signal processing manipulations

  11. Existing Audio Data Hiding Schemes • Schemes too slow • LSB embedding • QIM for phase coefficients • Adjusting relation between energies of different frames in time domain • Echo encoding • Simple and real-time • Sensitive to the presence of additional echos • SS data hiding scheme

  12. Sonic Watermarking • R. Tachibana, “Sonic Watermarking”, EURASIP Journal of Applied Signal Processing, no. 13, October 2004

  13. SS Information Embedding Yi[k]=Xi[k]+f(Cm, Xi-1[k]) Psycho-acoustically adjusting A pseudo-random sequence of length N, drawn from U[0,1] distribution

  14. SS Information Embedding (cont.) • The noise in band b never exceeds a pre-defined faction p of the maximum amplitude of Xi-1 in the same critical band • The host signal will mask the added signal within the same band • p controls the tradeoff between the sound quality and code reliability • Code range: [0, 1]  [0, αb] • Assumes that the frequency content of the host signal does not change significantly from one frame to the next f(Cm, Xi-1[k])=α[k]Cm[k] αb[k]=p．max Xi-1, b[k] k belongs to b [Encoding the data in real-time is desirable!]

  15. SS Information Decoding • Received signal • Y’[k]=Y[k]+W[k] • Preprocessing • Whitening to negative effects on cross correlation due to scaling • Y’w,b[k]=Y’bp[k]/max Y’b[k] • Code range changes accordingly • Correlation coefficients • The codeword corresponding to the highest correlation value is selected • Synchronization • Each codeword is added to 3 consecutive frames • N length-N Fourier transforms staring at consecutive samples are taken

  16. Implementation Parameters • Hanning windowed DFT of overlapping 4096-sample frames • # of symbols (codewords): M=2 • the length of codeword N=2048 • encoded into 20 sec pop music signal • White Gaussian noise was added

  17. Experimental Results SNR=10 log10 (Ehost+Ecode)/Enoise

  18. Experimental Results (cont.) • 0: no audible distortion • Barely audible for p<0.2 • 10 listeners

  19. Experimental Results (cont.) Empirical results for p=0.2

  20. Experimental Results (cont.) Sonic watermarking SS

  21. Experimental Results (cont.)

  22. A Localization and Navigation System • Reverberation time • 0.1 sec • First source • Always code 1 in music • Second source • Music only or music with code 2

  23. Experimental Results • Applications • Guiding in a shopping mall or an airport with music played • Weather information, shop discounts or flight times

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