Content-Dependent Watermarking Scheme in Compressed Speech With Identifying Manner and Location of Attacks

  • Authors:
  • O. T.-C. Chen;Chia-Hsiung Liu

  • Affiliations:
  • Nat. Chung Cheng Univ., Chia-Yi;-

  • Venue:
  • IEEE Transactions on Audio, Speech, and Language Processing
  • Year:
  • 2007

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Abstract

As speech compression technologies have advanced, digital recording devices have become increasingly popular. However, data formats used in popular speech codecs are known a priori, such that compressed data can be modified easily via insertion, deletion, and replacement. This work proposes a content-dependent watermarking scheme suitable for codebook-excited linear prediction (CELP)-based speech codec that ensures the integrity of compressed speech data. Speech data are initially partitioned into many groups, each of which includes multiple speech frames. The watermark embedded in each frame is then generated according to the line spectrum frequency (LSF) feature in the current frame, the pitch extracted from the succeeding frame, the watermark embedded in the preceding frame, and the group index which is determined by the location of the current frame. Finally, some of the least significant bits (LSBs) of the indices indicating the excitation pulse positions or excitation vectors are substituted for the watermark. Conventional watermarking schemes can only detect whether compressed speech data are intact. They cannot determine where compressed speech data are altered by insertion, deletion, or replacement, whereas the proposed scheme can. Experiments established that the proposed scheme used in the G.723.1 6.3 kb/s speech codecs embeds 12 bits in each compressed speech frame with 189 bits, and only decreases the perceptual evaluation of speech quality (PESQ) by 0.11. Additionally, its accuracy in detecting the locations of attacked frames is very high, with only two normal frames mistaken as attacked frames. Therefore, the proposed watermarking scheme effectively ensures the integrity of compressed speech data.