An encrypted speech retrieval algorithm based on Chirp-Z transform and perceptual hashing second feature extraction

A Classification Retrieval Method for Encrypted Speech Based on Deep Neural Network and Deep Hashing

“…Compared with the [25], the proposed method adopts the "two-stage" classification retrieval strategy, which improves the efficiency of hash matching to a certain extent. Compared with the [3], the retrieval efficiency of the proposed method is slightly lower than that of the [3], this is because the [3] first classifies the speech data, and then compresses the generated hash sequence through the stroke length compression technology, thereby shortening the matching time, however, the final perceptual hash sequence generation process is more complicated than the deep hashing construction process in this paper, and its test speech length is shorter than this paper. Therefore, the CNN/CRNN coding model proposed in this paper has better retrieval efficiency and fully meets the retrieval requirements of the encrypted speech retrieval system.…”

“…For example, He et al [2] proposed a retrieval algorithm based on syllable-level perceptual hashing, which uses the posterior probability feature of speech segment model to generate perceptual hash sequence, and realizes the spoken retrieval based on encrypted speech. Zhang et al [3] proposed an encrypted speech retrieval algorithm based on Chirp-Z transform and perceptual hashing second feature extraction, through Chirp-Z transform and sparse matrix to extract the perceptual hash digest, and encrypt the speech file according to the m sequence, which has good retrieval performance for noisy speech. Zhao et al [4] proposed an encrypted speech retrieval method based on perceptual hashing, which uses multifractal features and piecewise aggregation approximation to generate perceptual hash sequences, which has good discriminability and robustness.…”

“…In addition, in order to test the robustness of the deep hashing binary code generated by the two proposed coding models (CNN/CRNN), this paper uses the deep hashing binary code generated by the speech data after the four CPOs operations, such as amplitude reduction (-3dB), amplitude increase (+3dB), MP3 compression (MP3) and resampling 8-16kbps (R.Q) for evaluation and analysis, and compared with the perceptual hash method [2]- [3] and the deep hashing method [25] commonly used in the field of encrypted speech retrieval. Table 3 shows the comparison results of recall rate (R) and precision rate (P).…”

“…It can be seen from Table 3, the proposed two deep hashing coding models (CNN/CRNN) still have well recall rate and precision rate under four content preserving operations, which shows that the proposed method has good robustness and collision resistance. Compared with the perceptual hashing method [2,3] commonly used in the field of encrypted speech retrieval, the performance of the proposed method is slightly better, because the proposed method uses the deep neural network model (CNN/CRNN) to extract deep semantic features, which has strong semantic representation. At the same time, compared with the deep hashing method in [25], the performance of the proposed method is basically the same, in most cases, the test results of recall rate and precision rate have reached 100%.…”

“…In summary, the proposed "two-stage" classification retrieval method is more efficient than the traditional traversal retrieval method, and with the increase of feature hash length, the efficiency improvement is more obvious. In addition, in order to evaluate the retrieval efficiency of the proposed method, the experiment compares the average retrieval time (feature learning + retrieval matching) between the proposed method and the method in [2]- [3], [25], in the proposed method, the CNN/CRNN coding model with the length of 384 bits is used for the experiment. Table 5 shows the comparison results of retrieval efficiency between the proposed method and the existing methods.…”