Tamper-Resistant Corpus Retrieval Using Perceptual Hashing

Abstract

Conventional corpus retrieval tools are susceptible to malicious attacks, leading to the tampering of corpus resources. To solve this problem, in order to improve the security of corpus retrieval, a tamper resistance retrieval method of corpus based on perceptual Hash computer algorithm is proposed. First, the four dimensional chaotic map is used to encrypt the corpus resources to achieve Tamper resistance processing of the original corpus resources. Then, the robust features of the corpus resources are extracted, and after decomposition and dimensionality reduction, the feature sequence is transformed into a perceptual hash sequence, which facilitates matching the retrieval keywords with the hash sequence in the corpus during retrieval, further avoiding unauthorized modifications. Finally, the perceptual hash sequence is input into the lightweight neural network for training, and a combination of coarse and fine granularity is used to match the perceptual hash corpus in the corpus with the retrieval hash sequence input by the user, obtaining the retrieval results. The experimental results show that the retrieval accuracy of this method is higher than 98%, the tampering rate of the corpus is 0, and the retrieval performance and practical application value are greatly improved.

Keywords

• chaotic mapping
• corpus
• corpus retrieval
• perceived hash algorithm
• tamper resistance