A novel image hashing scheme with perceptual robustness using block truncation coding

• A novel perceptual image hashing scheme using block truncation coding is proposed.
• The pre-processing for regularization is first conducted to produce secondary image.
• Reconstruction levels and CSLBP-based matrix are extracted as perceptual features.
• Quantization and PCA operation are used to generate the final compact hash securely.

In this paper, we propose a novel perceptual image hashing scheme based on block truncation coding (BTC). In the proposed scheme, the pre-processing is first applied on original image through bilinear interpolation, Gaussian low-pass filtering, and singular value decomposition (SVD) to construct a secondary image for regularization. Then, BTC is conducted on the secondary image to extract perceptual image features, and the low and high reconstruction levels and the feature matrix of corresponding binary map after the computation of center-symmetrical local binary pattern (CSLBP) are compressed with quantization and data dimensionality reduction of PCA to produce the final compact binary hash. Experimental results demonstrate that the proposed scheme has the satisfactory performances of robustness, anti-collision, and security.