Perceptual image hashing by exploiting a visual attention model along with discrete wavelet transform and discrete cosine transform

Due to the easy accessibility of various image editing tools, image authentication has become a demanding area of research in multimedia technology. An image hash can be used for the authentication of an image that could be sensitive to content changes and invariant to perceptually similar images. The main problem in image hashing is when a system is designed to address the issues of perceptual robustness and discrimination simultaneously, as they are both inverse to each other. To address this problem, we exploited the phase spectrum of the Fourier transform (PFT), a visual attention model, to reveal the saliency map of the pre-processed test image. After that, the first-level discrete wavelet transform (DWT) is applied to obtain the four subbands, out of which the low low (LL) subband, which is located at the top left corner, comprises the lower-frequency components of the DWT and has a quite small variation because of suitable manipulations, similar to JPEG compression, and any malicious tampering in a particular portion of the image results in clear variations in the resultant coefficients of the LL subband. These LL subband coefficients are further exploited using the discrete cosine transform (DCT), as it has an energy compaction property. The energy of the image is gathered into a few DCT coefficients. Among these coefficients, the first one is the DC, which has the main information of the image, and the other ones are AC coefficients, which have the detailed information of the data. The hamming distance is used as a similarity metric among different hashes. Experimental validation is performed, focusing on malicious tampering detection, perceptual robustness, discrimination, hit rate, error rate, and computational complexity, confirming the effectiveness and efficiency of the proposed algorithm. Compared with other eminent algorithms, our proposed algorithm achieves good performance in terms of both hash length and execution time.