Medical image zero watermarking algorithm based on dual-tree complex wavelet transform, AlexNet and discrete cosine transform

This paper proposed for medical image using zero watermarking, dual-tree complex wavelet transform (DTCWT)-AlexNet and discrete cosine transform (DCT). Furthermore, we utilize the pre-training network AlexNet to extract significant characteristics from medical images. In addition, DTCWT and DCT are employed to convert the complex features, while a perceptual hash function is utilized to cause the feature vector. The original watermark is chaotically scrambled, an exclusive-OR (XOR) gate is inserted into the medical image, and a logical key vector is generated and stored to encrypt the watermark. In general, the medical image under test is used to cutting the important features and create a feature vector using the feature extraction technique. Subsequently, the logical key vector and the feature vector undergo an XOR operation to produce the encrypted watermark. By calculating the normalized correlation (NC) coefficient, the recovered watermark can be utilized to regulate the rights and specifics of the watermark in the medical image after obtaining and decrypting the encrypted watermark with greater than 0.5 NC values. The algorithm's design method tackles the challenge of protecting watermarks from conventional (Gaussian noise, JPEG, median filter) and geometric attacks ( rotation clockwise, anticlockwise, scaling, translation left, translation right, clipping X and Y direction) by incorporating the principles of DTCWT, AlexNet, DCT, and zero watermarking. Encrypting the watermark image through a scrambling process enhances the security of the approach, by ensuring that the watermark should not affect the quality of the image in any way. The research demonstrates that the proposed algorithm is robust and secure for protecting sensitive health information. •AlexNet-based zero-watermarking method extracts medical image features using trained CNN fc8 layer output.•Deep features have undergone transformations with the DTCWT and DCT, a feature vector is generated using a hash function.•The watermark data is recovered by multiple processes using the same technique during the image authentication.•The suggested algorithm is robust and can withstand most geometric and conventional attacks.