High-capacity reversible data hiding using quotient multi pixel value differencing scheme in encrypted images by fuzzy based encryption

The Reversible data hiding (RDH) approach can retrieve the original image from the marked image without any distortion. RDH in encrypted images is an approach that hides extra information into the ciphertext using a skill of recovering the actual data losslessly. To guarantee reversibility for addressing the information redundancy drawback, the cover image pixels are copied into two images. This paper presents a high capacity RDH scheme in encrypted images using fuzzy-based encryption. Initially, the texture classification is processed by a convolutional neural network (CNN) to classify the dense and transparent region. It automatically identifies the significant features without any individual supervision. Then, the plain text encryption is activated by the fuzzy group teaching with infinite elliptic curve (FGTIE) method. To overcome the demerit of FCM, the GTA is hybrid with FCM approach and the encryption is processed by the IE method. Next, a new embedding approach is used to enhance the embedding capacity, namely quotient multi-pixel value differencing (QMPVD). In order to obtain the higher PSNR and payload, the multi-pixel differencing is hybrid with the quotient value differencing. Finally, the original data is extracted and recovered with good quality and high capacity. The performances are evaluated using several performance metrics such as PSNR, SSIM, BER, MSE, embedding capacity/payload, sensitivity, specificity, tampering ratio, correlation coefficient, number of pixel change rate and unified average changing intensity. The performance of PSNR and capacity is compared with existing approaches named Encrypted image-based RDH with Paillier cryptosystem (EIRDH-PC), EIRDH with Redundancy Transfer (EIRDH-RT) and EIRDH with pixel value ordering (EIRDH-PVO). The performance is calculated for three groups of images such as the brain, lungs and abdomen. The implementation results show that the introduced model attained better performance compared to existing approaches in terms of PSNR and capacity. Besides, the proposed approach achieved the merits of no pixel expansion, lossless and alternative order recovery.