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Problem: Digital devices are becoming increasingly powerful and smart, which is improving quality of life, but presents new challenges to privacy protection. Visual cryptographic schemes provide data sharing privacy, but have drawbacks such as extra storage space, lossy secret images, and the need to store permutation keys.

Aim: This paper proposes a light-weight visual sign-cryptography scheme based on optimal key generation to address the disadvantages of existing visual cryptographic schemes and improve the security, sharing quality, and time consumption of multisecret images.

Methods: The proposed light-weight visual sign-cryptography (LW-VSC) scheme consists of three processes: band separation, shares generation, and signcryption/un-signcryption. The process of separation and shares generation is done by an existing method. The multiple shares of the secret images are then encrypted/decrypted using light-weight sign-cryptography. The proposed scheme uses a novel harpy eagle search optimization (HESO) algorithm to generate optimal keys for both the encrypt/decrypt processes.

Results: Simulation results and comparative analysis showed the proposed scheme is more secure and requires less storage space, with faster encryption/decryption and improved key generation quality.

Conclusion: The proposed light-weight visual sign-cryptography scheme based on optimal key generation is a promising approach to enhance security and improve data sharing quality. The HESO algorithm shows promise in improving the quality of key generation, providing better privacy protection in the face of increasingly powerful digital devices.

In this paper, we propose a neural-network approach for visual authorization, which is an application of visual cryptography (VC). The scheme contains a key-share and a set of user-shares. The administrator owns the key-share, and each user owns a user-share issued by the administrator from the user-share set. The shares in the user-share set are visually indistinguishable, i.e. they have the same pictorial meaning. However, the stacking of the key-share with different user-shares will reveal significantly different images. Therefore, the administrator (in fact, only the administrator) can visually recognize the authority assigned to a particular user by viewing the information appearing in the superposed image of key-share and user-share.

This approach is completely different from traditional VC approaches. The salient features include: (i) the access schemes are described using a set of graytone images, and (ii) the codebooks to fulfil them are not required; and (iii) the size of share images is the same as the size of target image.

A visual secret sharing (VSS) scheme is intended to share secret information in a group to avoid potential treat of interruption and modification. In this paper, we present a novel VSS scheme based on the improved local binary pattern (LBP) operator. It makes full use of local contrast features of LBP for concealing secret image data into different image shares, which can be used to recover the secret easily and exactly. By varying LBP extensions, we can design various kinds of VSS schemes for sharing secret information. Compared to the currently available VSS algorithms, the proposed scheme demonstrates better randomness in shares with less pixel expansion and exact determination in reconstruction with lower computational cost.

Many shares are generated from the secret images that are illogical containing certain message within them in visual cryptography. When all shares are piled jointly, they tend to expose the secret of the image. The multiple shares are used to transfer the secret image by using the encryption and decryption process by means of the elliptic curve cryptography (ECC) technique. In ECC method, the public key is randomly generated in the encryption process and decryption process, the private key ($H$) is generated by utilizing the optimization technique and for evaluating the performance of the optimization by using the peak signal to noise ratio (PSNR). From the test results, the PSNR has been exposed to be 65.73057, also the mean square error (MSE) value is 0.017367 and the correlation coefficient (CC) is 1 for the decrypted image without any distortion of the original image and the optimal PSNR value is attained using the cuckoo search (CS) algorithm when compared with the existing works.

Visual Cryptography (VC) is a process employed for the maintenance of secret information by hiding the secret messages that are embedded within the images. Typically, an image is partitioned into a number of shares that are stacked over one another in order to reconstruct back the original image accurately. The major limitation that existed in the traditional VC techniques is pixel expansion, in which pixel expansion is replaced with a number of sub-pixels in individual share, which causes a considerable impact on the contrast and resolution of the image that further gradually decreases the quality of the image. VC is named for its essential characteristics, such as transmitting the images with two or more shares with an equal number of black pixels and color pixel distribution. The secret message can be decrypted using Human Visual System (HVS). In this paper, 50 research papers are reviewed based on various classification algorithms, which are effectively used for the VC technique. The classification algorithms are categorized into three types, namely, meta-heuristic, heuristic, and evolutionary, and the research issues and challenges confronted by the existing techniques are reported in this survey. Moreover, the analysis is done based on the existing research works by considering the classification algorithms, tools, and evaluation metrics.

As education field has become online due to covid in both schools and colleges, e-learning security has become an important issue. An e-Learning framework provides a collection of online services that are helpful for the learners, resource persons and others who are involved in enhancing the management and delivery of education to all sections of people. The two most important aspects of an e-Learning system are better search of learning resources and the secure authentication between the learner and the trainer. This chapter introduces two novel methods: (i) optimization of Learning Object (LO) search based on learners’ characteristics, (ii) secure authentication of trainers and learners using visual cryptography. Storage and delivery of optimal resources that are well suited for individual learner is always a challenging task. To find the best learning objects, an enhanced attribute-based Ant Colony Optimization (ACO) algorithm that provides flexibility for the learners based on learner characteristics is proposed. A novel visual cryptography-based technique with kite-based partition technique is designed to perform file sharing and blockchain-based secure authentication and verification of valid learners is proposed for the framework. Several measures like match ratio, relevancy factor, and heuristic values show the efficiency of the proposed ACO search technique in the context of an e-Learning framework.

By chaotic encryption system and introducing the trusted third party (TTP), in this paper, an anti-cheating visual cryptography scheme (VCS) is proposed. The scheme solved the problem of dishonest participants and improved the security of chaotic encryption system. Simulation results and analysis show that the recovery image is acceptable, the system can detect the cheating in participants effectively and with high security.

With the rapid popularity of the 3D-TV, it is becoming more and more significant to protect the copyright in 3D image. To cater this need, we proposed a novel digital watermarking of 3D image scheme based on visual cryptography. Anaglyph 3D image is separated into left eye view image, right eye view image and the depth view image, while the watermark is pretreated based on the visual cryptography. Watermarking content protection is done with the help of Discrete Wavelet Transform (DWT) on anaglyph 3D images.