Image watermarking is an important technology to protect image content. However, different areas of images, especially the portrait images, are semantically different, and the important areas cannot be protected discriminately. This vulnerability is particularly evident when faced with AI background replacement, which change the whole portrait image except the important face area, and the embedded watermark will be destroyed. To address this issue, this paper proposes a novel semantic-guided watermarking scheme. We apply a semantic local embedding domain selection method to the watermarking model. By using the local guidance module, the watermark information can be embedded in the specific semantic regions of the image, providing discriminative protection for regions of portrait images, while enhancing the watermark’s imperceptibility and robustness. This scheme can effectively address malicious editing scenario of AI background replacement. Even if all regions of the image with low semantic level are tampered with, the watermark information can still be extracted with high precision. Experimental results demonstrate that the proposed scheme achieves excellent imperceptibility, robustness, and extraction accuracy, with a watermark extraction accuracy of 99.9% even under background replacement, validating its feasibility and practicality.

In recent research on image encryption, many schemes associate the key generation mechanism with the plaintext to resist chosen plaintext attacks. However, when the sender encrypts many images, a large amount of additional data related to the plaintext need to be transmitted, which leads to problems such as high transmission costs, high requirements for key storage space, and complex key management. Therefore, in this paper, we propose a self-sufficient plaintext-related JPEG image encryption scheme based on a unified key (SPJEU). This scheme establishes the connection between the plaintext and the key by selecting the direct current (DC) coefficients in the JPEG image through a unified key. Homomorphic encryption is applied to the selected DC coefficients, allowing plaintext information to be decrypted directly from the ciphertext domain using a specific calculation method. The remaining DC coefficients are encrypted through group diffusion, and the alternating current (AC) coefficients are grouped and permuted based on the run length. Extensive experiments show that our scheme can resist chosen plaintext attacks, avoid transmitting plaintext-related additional data in the communication channel, and simplify key management. This scheme also ensures the security and format compatibility of the ciphertext image, and the file increment after encryption is very small.

In recent years, a large number of personal images have been uploaded to social network platforms, contributing to the formation of image Big Data. These images are vulnerable to security threats, e.g., privacy inference, copyright infringement, and malicious tampering. Many image protection methods have been proposed, e.g., privacy protection methods based on adversarial perturbations, and copyright protection methods based on data hiding. However, these methods can only deal with a single security threat causing the image to suffer from residual threats. Therefore, this paper proposes a comprehensive image protection framework, which generates adversarial examples by embedding meaningful perturbations, achieving image privacy protection while protecting copyright and integrity by data hiding. In this framework, we design a novel high-capacity adversarial data hiding model (HADH) to support adversarially embedding of adequate robust watermarking for copyright protection and fragile watermarking for integrity protection. Experimental results show that HADH achieves a high embedding rate of 3.21 Reed-Solomon bits per pixel (RS-bpp), providing sufficient capacity for copyright and identity verification information. The capacity is even higher than other pure robust steganography schemes. In addition, the privacy protection performance is better than the existing adversarial attack-based privacy protection methods.

The advent of the Internet of Medical Things (IoMT) has greatly fastened the digitization of current medical institutions. Mobile crowdsourcing is an effective strategy for health data collection in IoMT environments to overcome the data-scarce problem. However, due to the openness of IoMT networks, users’ identities and sensitive data may be leaked during IoMT crowdsourcing. Meanwhile, IoMT crowdsourcing may introduce low-quality data from unreliable participants. Multireceiver signcryption is a promising mechanism to ensure confidentiality and authenticity in an efficient manner. However, existing multireceiver signcryptions cannot fully meet the needs of IoMT crowdsourcing in terms of privacy protection, on-demand participation, and malicious behavior resistance. In this article, we integrate attribute-based credentials with multireceiver encryption and propose a novel multireceiver conditional anonymous signcryption (MCAS) scheme for crowdsourced IoMT environments to address the above challenge. Specifically, conditional anonymous authentication with selective attribute disclosure is achieved, thereby allowing a worker to self-disclose some attributes and anonymously certify his/her crowdsourcing qualifications, and also achieving the traceability of malicious behaviors. Meanwhile, one-to-many secure data sharing with outsourced data signcryption and unsigncryption is realized to prevent the leakage of sensitive IoMT data and mitigate the computational burden of power-limited mobile devices. Moreover, rigorous security analysis demonstrates that our MCAS scheme achieves the expected properties, i.e., confidentiality, anonymity, fine-grained authentication, traceability, and nonrepudiation. Extensive experimental results show that our MCAS outperforms state-of-the-art schemes, demonstrating our scheme’s appropriateness for IoMT crowdsourcing.

In recent years, the uploading of massive personal images has increased the security risks, mainly including privacy breaches and copyright infringement. Adversarial examples provide a novel solution for protecting image privacy, as they can evade the detection by deep neural network (DNN)-based recognizers. However, the perturbations in the adversarial examples typically meaningless and therefore cannot be extracted as traceable information to support copyright protection. In this paper, we designed a dual protection scheme for image privacy and copyright via traceable adversarial examples. Specifically, a traceable adversarial model is proposed, which can be used to embed the invisible copyright information into images for copyright protection while fooling DNN-based recognizers for privacy protection. Inspired by the training method of generative adversarial networks (GANs), a new dynamic adversarial training strategy is designed, which allows our model for achieving stable multi-objective learning. Experimental results show that our scheme is exceptionally robust in the face of a variety of noise conditions and image processing methods, while exhibiting good model migration and defense robustness.

This paper proposes a stochastic dominant cognitive experience-guided learning framework for particle swarm optimization (SDCEGPSO) to enhance its search ability in complex environment. Specifically, different from classical PSOs, SDCEGPSO randomly selects dominant cognitive experiences to guide the learning of particles. To this end, the cognitive experiences of all particles, namely their personal best positions, are sorted from the best to the worst. Then, each particle randomly chooses a personal best position better than its own to learn. For the cognitive experience selection, this paper designs three selection methods, namely the random selection, the roulette wheel selection, and the tournament selection. With this learning framework, particles have diverse guiding exemplars to learn from and thus high search diversity is expectedly maintained. Experiments conducted on the 50-D and 100-D CEC2014 problem suite have verified the effectiveness of SDCEGPSO. Compared with the classical global PSO (GPSO) and local PSO (LPSO), SDCEGPSO with the three selection schemes achieve significantly better performance. Besides, among the three selection schemes, the binary tournament selection is the most effective one to help SDCEGPSO solve optimization problems.

DNA sharing and querying of personal genomic sequences have becoming more critical than ever with the accumulation of large-scale biomedical data. Quantitative genomic studies heavily rely on multi-source DNA datasets from different institutions, who are reluctant to share the data via cloud centers. The high sensitivity of DNA has compelled the government to restrict its acquisition and usage. One potential solution to tackle the issue is designing a secure query strategy on the encrypted DNA datasets. However, the popular secure DNA query schemes remain defective in verifiability, reliability, and NDA privacy. To relieve the issues, we propose a DNA searchable encryption method named EncGD to achieve verifiable privacy-preserving queries and reliable updates on multi-source dynamic DNA datasets. Specifically, the proposed scheme EncGD is designed by the plaintext-related permutation and substitution primitives, which can enhance the DNA privacy due to the chosen-plaintext attack (CPA)-resist ability. Furthermore, the method realizes the verifiability and reliability by adding known information to block the malicious behaviors of clouds. Experimental results demonstrate the superior efficiency of the proposed method comparing with the two state-of-the-art schemes in terms of time and space costs.

Double random-phase encoding- (DRPE-) based compressive sensing (CS) systems support image authentication for noisy images. When extending such systems to resource-constrained applications, how to ensure the authentication strength for noisy images becomes challenging. To tackle the issue, an efficient and secure image authentication scheme is presented. The phase information of the plain image is generated using DRPE and quantized into a binary image as the authentication information. Meanwhile, a sparser error matrix generated by the same plain image and vector quantization (VQ) image works as the input of CS. The authentication information and VQ indexes are self-hidden into the quantized measurements to construct the combined image. Then, it is permutated and diffused with the chaotic sequences generated from a modified Henon map. After decryption at the receiver side, the verifier can implement the blind authentication between the noisy decoded image and the reconstructed image. Supported by the detailed numerical simulations and theoretical analyses, the DRPE-CSVQ exhibits more powerful compression and authentication capability than its counterpart.

Most anti-collusion audio fingerprinting schemes are aiming at finding colluders from the illegal redistributed audio copies. However, the loss caused by the redistributed versions is inevitable. In this letter, a novel fingerprinting scheme is proposed to eliminate the motivation of collusion attack. The audio signal is transformed to the frequency domain by the Fourier transform, and the coefficients in frequency domain are reversed in different degrees according to the fingerprint sequence. Different from other fingerprinting schemes, the coefficients of the host media are excessively modified by the proposed method in order to reduce the quality of the colluded version significantly, but the imperceptibility is well preserved. Experiments show that the colluded audio cannot be reused because of the poor quality. In addition, the proposed method can also resist other common attacks. Various kinds of copyright risks and losses caused by the illegal redistribution are effectively avoided, which is significant for protecting the copyright of audio.

Secret sharing is an important component of cryptography protocols and has a wide range of practical applications. However, the existing secret sharing schemes cannot apply to computationally weak devices and cannot efficiently guarantee fairness. In this study, a novel outsourcing secret sharing scheme is proposed. In the setting of outsourcing secret sharing, clients only need a small amount of decryption and verification operations, while the expensive reconstruction computation and verifiable computation can be outsourced to cloud service providers (CSP). The scheme does not require complex interactive argument or zero‐knowledge proof. The malicious behaviour of clients and CSP can be detected in time. Moreover, the CSP cannot get any useful information about the secret, and it is fair for every client to obtain the secret. At the end of this study, the authors prove the security of the proposed scheme and compare it with other secret sharing schemes.

A novel method of meaningful image encryption is proposed in this paper. A secret image is encrypted into another meaningful image using the algorithm of reversible data hiding (RDH). High covertness can be ensured during the communication, and the possibility of being attacked of the secret image would be reduced to a very low level. The key innovation of the proposed method is that RDH is applied to compressive sensing (CS) domain, which brings a variety of benefits in terms of image sampling, communication and security. The secret image after preliminary encryption is embedded into the sparse representation coefficients of the host image with the help of the dictionary. The embedding rate could reach 2 bpp, which is significantly higher than those of other state-of-art schemes. In addition, the computational complexity of receiver is reduced. Simulations verify our proposal.

The first joint fingerprinting and decryption (JFD) for vector quantization (VQ) images addressed the problem that the decrypted multimedia data may be redistributed from authorized customers to unauthorized customers. The scheme also caused conventional JFD methods to be equipped with a special ability to resist noise interference. Till now, some existing schemes related have been proposed to protect the multimedia content and distribution, but these schemes failed to tackle several problems existing in the original JFD scheme based on VQ image, including high transmission cost and severe fingerprinted image distortion. In this paper, we propose a novel JFD method by combining a weight-sum function with fingerprinting embedding and extraction for VQ images. Under the combination, the visual quality of the fingerprinted image is further improved; also the fingerprint extraction implements a blind extraction process. Experiments and analyses demonstrate the feasibility of the proposed method.

Abstract The preservation of high peak signal‐to‐noise ratio of the watermarked image is required in some applications. The chaos‐based fragile watermarking proposed by Rawat et al. in 2011 can meet this requirement, for only one LSB plane of the original image is modified by the watermark data. However, there are three drawbacks in the original scheme: (i) insecurity, the host image can be tampered without being detected; (ii) the detected tampered area is incomplete; and (iii) the most important thing is that the tampered area is unrecoverable. Enlightened by the work of Rawat et al., we propose a new recoverable chaos‐based fragile watermarking scheme, in which all of the drawbacks in the original scheme have been overcome, while the size of the watermark is unchanged, therefore the high peak signal‐to‐noise ratio characteristic can be preserved. The chaotic map is used to ensure the security; the idea of block dividing is adopted to improve the tamper detection and to make storage space for the recovery data embedding; and the border matching recovery based on vector quantization is proposed to improve the recovery result of the target image. Experiments show that our method is secure and recoverable, and the recovery ability is extremely satisfactory. Copyright © 2016 John Wiley & Sons, Ltd.