In a (k, n)-threshold secret image sharing (SIS) scheme, a secret image is encoded intonshadow images and distributed to the corresponding participants, enabling lossless reconstruction with anykcorrect shadow images. This inherent fault tolerance allows up ton–kshadow images to be lost or corrupted. However, in real-world scenarios, all shadow images are susceptible to malicious tampering, cropping, or noise during transmission and storage, making it difficult to guarantee the availability ofkintact shadows. Robust secret image sharing (RSIS) schemes have been proposed to address this issue, yet existing methods often suffer significant degradation in reconstruction quality as the attack strength increases, revealing limitations in their robustness. To address these issues, we propose an RSIS scheme against malicious shadow images by Reusing Polynomial Coefficients with hash function (RSIS-RPC), which provides both malicious shadow detection and error correction capabilities. The correction capability improves with the degree of coefficient reuse, where greater reuse provides stronger resilience to pixel corruption. However, as more coefficients are reused, the size of the generated shadow images increases correspondingly, resulting in higher storage requirements. This trade-off between robustness and efficiency makes the proposed scheme adaptable to diverse application scenarios requiring secure and resilient image sharing. Experimental results and analyses demonstrate that the proposed scheme achieves superior robustness compared to existing schemes.

The advent of deep learning has accelerated the development of facial manipulation techniques, particularly face-swapping and face attribute editing, raising serious concerns about privacy and identity-related misuse. Existing proactive defense methods predominantly target attribute editing and often generalize poorly to face-swapping models, making it difficult to provide effective protection across both tasks within a unified framework. To bridge this gap, we propose a generalized defense framework, Epoch-Adaptive Adversarial Perturbation Optimization (EA-APO). Specifically, EA-APO introduces a proactive defense mechanism that establishes optimal adversarial paths by optimizing perturbations on a white-box surrogate model to enhance adversarial transferability, and applies the resulting perturbations to source face images to disrupt both face swapping and face attribute editing, even against previously unseen target models in black-box settings. This approach mitigates identity feature tampering while adapting to changes in visual attributes and preserving high-quality adversarial examples. Experimental results show the generalization of our method across multiple face-swapping and attribute-editing models, including commercial ones, while also maintaining strong defense under various common post-processing operations and real-world social media transmission conditions, underscoring its potential for real-world deployment.

To ensure the authenticity and validity of Secret Image Sharing (SIS) schemes, Verifiable Secret Image Sharing (VSIS) methods have been proposed. However, existing VSIS schemes are vulnerable to attacks from Dishonest Participant (DP). The dishonest participants may still accessk-1 valid shares when they are identified, enabling them to recover the secret image. To address this issue, a VSIS based on polynomial interpolation for resisting Dishonest Participant Attacks (VSIS-RDPA) is proposed. Unlike traditional SIS schemes, where secret pixels are used as polynomial coefficients, our scheme treats secret pixel values as function values of the polynomial. On the contrary, the secret key and secret pixel values serve as inputs to reconstruct ak-2-degree polynomial using Lagrange interpolation, enhancing security against malicious participants. Authentication parameters generated by a hash function are combined with thek-2-degree polynomial to form a completek-1-degree polynomial, which subsequently is utilized to generate shares. On the receiver end, thek-1-degree polynomial is first reconstructed, and the authentication parameters generated by the hash function are compared with those obtained from the reconstructed polynomial. If the two sets of values match, the authentication is successful, allowing for the recovery of the secret image. In addition, a modified authentication phase with ECC is also proposed to enhance the robustness of authentication. Experimental results and analysis demonstrate that the proposed schemes can resist DP attacks and ensure efficiency, verifiability, and security.

Forged videos are often subjected to double compression. When a forger maliciously or unintentionally increases the video's bitrate during re-encoding, the resulting videos are termed fake bitrate videos. Detecting these videos offers a generalized approach for efficiently identifying potentially forged content within large datasets. However, previous research has largely focused on video-level detection of fully fake bitrate videos, where an entire video is re-encoded at a higher bitrate after content modification or the creation of fake high-definition (HD) footage. In practice, a skilled forger may adjust the bitrate of only specific video segments, generating partial fake bitrate videos-a common manipulation in tampering processes like video splicing. Existing methods face difficulties in detecting such partial modifications at the frame level and in pinpointing the manipulated segments. Our study addresses this gap by introducing a novel frame-level detection approach, which significantly enhances forensic precision. We simultaneously account for two types of abnormal frames arising from re-encoding and bitrate escalation and, for the first time, define fake bitrate video detection as a triple classification problem. To meet the challenges of this task, we extract anomalous bitrate-compression traces that capture subtle differences among the three frame types. Additionally, we propose the Trident Transformer Network (TTNet), a model designed to effectively integrate and learn high-frequency information within the encoding domain. Our approach achieves substantial improvements in accuracy, surpassing state-of-the-art methods by 3.62% and 11.95% in video-level and frame-level detection scenarios, respectively.

Perceptual hashing has garnered significant attention for its wide-ranging applications in image retrieval and authentication domains. However, existing algorithms often struggle to detect subtle manipulations confined to small regions of an image. In this paper, we introduce a novel framework, Manipulation-Aware Deep Perceptual Hashing (MADPHash), which leverages feature consistency to enhance sensitivity to such subtle manipulations. MADPHash explicitly treats tampered images as a distinct category, incorporates a tampering detection objective into the perceptual hash generation process, and employs a Consistency Constraint Module to amplify discrepancies between tampered and untampered regions. Comprehensive experiments conducted on five benchmark datasets demonstrate that MADPHash significantly improves the detection of subtle manipulations while maintaining robustness against content-preserving transformations, outperforming several state-of-the-art perceptual hashing methods.

The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinformation, deepfakes, and copyright infringement. Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time pre-processing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of pre-processing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI detection.

The (k,n)-threshold Secret Image Sharing (SIS) is a naturally fault-tolerant technique for image privacy protection. A secret image is processed through secret sharing to generatenshadow images, which are then distributed tondifferent recipients. During the recovery phase, the complete secret image can be reconstructed by anykout ofnshadow images. Although (k,n)-threshold SIS itself allows for the loss of up to$n-k$shadow images, if there are pixel errors in the remainingkshadow images, the recovery of the secret image will be declared a failure. Therefore, Robust Secret Image Sharing (RSIS) has been proposed to address the issue. However, the current proposed RSIS schemes only demonstrated limited robustness against noise attacks. This paper presents a novelk-consistency-based RSIS scheme to resist malicious attacks, including noise, JPEG compression, tampering, and cropping. In the sharing phase, a dual-SIS mechanism is first designed to perform two rounds of secret sharing on the secret image. In the recovery phase, high-quality secret image can be reconstructed based onk-consistency after attacking. The experimental results demonstrated that our scheme not only provides comprehensive robustness but also allows for flexible adjustment of shadow images’ sizes, ensuring both security and efficiency during image sharing.

The purpose of deep image hiding is to embed the secret image imperceptibly in an equally sized cover image, and then recover the secret image almost perfectly at the receiver end. How to improve the quality of recovered secret images while ensuring the visual quality and security of stego images is an important challenge. In order to address this issue, a novel deep image hiding framework called EFCA-DIH (Edge Features and Coordinate Attention-based Invertible Network for Deep Image Hiding) is proposed. Firstly, an important feature extraction module is proposed to extract wavelet sub-band features coupled with edge features, thereby hiding the secret image better in the cover image. Secondly, a coordinate attention mechanism is introduced into the invertible hidden module to embed the secret information in the complex texture regions. Finally, an edge feature loss function is designed to constrain the edge differences between the stego image and the cover image, and between the secret image and the recovered secret image, thereby improving the quality of both the stego image and the recovered secret image. Experimental results have demonstrated that our EFCA-DIH significantly improves the quality of recovered secret images compared with other state-of-the-art methods, while maintaining the visual quality and security of stego images.

Reversible data hiding (RDH) is one special type of data hiding, and is widely used for many intended applications. Moreover, to protect the cover image, RDH in encrypted image (RDHEI) schemes are accordingly proposed. In RDHEI, if the marked image is lost, the cover image and the secret data cannot be restored. To address the issue of losing sub marked images, RDH in shared image (RDHSI) by secret sharing (SS) is proposed to achieve fault-tolerance. Recently, an anonymous application scenario using RDHSI is introduced, on which multiple data hiders in RDHSI serve as reviewers in a committee. When the number of agreement votes is above the threshold of SS, the application is approved. However, there are weaknesses in this application framework: (i) reviewers cannot provide equal right to vote (namely, they cannot cast “Yes” and “No” votes, respectively), and (ii) anonymous submission is not really achieved. In the paper, we propose a new RDHSI to allow data hiders can hide approved sub data or disapproved sub data into sub images. Therefore, how to perform recovery and extraction from n marked images (some embedded with “Yes” votes and some with “No” votes) should be carefully designed. Based on the consistency property of polynomial interpolation, we conduct verification, recovery and extraction algorithms from n marked images. In addition, we use Hamming codewords to represent pixel difference instead of directly hiding pixel difference for reversibility, and this improvement also improves the embedding capacity. Compare with the current anonymous application scheme using RDHSI, the embedding rate in this paper can reach 3.5 bits per pixel (bpp), which is an improvement of 2 bpp. Thus, it is more suitable for the anonymous submission of application framework.

Recently, Reversible Data Hiding in Encrypted Images with Multiple Hiders (RDHEI-MH) has attracted the attention of researchers, as it can satisfy the requirements of multiparty embedding. In this paper, a universal framework for RDHEI-MH is proposed, in which most of existing RDH algorithms in the plaintext domain (including Data Expansion (DE)-based and Histogram Shifting (HS)-based algorithms) can be applied in encrypted images without performance loss. That cannot be achieved by current existing schemes. The framework contains three entities: image owner, multiple hiders, and image receiver. The cover image is shared by Additive Secret Sharing (ASS) at the image owner’s side, and then each image share is sent to the corresponding hider. Based on the Secure Multiparty Computation (MPC) protocols specially designed for RDH operations, the hiders can securely perform computation on shares to embed additional data. After receiving all the marked image shares, the image receiver can reconstruct the marked image, and further extract the data. Compared with the previous RDHEI-MH schemes, the proposed scheme achieves full-separability, i.e., the extraction and decryption operations are commutative. The experimental results and theoretical analyses demonstrate that the proposed scheme has high visual quality of the reconstructed image and high embedding capacity, while its security is proved.

With the growing popularity of high-resolution (HR) video and the continuous growth of network bandwidth, the challenge of object removal detection in HR videos has attracted significant attention. Expert forgers leverage the rich detail in HR videos for meticulous pixel manipulation and apply sophisticated postprocessing techniques to hide high-frequency artifacts, thereby making forgery detection and localization more difficult when existing schemes are used. Additionally, the end-to-end framework simplifies the detection and localization process, which has not been considered in previous work. To solve the above issues, a spatiotemporal encoder−decoder network (SEDN) is proposed for end-to-end object removal forgery detection in HR videos. In the SEDN, a new model composed of a 3D asymmetric dual-stream network (3D-ADSN) and Transformer is proposed. The 3D-ADSN is utilized as the encoder, which fully integrates the high-frequency and low-frequency spatiotemporal information of videos. Transformer is utilized as the decoder to capture the global structure spatiotemporal information of the long-range feature sequence obtained by the encoder. This network combination successfully achieves simultaneous detection in the temporal and spatial domains without any additional postprocessing calculations. The experimental results demonstrate the better performance of the SEDN at different resolutions.

Secret sharing (SS) is a threshold technology that shares a secret value by generating and distributing${n}$shares in the way that a set of any${k}$shares can recover the secret. On the other hand, blockchain is a decentralized system establishing a secure data structure for storing data in the form of a hash chain. Combining SS and blockchain is definitely a promising paradigm in the data security field. Recently, the fast healthcare interoperability resources (FHIRs) standard is defined for medical data exchange across various medical institutions. In this article, we combine SS, ElGamal’s encryption/decryption, and blockchain to propose a multihospital FHIR database authorization (MFDA). To achieve a secure MFDA, the group-based SS (GSS) and the notion using GSS on exponent (referred to as EGSS) are proposed. The secrets of GSS and EGSS have the discrete logarithm problem (DLP) relation, on which the secrets can be used as ElGamal’s private key and public key, respectively. Also, their shares will have DLP relation. By uploading public shares of EGSS on blockchain and using private shares of GSS in a private network, we can recover ElGamal’s public key from a deployed smart contract on blockchain and recover ElGamal’s private key from a qualified set. Meantime, when recovering private key, the private shares of GSS can be authenticated by public shares on blockchain to check tampering and corrupted of private shares during the communication in a private network. Theorems, examples, and experiments demonstrate that the proposed MFDA has efficient and secure database authorization.

The rapid development of cloud computing probably benefits many of us while the privacy risks brought by semi-honest cloud servers have aroused the attention of more and more people and legislatures. In the last two decades, plenty of works seek to outsource various specific tasks to servers while ensuring the security of private data. The tasks to be outsourced are countless; however, the computations involved are similar. In this article, we construct a series of novel protocols that support the secure computation of various functions on numbers (e.g., the basic elementary functions) and matrices (e.g., the calculation of eigenvectors and eigenvalues) on arbitrary$n\geq 2$servers. All protocols only require constant rounds of interactions and achieve low computation complexity. Moreover, the proposed$n$-party protocols ensure the security of private data even though$n-1$servers collude. The convolutional neural network models are utilized as the case studies to verify the protocols. The theoretical analysis and experimental results demonstrate the correctness, efficiency, and security of the proposed protocols.

Reversible data hiding is widely utilized for secure communication and copyright protection. Recently, to improve embedding capacity and visual quality of stego-images, some Partial Reversible Data Hiding (PRDH) schemes are proposed. But these schemes are over the plaintext domain. To protect the privacy of the cover image, Reversible Data Hiding in Encrypted Images (RDHEI) techniques are preferred. In addition, the full separability of cover image reconstruction and data restoration is also an important characteristic that cannot be achieved by most RDHEI schemes. To solve the issues, a partial and a complete Reversible Data Hiding in Shared Images with Separate Cover Image Reconstruction and Secret Extraction (RDHSI-SRE) are proposed in this paper. In the proposed schemes, the secret data is divided by Secret Sharing (SS). Then, the marked shared images are generated based on the proposed modify-and-recalculate strategy. The receiver can extract embedded data and reconstruct the image separably usingk-out-of-nmarked shared images. In the embedding phase of partial RDHSI-SRE (PRDHSI-SRE), the pixel values are modified according to the proposed Minimizing-Square-Errors Strategy to achieve high visual quality, and the complete RDHSI-SRE (CRDHSI-SRE) embeds data by modifying random coefficients to achieve reversibility. The experimental results and theoretical analyses demonstrate that the proposed schemes have a high embedding performance. Most importantly, the proposed schemes are fault-tolerant and completely separable.

Invertible secret image sharing with authentication (ISISA) distributes comprehensible stego images generated from secret images and cover images to involved participants. The secret image and cover image can be correctly recovered after authentication. However, existing ISISA schemes suffer from issues such as a single kind of image, limited embedding capacity, poor visual quality and a lack of authentication capability. To address these issues, this paper provides a novel invertible secret image sharing scheme with authentication for embedding color palette images into true color images. In this scheme, the pixels of the palette secret image and the bits of the cover pixel are used as coefficients of the polynomial. Share is embedded into a true color cover image to generate an intermediate stego image. Authentication information is then derived from the intermediate stego image and hidden in the cover image. The final stego images that resemble the cover image are obtained and sent to authorized participants. At the receiver end, once k stego images are verified, the secret image and cover image can be losslessly recovered for a (k, n)-threshold scheme. The experimental results and theoretical analysis demonstrated the superiority and practicality of the scheme.

Image copy-move forgery detection (CMFD) has become a challenging problem due to increasingly powerful editing software that makes forged images increasingly realistic. Existing algorithms that directly connect multiple scales of features in the encoder part may not effectively aggregate contextual information, resulting in poor performance. In this paper, an end-to-end context multiscale cross-fusion network (CMCF-Net) is proposed to detect image copy-move forgery. The proposed network consists of a multiscale feature extraction fusion (MSF) module and a multi-information fusion decoding (MFD) module. Multiscale information is efficiently extracted and fused in the MSF module utilizing stacked-scale feature fusion, which improves the network's forgery localization ability on objects of different scales. The MFD module employs contextual information combination and weighted fusion of multiscale information to guide the network in obtaining relevant clues from correlated information at multiple different scales. Experimental results and analysis have demonstrated that the proposed CMCF-Net achieves the best localization results with higher robustness.

In the industrial Internet of Things (IoT), large amounts of image data are collected, stored, and transmitted by low-power cooperative sensors and heterogeneous devices, such as monitoring pictures taken by surveillance cameras in intelligent transportation. Thus, the increasing image data would be outsourced and shared between different distributed servers. Recently, some secret image sharing with authentication (SISA) schemes were proposed, which can provide image sharing in the distributed IoT system. However, if the IoT data are tampered with by malicious attackers, the error cannot be corrected, which causes unreliability and data loss. Therefore, in order to solve the above issues, we propose a secure and reliable secret image-sharing system based on extended Hamming codes (RSIS) in IoT. In RSIS, a novel distributed IoT architecture-based secret sharing is proposed. A secret image is shared into a series of stego-images. These stego-images can be distributed among multiple edge servers in IoT, which is not easy to attract attentions of attackers. In RSIS, check bits generated by authentication bits and hamming code are embedded into the stego-images to identify the tampered places with a high probability. Theoretical analysis and experiments prove that the proposed scheme achieves a high authentication capability, and the bit errors of data can be accurately detected and corrected. Generally, the scheme is effective and practical.

Reversible Data Hiding in Encrypted Images (RDHEI) has drawn increasing concern in multimedia cloud computing scenarios. It embeds secret message into the encrypted carrier while preserving the confidentiality of the image. However, most RDHEI schemes have only one hider and one image carrier which are not efficient for the distributed system with multiple participants. Moreover, if the stego-image is lost, the cover image and the embedded data cannot be restored. To solve above issues, this paper proposes Reversible Data Hiding in Shared Images based on syndrome decoding and homomorphism (RDHSI). In RDHSI, the cover image and secret data are distributed using secret sharing. Multiple data hiders embed shared data into the shared images based on the Syndrome Decoding of Hamming codes and the additive homomorphism of the polynomial. On the receiver side, the lossless cover image and secret data are obtained. The reversibility and privacy preserving of the cover image, the complete extraction of secret data, and the fault-tolerance of the proposed schemes are achieved. The experimental results show that PSNR of the marked image is above 54 dB at the embedding rate of 0.43bpp. In order to improve the payload of secret data, the Modified RDHSI is further proposed. Generally, the proposed schemes are secure, effective and fault-tolerant.

Reversible Data Hiding in Encrypted Image (RDHEI) schemes may redistribute the data hiding procedure to other parties and can preserve privacy of the cover image. Recently, cloud computing technology has led to the rapid growth of networked media, and many multimedia rights are owned by multiple parties, such as a film's producer and multiple distributors. Thus, the data hiding task could be distributed to multiple distributed servers. Multi-party data hiding has become an important demand for networked media. In addition, it is essential to preserve multi-server and multi-message privacy and data integrity. However, most of the RDHEI schemes involve only one data hider. That inspired us to design the secure multi-party embedding over distributed encrypted-image servers as a solution for multi-party RDHEI applications. In this article, we propose a novel Reversible Data Hiding over Distributed Encrypted-Image Servers (RDH-DES) based on secret sharing. The Chinese remainder theorem, secret sharing, and block-level scrambling are developed as a lightweight cryptography to generate the encrypted image shares. These shares are distributed to different image servers and are used to embed secret data in the proposed framework. The marked encrypted image can be constructed through the marked encrypted shares from different parties, and the decryption and extraction can be completed by the receiver. The experimental results and theoretical analysis have demonstrated that the proposed scheme is secure and effective.

With the rapid development of network media, increasing research on reversible watermarking has focused on improving its robustness to resisting attacks during digital media transmission. There are some other reversible watermarking schemes that work in the encrypted domain for preserving the privacy of the cover image. The robustness of the watermarking and the privacy preserving of the cover image have become the key factors of reversible watermarking. However, there are few robust reversible watermarking schemes in the encrypted domain that could resist common attacks (such as JPEG compression, noise addition) and preserve privacy at the same time. In addition, the embedding capacity of a robust watermark and the efficiency of the encryption method must be considered. Recently, cloud computing technology has led to the rapid growth of network media, and many multimedia properties are owned by multiple parties, such as a film’s producer and multiple distributors. Multi-party watermarking has become an important demand for network media to protect all parties’ rights. In this paper, a Robust Reversible Watermarking scheme in Encrypted Image with Secure Multi-party (RRWEI-SM) based on lightweight cryptography is first proposed. Additive secret sharing and block-level scrambling are developed to generate the encrypted image. Then, the robust reversible watermarking based on significant bit Prediction Error Expansion (PEE) is performed by Secure Multi-party Computation (SMC). For applications with high robustness, a Modified RRWEI-SM is proposed by exploiting two-stage architecture. Furthermore, both the RRWEI-SM scheme and Modified RRWEI-SM scheme are separable and can be applied to multiparty copyright protection. The experimental results and theoretical analysis demonstrate here that the RRWEI-SM and the Modified RRWEI-SM are secure, robust and effective.

Secret Image Sharing (SIS) as a secure data sharing scheme in multiple cover images, has become an increasing researchers' concern. In traditional SIS, the cover image can't be recovered losslessly. The distorted cover images would reduce the practicability of the scheme, especially in medical and military images. The lossless recovery of cover images is required since all details of these images are very critical. In current Invertible SIS (ISIS), the secret image and the cover image may not be reconstructed losslessly. In addition, the authentication capability, visual quality of the stego cover image and embedding rate are limited in spatial domain-based ISIS. As an important carrier, the binary cover image is desired in real applications. Therefore, this paper proposes Transform domain-based Invertible and Lossless Secret Image Sharing schemes with Authentication (T-ILSISA), namely Integer Wavelet Transform-based ILSISA (IWT-ILSISA) and Binarization Transform-based ILSISA (BT-ILSISA) respectively. In (k,n) threshold IWT-ILSISA, the pixels of secret image and the data of cover image are regarded as the coefficients of the (k-1) degree polynomial. The values of generated share are embedded into IWT domain of the cover image. In BT-ILSISA, many different cover images are applied. The generated shares are transformed to the meaningful images since noise-like shares are easy to attract the attacker's attention, are suspected to censors and are difficult for identification and management. In the two schemes, the original secret image and the cover image can be recovered losslessly. The experimental results and theoretical analysis demonstrate that the performances of IWT-ILSISA are better than other similar schemes in the terms of embedding capacity, authentication capability and visual quality of the stego cover image. The BT-ILSISA has a lower computational complexity of the recovery.

The communication in a 6G-enabled network in a box (NIB) needs to meet the characteristics of fast, convenient, and safe. Secret sharing scheme has become a hot topic nowadays due to unconditional security and simple decryption. At the same time, a quick response (QR) code as a popular carrier has been widely applied in various industrial applications because of the data payload and convenience. Thus, the combination of secret sharing and QR code provides a solution by satisfying the requirements of 6G-enabled NIB. In this article, we design a QR code secret sharing scheme with authentication to protect private data and prevent cheater. In this scheme, a secret image is first divided into a series of shadows based on a polynomial, and authentication bits are generated based on the generated shadows. Then shadows and the authentication bits are embedded into the cover QR codes according to the error correction redundancy and the homomorphism of the Reed-Solomon code in the QR code. In addition, the secret can be restored with the qualified shares and the authentication bits could verify the authenticity of the embedded shadows. Compared with existing schemes, the proposed scheme not only guarantees a high capacity but also embeds more authentication bits to improve the authentication ability. In addition, experimental results have demonstrated that the proposed scheme is both robust and secure.

Compared to the encrypted-image-based reversible data hiding (EIRDH) method, the encrypted-signals-based reversible data hiding (ESRDH) technique is a novel way to achieve a greater embedding rate and better quality of the decrypted signals. Motivated by ESRDH using signal energy transfer, we propose an improved ESRDH method using code division multiplexing and value expansion. At the beginning, each pixel of the original image is divided into several parts containing a little signal and multiple equal signals. Next, all signals are encrypted by Paillier encryption. And then a large number of secret bits are embedded into the encrypted signals using code division multiplexing and value expansion. Since the sum of elements in any spreading sequence is equal to 0, lossless quality of directly decrypted signals can be achieved using code division multiplexing on the encrypted equal signals. Although the visual quality is reduced, high-capacity data hiding can be accomplished by conducting value expansion on the encrypted little signal. The experimental results show that our method is better than other methods in terms of the embedding rate and average PSNR.

SUMMARY Cloud computing as a promising technology and paradigm can provide various data services, such as data sharing and distribution, which allows users to derive benefits without the need for deep knowledge about them. However, the popular cloud data services also bring forth many new data security and privacy challenges. Cloud service provider untrusted, outsourced data security, hence collusion attacks from cloud service providers and data users become extremely challenging issues. To resolve these issues, we design the basic parts of secure re‐encryption scheme for data services in a cloud computing environment, and further propose an efficient and secure re‐encryption algorithm based on the EIGamal algorithm, to satisfy basic security requirements. The proposed scheme not only makes full use of the powerful processing ability of cloud computing but also can effectively ensure cloud data security. Extensive analysis shows that our proposed scheme is highly efficient and provably secure under existing security model. Copyright © 2015 John Wiley & Sons, Ltd.