In the context of the Internet of Medical Things (IoMT), medical images are highly vulnerable to unauthorized analysis and recognition by deep neural networks (DNNs) during transmission and sharing, posing serious threats to patient privacy and the rights of legitimate users. Reversible Adversarial Examples (RAEs) have been proposed to prevent unauthorized DNNs from interpreting image content without affecting normal usage by authorized parties. However, existing RAE methods primarily focus on privacy protection while largely neglecting copyright issues. To address this issue, we propose a robust RAE generation algorithm based on digital watermarking, which achieves copyright protection and reversible adversarial protection. Specifically, LayerCAM is used to determine the watermark embedding region (WR), and a multi-objective optimization method with dynamic weight adjustment named as DW-NSGA-PSO is presented to optimize the embedding position and transparency. Then, the alpha blending strategy is adopted to fuse the watermark into the original sample, generating a watermarked adversarial example. Next, the watermarked image with location information serves as the base sample, and optimized adversarial perturbations are embedded into the WR to resist removal attack of watermark removal networks. Finally, reversible data hiding (RDH) is used to embed information about the WR into the non-embedding regions of the image, enabling reversible adversarial protection. Besides, our algorithm further enhances the transfer attack capability of RAEs and robustness against various image attacks by randomly preprocessing the images. Experimental results demonstrate that the proposed algorithm achieves significantly attack performance on both color and grayscale image datasets, reaching a maximum ASR of 91%, and it may effectively protect both privacy and copyright across multiple datasets while maintaining good visual quality.

The rapid development of social networks, coupled with the proliferation of Internet of Things image-capture devices, has made sharing images easier but also raised concerns about privacy leakage. For this reason, image content privacy protection has attracted great attention and focused research. However, existing schemes suffer from participants inability to self-manage privacy, poor visual usability of protected images, and risks of key theft during transmission. To address these challenges, this article proposes a self-managed privacy control and sharing scheme (SMPCS) based on thumbnail-preserving and elliptic curve Diffie-Hellman (ECDH) for social networks. In SMPCS, we first design a key generation and sharing scheme supporting multikey distribution based on ECDH and public key authentication. This scheme generates a secure data block for each participant, containing their facial codes and the owner’s public key, to ensure the security of key transmission. Next, the sensitive areas in social image are encrypted using the designed adaptive thumbnail-preserving encryption based on sensitive area (ATPE-SA), ensuring that the encrypted social image maintains visual usability. Moreover, utilizing the designed adaptive blocking module, participants can balance the privacy and usability of encrypted images by adjusting privacy parameters to meet diverse wishes. Finally, participants can choose whether to reconstruct social images according to their privacy wishes, achieving autonomous privacy control. Extensive experimental analyses and comparisons with state-of-the-art schemes demonstrate the superiority of the proposed SMPCS in terms of autonomous privacy control, usability and security.

Traditional JPEG image encryption that prioritizes solely confidentiality fails to account for the pressing usability requirements of cloud-based environments, thus boosting the boom in thumbnail-preserving encryption (TPE) to balance image privacy and usability. However, existing TPE schemes for JPEG images face numerous challenges, such as insufficient security, inability to achieve lossless decryption, and high file extension. To address these challenges, we propose a TPE scheme based on dynamic M-ary decomposition and adaptive threshold constraints (TPE-MDTC). First, the valid ranges of quantized DC coefficients for JPEG images are determined. Then, a sum-preserving encryption method for quantized DC coefficients with compliance threshold constraints is designed using the bit-plane permutation to preserve thumbnails with high accuracy. Next, the introduction of dynamic M-ary decomposition effectively changes bit statistical characteristics preserved by bit-plane permutation, enhancing the ciphertext security. Finally, a quantized AC encryption method with RV (Run/Value) pair global permutation is proposed, effectively modifying the unit block features, thereby significantly improving the security and attack resistance of encrypted images. Experimental results show that the proposed TPE-MDTC scheme can reconstruct the original JPEG images without loss, and the generated ciphertext images exhibit significant advantages over previous schemes regarding file extension and security.

The popularity of wearable medical data collection and surveillance devices provides real-time guarantees for the whole process of a patient's medical treatment, especially medical image data plays a key role. However, existing medical images face data leakage, pollution and vulnerability to attacks during transmission over wireless body area networks(WBANs). To address these issues, a privacy protection algorithm based on Hopfield cross neural network (HCNN) for medical data is proposed. Specifically, the HCNN model is first constructed and its dynamic behavior is analyzed, which is suitable for application to image encryption. Then, a confusion method of NZ fractal curve sorting matrix (NZ-FCSM) is designed to achieve good encryption effect. Subsequently, the secret image sharing (SIS) technique based on sharing matrix is introduced to enhance the algorithm robustness. Finally, an alignment embedding of double diamond prediction (AEDDP) method is proposed to implement lossless hiding of private information. The present issues in medical image protection include ensuring the security and effectiveness of encryption algorithms while maintaining the robustness and concealment of ciphertext data, and balancing the need for preservation with the limited resources of complex work environment. Experimental results show that the proposed algorithm achieves PSNR of 53 dB for the cipher image, more than 36 dB for the reconstructed image, and the information entropy of the secret image is over 7.99, and displays good robustness. These findings highlight the validity of the algorithm in medical image data privacy preserving applications that ensure confidentiality and extend to practical applications of concealed transmission of confidential information and secure multi-party transactions.

In recent years, the Metaverse has garnered significant attention in social and Metahuman realms, showcasing substantial value and immense developmental potential through its integration of virtual and real worlds. However, this integration has also raised security concerns. For instance, in digital image forensics, the malicious dissemination of false images by wrongdoers could result in serious consequences and the propagation of misinformation. This article presented a novel two-branch network (abbreviated as CAFTB-Net) to detect and localize the forged regions of document images in the Metaverse. One branch extracts manipulation trace directly from spatial information, e.g., unnatural smears, anomalies between pixels. The other branch employs an SRM filter to transform the input image from the color domain into the noise domain, effectively extracting anomalies, such as global noise inconsistencies from the noise domain. Compared to spatial domain features, the discontinuity of forgery traces within the noise domain aids the network in authenticating the document image. The two branches extract local and global features in the forged document images. Finally, we propose a cross-attention module to fuse local spatial features and global noise features. Extensive experimental results demonstrate that the proposed network achieves F1 scores of 0.819 and 0.948 on the SACP and ICDAR datasets, respectively, with AUC scores of 0.933 and 0.764, outperforming some of the state-of-the-art algorithms.

As the development of the Internet of Things (IoT), the security of images in social networks is attracting more and more attention. Among the various encryption methods, thumbnail-preserving encryption (TPE) has gained much attention and diverse study, for it has powerful capability of balancing the security and usability of cloud storage images by simultaneously securing privacy and preserving visual information of images. Unfortunately, most of the existing TPE schemes for JPEG images have the disadvantages of limited thumbnail precision or weak security or irreversibility, making them vulnerable to cryptanalysis. To address these issues, we propose a TPE based on adjustable precision (TPE-AP) for JPEG images. First, a joint adjustment strategy is introduced for encrypted quantized QC coefficient (QDCC) and quantized AC coefficient (QACC) of plain image, which makes the security and usability of the thumbnail controllable by exploiting numerical characteristics of QDCC and distribution features of QACC. Second, a time-varying encryption strategy based on international time is presented, characteristics of JPEG compression and quantized coefficients are combined to generate encryption sequences, improving the security of the whole encryption process. In addition, we explore the redundancy of QACC within DCT blocks and provide an improved embedding strategy to solve irreversibility problem. Experimental results show that the peak signal-to-noise ratio (PSNR) of thumbnail-preserving accuracy reaches 52 dB, the file expansion is minimally limited to 6.3%, the decryption time less than 0.13 s, and the mean average precision (mAP) of retrieval attains 62%, it indicates that TPE-AP outperforms the state-of-the-art methods.

Medical images on the Internet of Medical Things (IoMT) can be easily collected, recognized and analyzed by unauthorized individuals and companies using deep neural networks (DNNs). The illegal use of this data compromises patient privacy and the rights of authorized users. The reversible adversarial example (RAE) is proposed to prevent unauthorized DNNs from recognizing privacy images without affecting authorized users. Nevertheless, existing RAE methods lack research on copyright protection, and unrestricted distribution and sale of these images can seriously jeopardize the copyrights of the image owners. To address this issue, this paper proposes an RAE based on visible watermark perturbation (RAE-VWP). Specifically, we first obtain the watermark embedding region (WER) of the image by the proposed saliency map fusion algorithm and save the information of this region using the reversible data hiding technique. Then, the proposed adaptive memory harmony search algorithm is applied to optimize the position and transparency of the embedded watermark perturbation. Finally, the RAE is generated by embedding the watermark perturbation into the WER of the image using the alpha blending technique. Particularly, the ensemble watermark vaccine is proposed to defend against attacks from watermark removal networks, significantly improving the robustness of RAE-VWP. The original image can be recovered without distortion by extracting the information saved in the RAE. Numerous experiments have shown that RAE-VWP can simultaneously protect image privacy and copyright with excellent reversibility.

With the large-scale deployment of the Internet of Things (IoT), lots of images are generated and outsourced to the cloud to alleviate storage burdens. Encrypted image retrieval has been widely studied as a promising technique for balancing privacy and usability. However, existing schemes have primarily focused on retrieval usability but neglected visual usability, resulting in limitations in image management, even in scenarios where preserved visual information poses no hazard to privacy. In this article, we are thus inspired to propose a privacy-preserving JPEG image retrieval scheme that effectively enhances retrieval efficiency and accuracy while ensuring low-file expansion and excellent thumbnail-preserving accuracy for ciphertext images. Specifically, the well-designed thumbnail-preserving encryption (TPE) enables accurate thumbnail-preserving and high-quality decryption of encrypted images by integrating Huffman coding and information embedding techniques. Moreover, the proposed TPE method fully considers JPEG compression to decrease the ciphertext file expansion and gain good format compatibility. Additionally, adaptive encryption key generation is designed to minimize key storage and considerably bolster security. Also, the cloud can generate preview thumbnails for uploaded TPE-encrypted images, and then extract the Hue-saturation-value (HSV) and uniform local binary pattern (ULBP) features from thumbnails instead of encrypted images to boost retrieval efficiency and accuracy. Experimental results show that the peak signal-to-noise ratio (PSNR) of thumbnail-preserving accuracy and decrypted image quality reaches 52 dB and 61 dB, respectively, the file expansion is minimally limited to 6%, and the mean average precision (mAP) of retrieval attains 64%, indicating that our scheme significantly outperforms the state-of-the-art ones.

In this paper, a new four-dimensional chaotic system derived from the continuous Hopfield neural network (CHNN) model is designed, and the weight parameters are optimized to achieve superior dynamics. Furthermore, we verify the superior performance of the system through an analysis of its dissipation and other aspects. Meanwhile, to address the issues of low reconstruction quality and unsatisfactory security performance of the current compressed sensing (CS)-based image encryption algorithm, this paper introduces a compression encryption algorithm based on the chaotic system. Specifically, this algorithm designs a new fractal curve based on the Hilbert curve by incorporating a unique rotation and connection in the iterative process, which allows for effective displacement of the image. Additionally, a new measurement matrix with low spectral norm is constructed utilizing the QR decomposition based on the Householder transform to improve the compression performance. Finally, this paper introduces a bidirectional Z-shaped diffusion method based on chaotic sequences and optimized multiple logical operations (BZCM). By leveraging the optimized logic operation rules and logic key matrix proposed in this paper, this method enhances the diffusion effect. Experimental analyses demonstrate that the proposed algorithm achieves high security and reconstruction performance.

The growing practice of outsourcing captured photos to the cloud has provided users with convenience while also raising privacy concerns. Traditional image encryption techniques prioritize privacy protection but often compromise usability, which is unacceptable for cloud users. To strike a balance between image privacy and usability, scholars have proposed thumbnail-preserving encryption (TPE), whose cipher image preserves the same thumbnail as the plain image while erasing details beyond the thumbnail, providing visual usability while protecting privacy. Regrettably, most of the proposed TPE schemes are not well-suited for widely used JPEG images, and existing TPE schemes supporting JPEG suffer from drawbacks such as poor visual usability, high expansion rate, and the inability to decrypt without loss. Besides, the retrieval designed for TPE-encrypted images exhibits limited generalization. To address these challenges, we pertinently introduce a TPE based on adaptive deviation embedding (TPE-ADE) for JPEG images, incorporating Huffman coding and reversible data hiding techniques. By leveraging JPEG in-compression encryption, we achieve perfectly reversible TPE that enhances visual usability and reduces expansion rates of TPE-encrypted images. Additionally, we encourage the TPE-encrypted images to resemble low-resolution images (LRIs). Then, the convolutional neural network (CNN) is employed to recognize and retrieve LRIs to verify the functionality of TPE-encrypted images. Also, a teacher-assistant-student (TAS) learning paradigm is proposed to optimize the CNN model, enhancing the performances of recognition and retrieval. Experimental results validate the superiority of our encryption algorithm and the effectiveness of TAS.

With the development of telemedicine diagnosis technology, the collection, storage, and transmission of medical data has become a pivotal problem. To solve these problems, in terms of semi-tensor product compressed sensing (STP-CS) and hybrid cloud, a new medical data transmission framework is presented in this article, which can ensure the efficiency, confidentiality, and verifiability of data transmission. According to the edge detection, the authentication information is embedded into the insignificant area of the medical image to protect the authenticity of the data. STP-CS is utilized to measure and encrypt the medical image, which improves the efficiency of data transmission. In order to make the image data more secure, an adaptive cyclic shift diffusion algorithm based on chaotic sequence is used to effectively diffuse the measurement results. In addition, we also apply encoding for the quantized measurement value to be used as tamper proof authentication. The simulation results prove that the presented medical data transmission scheme can effectively improve the image reconstruction effect, transmission efficiency, and security.

With the substantial increase in image transmission, the demand for image security is increasing. Noise-like images can be obtained by conventional encryption schemes, and although the security of the images can be guaranteed, the noise-like images cannot be directly previewed and retrieved. Based on the rank-then-encipher method, some researchers have designed a three-pixel exact thumbnail preserving encryption (TPE2) scheme, which can be applied to balance the security and availability of images, but this scheme has low encryption efficiency. In this paper, we introduce an efficient exact thumbnail preserving encryption scheme. First, blocking and bit-plane decomposition operations are performed on the plaintext image. The zigzag scrambling model is used to change the bit positions in the lower four bit planes. Subsequently, an operation is devised to permute the higher four bit planes, which is an extended application of the hidden Markov model. Finally, according to the difference in bit weights in each bit plane, a bit-level weighted diffusion rule is established to generate an encrypted image and still maintain the same sum of pixels within the block. Simulation results show that the proposed scheme improves the encryption efficiency and can guarantee the availability of images while protecting their privacy.

To balance the privacy and usability of images in the cloud, Tajiket al.recently designed a thumbnail preserving encryption (TPE) based on sum-preserving encryption, however, multiple iterations make it inefficient. We use CycleGan to simulate randomized unary encoding (RUE) and achieve a more efficient TPE. Thumbnail consistency loss is proposed to guarantee the visual quality of the encrypted image, and the quality of decrypted image is improved via ssim-loss. Besides, a decryption network with a key is retrained such that it can decrypt cipher images in multiple domains, and a binary string is adopted as the key instead of the decryption network parameters to facilitate sharing of images. Simulations verify the effectiveness of the proposed algorithm.