Search Results (171)

In this paper, we propose a block-distortion-free reversible data hiding method for encryption-then-compression (EtC) images that supports fully flexible access privileges without constraints on the restoration order. The proposed approach redesigns the pre-processing strategy of previous work to ensure a clear separation of processing roles between the image owner and the data hider. It also introduces a pixel-value modification process that divides the target range into two regions to mitigate the influence of negative–positive inversion during restoration. As a result, block distortion in marked images is eliminated while preserving role separation between the image owner and the data hider. The proposed method offers four key advantages: flexible access privileges, elimination of block distortion, explicit role separation, and competitive hiding capacity comparable to existing methods with flexible restoration capabilities. Experimental results demonstrate that the proposed method achieves a high marked-image quality and competitive hiding capacity while maintaining the compression performance of marked EtC images. Furthermore, security analysis confirms the robustness of the generated EtC images against a representative ciphertext-only attack. Full article

Reversible data hiding (RDH) enables the reversible embedding of additional data into cover media, allowing the cover media to be perfectly recovered after extracting the embedded data. RDH relies on accurate prediction of pixels to generate sharply distributed prediction error histograms, thereby maximizing embedding capacity and minimizing visual distortion. While convolutional neural network (CNN)-based predictors excel in smooth regions of cover images by leveraging local correlation, they often fail to produce accurate predictions in the textured regions. To address the limitation of CNN predictors, we propose a novel attention fusion-based CNN predictor (AFCNNP) that adaptively combines the CNN predictor with a non-local means (NLM) predictor. The proposed fusion framework learns spatial weight maps to favor CNN predictions in smooth regions and NLM predictions in textured regions. The experimental results show that the proposed framework outperforms other state-of-the-art CNN predictors by significantly lowering the mean absolute error, mean squared error, and variance of prediction errors, leading to more accurate pixel predictions. With the proposed fusion framework, the embedding and visual performance of prediction error expansion (PEE)-based RDH is improved compared to typical CNN-based RDH methods. Full article

Reversible data hiding (RDH) is a key technique in secure multimedia applications, enabling the exact recovery of both embedded data and the original cover content. To further enhance security and embedding capacity, this paper proposes a dual-image reversible data hiding (DIRDH) method based on absolute moment block truncation coding (AMBTC). In the proposed scheme, two identical AMBTC-decoded images are exploited as twin covers, and secret bits are adaptively embedded into paired pixels using a variable embedding rate. To ensure data integrity, a lightweight Hash-based Message Authentication Code (HMAC) mechanism is integrated, allowing reliable detection of tampering without additional side information. Experimental results demonstrate that the proposed method achieves high embedding capacity while preserving good visual quality and provides effective authentication against representative tampering cases, including pixel modification, noise addition, and cropping. These contributions highlight the advantages of combining DIRDH with AMBTC, offering a practical and secure solution for high-capacity reversible data hiding. Full article

With the proliferation of Internet of Things (IoT) deployments and mobile sensing systems, reversible data hiding in encrypted images (RDHEI) has emerged as a cornerstone technology for secure cloud-based sensor data management. RDHEI ensures data confidentiality while enabling bit-to-bit restoration of original visual assets. However, conventional RDHEI methods often struggle to optimize the trade-off between high embedding capacity (EC) and the fidelity requirements of sensor-acquired content. This paper proposes an advanced RDHEI framework based on Adaptive Predicted Value Computation and Pixel Classification (APVCPC). The core contribution is a context-aware prediction engine that adaptively selects optimal estimation functions based on local texture complexity, significantly enhancing prediction accuracy in heterogeneous image regions. Subsequently, a content-driven pixel classification paradigm categorizes pixels into loadable (Lpxls) and non-loadable (NLpxls) sets using a dynamic threshold, maximizing the utilization of spatial redundancy. The proposed scheme further supports separable data extraction and image decryption, providing flexible access control for diverse user privileges in secure sensing scenarios. Experimental results on standard benchmarks and the BOW-2 database demonstrate that APVCPC achieves a superior average embedding rate exceeding 2.0 bpp and ensures perfect reversibility, significantly outperforming state-of-the-art techniques in terms of both capacity and security. Full article

We developed a novel pattern-based dual-image reversible data hiding scheme utilizing vertical bars and bitten square blocks to improve data capacity, security, embedding efficiency, and visual quality. By embedding secret messages into two grayscale images that closely match the original, the approach significantly lowers the risk of detection by unauthorized parties. The technique includes reference matrix construction, position-based embedding, and perfect message extraction and image recovery. Experimental results demonstrate that the pattern-based method achieves a superior balance between payload and visual quality, making it well-suited for secure and high-performance applications. Full article

Encryption-then-compression (EtC) based on block scrambling enables privacy-preserving image sharing while maintaining compatibility with standard image codecs, yet it disrupts the spatial correlations and synchronization cues required by conventional reversible data hiding (RDH). This difficulty is further amplified in grayscale-based EtC pipelines, where RGB-to-YCbCr conversion and component serialization introduce representation shifts and non-bijective rounding/clamping effects, complicating reliable embedding and extraction. This paper presents a unified RDH framework compatible with both RGB-based and grayscale-based block-scrambling EtC systems, without altering the underlying encryption procedures. The core idea is to restore embedding and extraction synchronization directly in the encrypted domain using two encryption-invariant cues: diagonal pixel absolute difference (DPAD) and an encryption-invariant synchronization index (EISI), together with domain-consistent handling of the grayscale conversion pipeline. Experimental results on standard datasets demonstrate perfect reversibility and stable embedding performance under the evaluated settings, with negligible impact on lossless compressibility. We further observe that the proposed embedding can increase statistical dispersion within encrypted blocks; although not designed as a security enhancement, this effect degrades the performance of representative texture-based analyses in the considered ciphertext-only setting. Full article

Encryption-then-compression (EtC) enables secure image processing while retaining coding efficiency. In grayscale-based EtC pipelines with YCbCr transformation and component serialization, reversible data hiding (RDH) becomes challenging because cross-channel correspondence is disrupted, and block-wise encryption operations (permutation, rotation, and brightness inversion) break embedding synchronization. This paper presents a block-independent and lightweight RDH framework for such component-serialized grayscale EtC systems. The framework combines diagonal pixel absolute difference (DPAD)-based embedding with an encryption-invariant synchronization index (EISI), enabling reliable encrypted-domain extraction and self-synchronization under component serialization and block permutation, without auxiliary side information or any modification to the underlying EtC pipeline. All operations are performed locally at the block level, making the framework naturally parallelizable when needed. Experiments on standard datasets with diverse texture characteristics demonstrate reliable data extraction and perfect reversibility while preserving the structural properties required for secure encryption and lossless-mode compression. These results indicate that the proposed framework is well-suited to practical EtC deployments where lightweight implementation and block-level independence are essential. Full article

Prediction Error Ordering (PEO) can integrate the encoding gains of both prediction and sorting by replacing the PVO (Pixel Value Ordering) pixel blocks with prediction-error blocks before sorting. It remains an open topic to construct a consistent, high-accuracy, and block-based prediction method for both the cover and stego images, which are the images before and after embedding the secret data. This paper proposes a novel scheme that explicitly exploits image self-similarity through a deterministic, non-linear prediction framework. Our scheme constructs four independent but self-similar images using a down-sampling and a bicubic-based up-sampling algorithm. Then, a linear ridge regression model learns the self-similarity among pixels with the same coordinates across the four dependent images. It also provides two new 2D mappings to reduce embedding disturbances further. Experimental results demonstrate that our PEO scheme achieves average PSNRs of 59.7 and 58.6 dB at 0.04 bpp, 56.7 and 56.0 dB at 0.08 bpp on the Kodak and USC-SIPI image datasets, respectively. Full article

Thumbnail-preserving encryption (TPE) aims to balance data security and usability by allowing encrypted content to retain a coarse visual preview while protecting sensitive details. While existing TPE techniques primarily target 2D images, effective and reversible TPE for 3D point clouds remains underexplored. This paper proposes a thumbnail-preserving encryption framework specifically designed for point clouds, addressing the challenges arising from irregular spatial structure and viewpoint-dependent visualization. The proposed method integrates perception-guided spatial subdivision with key-dependent chaotic perturbation to obfuscate fine-grained geometric details while intentionally preserving coarse structural information under the TPE threat model. A reversible integer-domain design is further incorporated to enable exact recovery of the original point cloud and support reversible data hiding by exploiting coordinate-level redundancy. Extensive experiments conducted on diverse point clouds demonstrate that the proposed framework maintains stable thumbnail fidelity across different viewing conditions, achieving high structural similarity, while guaranteeing perfect reversibility with zero reconstruction error. In contrast to existing image-based TPE frameworks, the proposed method extends the TPE paradigm to 3D point clouds by providing full reversibility, auxiliary message embedding support, and stable thumbnail fidelity under varying viewing conditions. Quantitative results demonstrate that thumbnail-level structural similarity is well preserved, while the original point clouds are exactly recovered after decryption. Full article

Ensuring the integrity of medical images is essential to securing clinical workflows, telemedicine platforms, and healthcare IoT environments. Existing watermarking and reversible data-hiding approaches often modify pixel intensities, reducing diagnostic fidelity, introducing embedding constraints, or causing instability under compression and format conversion. This work proposes a distortion-free, non-embedding authentication framework that leverages the inherent stability of the most significant bit (MSB) patterns in the Non-Region of Interest (NROI) to construct a secure and tamper-sensitive reference for the diagnostic Region of Interest (ROI). The ROI is partitioned into fixed blocks, each producing a 256-bit SHA-256 signature. Instead of embedding this signature, each hash bit is mapped to an NROI pixel whose MSB matches the corresponding bit value, and only the encrypted coordinates of these pixels are stored externally in a secure database. During verification, hashes are recomputed and compared bit-by-bit with the MSB sequence extracted from the referenced NROI coordinates, enabling precise block-level tamper localization without modifying the image. Extensive experiments conducted on MRI (OASIS), X-ray (ChestX-ray14), and CT (CT-ORG) datasets demonstrate the following: (i) perfect zero-distortion fidelity; (ii) stable and deterministic MSB-class mapping with abundant coordinate diversity; (iii) 100% detection of intentional ROI tampering with no false positives across the six clinically relevant manipulation types; and (iv) robustness to common benign Non-ROI operations. The results show that the proposed scheme offers a practical, secure, and computationally lightweight solution for medical image integrity verification in PACS systems, cloud-based archives, and healthcare IoT applications, while avoiding the limitations of embedding-based methods. Full article

Pixel value ordering (PVO) is a widely used framework for reversible data hiding (RDH). As the demand for higher embedding capacity continues to grow, achieving a proper balance between capacity and image quality has become increasingly important. In this paper, we propose a novel PVO-based multi-pixel embedding RDH scheme for grayscale images, which improves capacity by embedding multiple bits of data within multiple pixels in each block. A PVO recovery strategy is designed to guarantee reversibility while minimizing image distortion when multiple bits are embedded per block. Moreover, an improved flexible spatial location strategy is introduced, which defines pixel positions within a block using twelve modes. By selecting the optimal mode for each block, the number of expandable prediction errors is increased, further enhancing embedding capacity. In addition, the artificial lemming algorithm (ALA) is employed to optimize embedding parameters, enabling a better balance between capacity and visual quality for a given payload. Experimental results demonstrate that the proposed method achieves significantly improved embedding capacity while maintaining high image quality, offering a well-balanced performance compared to similar PVO-based schemes. Full article

This paper presents a federated learning (FL) architecture tailored for anomaly detection in wire arc additive manufacturing (WAAM) that preserves data privacy while enabling secure and distributed model training across heterogeneous process units. WAAM’s inherent process complexity, characterized by high-dimensional and asynchronous sensor streams, including current, voltage, travel speed, and visual bead profiles, necessitates a decentralized learning paradigm capable of handling non-identical client distributions without raw data pooling. To this end, the proposed framework integrates reversible data hiding in the encrypted domain (RDHE) for the secure embedding of sensor-derived features into weld images, enabling confidential parameter transmission and tamper-evident federation. Each client node employs a domain-specific long short-term memory (LSTM)-based classifier trained on locally curated time-series or vision-derived features, with model updates embedded and transmitted securely to a central aggregator. Three FL strategies, FedAvg, FedProx, and FedPer, are systematically evaluated against four robust aggregation techniques, including KRUM, Multi KRUM, and Trimmed Mean, across 100 communication rounds using eight non-independent and identically distributed (non-IID) WAAM clients. Experimental results reveal that FedPer coupled with Trimmed Mean delivers the optimal configuration, achieving maximum F1-score (0.912), area under the curve (AUC) (0.939), and client-wise generalization stability under both geometric and temporal noise. The proposed approach demonstrates near-lossless RDHE encoding (PSNR > 90 dB) and robust convergence across adversarial conditions. By embedding encrypted intelligence within weld imagery and tailoring FL to WAAM-specific signal variability, this study introduces a scalable, secure, and generalizable framework for process monitoring. These findings establish a baseline for federated anomaly detection in metal additive manufacturing, with implications for deploying privacy-preserving intelligence across smart manufacturing (SM) networks. The federated pipeline is backbone-agnostic. We instantiate LSTM clients because the sequences are short (five steps) and edge compute is limited in WAAM. The same pipeline can host Transformer/TCN encoders for longer horizons without changing the FL or security flow. Full article

With the rapid proliferation of digital multimedia in resource-constrained Internet of Things (IoT) environments, there is growing demand for efficient image compression combined with secure data embedding. Existing Vector Quantization (VQ)-based Reversible Data Hiding (RDH) methods prioritize embedding capacity while neglecting reconstruction fidelity, often introducing noticeable quality degradation in edge regions—unacceptable for high-fidelity applications such as medical imaging and forensic analysis. This paper proposes a lightweight RDH framework with a once-offline trained VQ codebook that simultaneously performs secure data embedding and visual quality enhancement for VQ-compressed images. Quality Improvement Codes (QIC) are generated from pixel-wise residuals between original and VQ-decompressed images and embedded into the VQ index table using a novel Recoding Index Value (RIV) mechanism without additional transmission overhead. Sobel edge detection identifies perceptually sensitive blocks for targeted enhancement. Comprehensive experiments on ten standard test images across multiple resolutions (256 × 256, 512 × 512) and codebook sizes (64–1024) demonstrate Peak Signal-to-Noise Ratio (PSNR) gains of +4 to +5.39 dB and Structural Similarity Index Measure (SSIM) improvements of +4.12% to +9.86%, with embedding capacities approaching 100 Kbits. The proposed approach consistently outperforms existing methods in both image quality and payload capacity while eliminating computational overhead of deep learning models, making it highly suitable for resource-constrained edge devices and real-time multimedia security applications. Full article

As an emerging concept, steganography without embedding (SWE) hides a secret message without directly embedding it into a cover. Thus, SWE has the unique advantage of being immune to typical steganalysis methods and can better protect the secret message from being exposed. However, existing SWE methods are generally criticized for their poor payload capacity and low fidelity of recovered secret messages. In this paper, we propose a novel steganography-without-embedding technique, named DF-SWE, which addresses the aforementioned drawbacks and produces diverse and natural stego images. Specifically, DF-SWE employs a reversible circulation of double flow to build a reversible bijective transformation between the secret image and the generated stego image. Hence, it provides a way to directly generate stego images from secret images without a cover image. Besides leveraging the invertible property, DF-SWE can invert a secret image from a generated stego image in a nearly lossless manner and increase the fidelity of extracted secret images. To the best of our knowledge, DF-SWE is the first SWE method that can hide multiple images into one image with the same size, significantly enhancing the payload capacity. According to the experimental results, the payload capacity of DF-SWE achieves 24–72 BPP, which is 8000∼16,000 times more compared to its competitors while producing diverse images to minimize the exposure risk. Importantly, DF-SWE can be applied in the steganography of secret images in various domains without requiring training data from the corresponding domains. This domain-agnostic property suggests that DF-SWE can (1) be applied to hiding private data and (2) be deployed in resource-limited systems. Full article

With the proliferation of image-capturing and display-enabled IoT devices, ensuring the authenticity and integrity of visual data has become increasingly critical, especially in light of emerging cybersecurity threats and powerful generative AI tools. One of the major challenges in such sensor-based systems is the ability to protect privacy while maintaining data usability. Reversible data hiding has attracted growing attention due to its reversibility and ease of implementation, making it a viable solution for secure image communication in IoT environments. In this paper, we propose reversible data hiding techniques tailored to the content characteristics of images. Our approach leverages subsampling and quadtree partitioning, combined with multi-stage prediction schemes, to generate a predicted image aligned with the original. Secret information is embedded by analyzing the difference histogram between the original and predicted images, and enhanced through multi-round rotation techniques and a multi-level embedding strategy to boost capacity. By employing both subsampling and quadtree decomposition, the embedding strategy dynamically adapts to the inherent characteristics of the input image. Furthermore, we investigate the trade-off between embedding capacity and marked image quality. Experimental results demonstrate improved embedding performance, high visual fidelity, and low implementation complexity, highlighting the method’s suitability for resource-constrained IoT applications. Full article