ABSTRACT Reversible data hiding in encrypted images (RDH‐EI) is a key technique for secure communication, secure cloud storage and privacy protection. Most existing RDH‐EI algorithms rely on a single and static bit‐plane encoding strategy, failing to fully exploit the structural differences of bit‐planes from the block level to the sequence level, thereby limiting further improvements on embedding capacity. To address this issue, this paper proposes a novel RDH‐EI algorithm based on Bit‐plane Classification and Adaptive Group Coding (hereafter BCAGC algorithm). The core contributions are as follows: (1) A bit‐plane classification mechanism is proposed. It categorises bit‐planes into simple or complex types according to the number of non‐all‐zero 4‐bit sequence (NAZ‐4BS) patterns. (2) An adaptive group coding scheme is proposed. It dynamically selects between fixed‐length coding and Huffman coding based on bit‐plane types and the frequency distribution of NAZ‐4BS patterns, thereby achieving compact coding tables and efficient bit‐plane compression. Experimental results demonstrate that the proposed BCAGC algorithm achieves average embedding rates of 4.1472, 4.0565 and 3.4465 bpp on the BOSSbase, BOWS‐2 and UCID datasets, respectively, outperforming several state‐of‐the‐art RDH‐EI methods.

Secure and high-capacity secret information transmission is an important task of the image hiding research. The existing image hiding methods face some critical issues: cover-based methods offer high capacity but introduce image distortion and security risks, whereas secure coverless methods have low capacity. To address these issues, this paper proposes a novel generative-based coverless multi-image hiding method called GCL-MIH, which can achieve high capacity and high security. The GCL-MIH first utilizes a feature reverse module to compress multiple secret images into multiple feature vectors and then normalizes them to generate a vector that conforms to a standard normal distribution, and finally inputs this vector into an invertible generative network (Flow-GAN) to generate a face image, enabling coverless multiple-image hiding without a predefined cover image. Experimental results demonstrate that the GCL-MIH successfully hides up to four images within a single generated face image, achieving a maximum embedding rate of 32 bpp. This capacity far exceeds those of the existing coverless methods. On the COCO test set, the generated stego images of the GCL-MIH are highly realistic (FID score: 11.98), and the recovered secret images exhibit satisfactory fidelity (the average PSNR and SSIM of four recovered secret images are 33.18 dB and 0.9412).

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.

Reversible data hiding (RDH) in shared images is an effective technique for securely storing and managing confidential images. However, most existing methods suffer from a noticeable data expansion and cannot achieve a good trade-off between data expansion and embedding rate. To address this issue, we propose a novel RDH in shared images (RDHSI) using overlapped coefficients in polynomials. In the proposed method, an original image is compressed losslessly to reduce its size before sharing, and then the compressed image is divided into a series of groups, where any two adjacent groups have an overlapped part. Next, each group is shared by our proposed (k,n)-threshold based sharing technique, which is performed by the polynomial over Galois field GF(28) with overlapped coefficients. Finally, data embedding is performed on each shared image by bit replacement according to a constructed 0-1 matrix. Experimental results demonstrate that the proposed method can effectively reduce the sizes of the shared images and achieve a high embedding capacity.

Deep neural networks for image classification require protection against unauthorized use and redistribution. Existing watermarking methods suffer from a critical vulnerability: watermarks are always active and detectable, allowing adversaries to identify and remove them before deployment. We propose DormMark, a novel framework for image classification models that introduces delayed-activation watermarks which remain dormant and hidden under deployment-time black-box query auditing during initial deployment, but automatically activate upon fine-tuning. Our approach employs a three-stage training paradigm: (1) embedding watermarks using triggered samples, (2) masking to suppress watermark functionality while preserving its latent presence, and (3) activation through standard fine-tuning without owner intervention. This mechanism exploits neural networks’ forgetting-remembering behaviors during continued training, creating a fragile equilibrium that behaves similarly to a clean model under deployment-time black-box auditing but reliably manifests ownership indicators after modification. We consider a private-key black-box verification setting in which the owner keeps the concrete trigger instances secret. Experiments across multiple architectures (VGG19, ResNet-18/56, DenseNet-121, WideResNet-34) and datasets (CIFAR-10, CIFAR-100, GTSRB) demonstrate 100% watermark success rates, high imperceptibility (PSNR > 38 dB, SSIM = 0.99), negligible accuracy loss (< 0.04%), and robustness against 80% parameter pruning. DormMark represents a paradigm shift from static to conditionally-activated ownership verification, providing a more robust framework for intellectual property protection.

ABSTRACT Reversible data hiding in encrypted images (RDHEI) enables secret data embedding within encrypted images while allowing for the lossless recovery of the original image after data extraction. This technique holds significant applications in various domains such as cloud storage and data security. However, many existing RDHEI methods suffer from limited embedding capacity. To address this limitation, we present a novel and high capacity RDHEI algorithm via bit‐plane block rearrangement and intra‐block compression coding (hereafter BRBCC algorithm). First, the prediction error (PE) image is generated by using a median edge detection predictor, and the high‐order zero‐valued bit‐planes are compressed. The non‐zero‐valued bit‐planes are then separated into non‐overlapping blocks that can be classified as all‐zero blocks, embeddable blocks, or non‐embeddable blocks. These blocks are then sorted and grouped in terms of block type for block coding. Finally, a new intra‐block compression coding technique with small coded data for locating block elements is proposed to conduct effective compression and thereby reserve more space for embedding secret data. Experimental results indicate that the embedding rates of the BRBCC algorithm reach 3.9381 and 3.8436 bpp on the public datasets of BOSSbase and BOWS‐2, respectively, outperforming some state‐of‐the‐art RDHEI algorithms and exhibiting good application potential.

Image sharing is a crucial security technique of protecting the original image. Most image sharing methods elaborate on the uncompressed formats and cannot be applied directly to compressed formats such as JPEG format. Recently, some related JPEG sharing methods were proposed. However, these methods are short in size reduction. To address this issue, we introduce a novel JPEG image sharing method that significantly reduces the file sizes of shared images while maintaining compatibility with the JPEG standard. Specifically, an original JPEG bitstream is parsed to obtain three components, namely, DC appended bits (DCAs), AC Huffman codes (ACHs), and AC appended bits (ACAs). Different sharing mechanisms are applied for these three components. The DCAs are shared over GF(2m) in a way that prevents overflow in the shared DC coefficients. The ACH sharing is performed by polynomials over GF(p) with an intermediate process by Huffman table mapping and the ACAs are shared by polynomials over GF(2m) with directed codes. Consequently, the ACH and ACA shares with smaller sizes are generated, respectively. To be compatible with the JPEG standard, the shared ACAs are adaptively modulated according to the shared ACHs. Finally, the shared JPEG images can be reconstructed by the shared DCAs, ACHs, ACAs and the original partial JPEG information. The experimental results demonstrate that the proposed method can efficiently reduce the file sizes of shared JPEG images and preserve the JPEG format for each shared image.

Reversible data hiding in encrypted images (RDHEIs) has attracted considerable attention, as it can facilitate the management of massive encrypted images and can be employed for covert communication. Recent research has demonstrated that the RDHEI methods with pixel prediction can achieve a more significant embedding capacity than those that do not utilize pixel prediction. Moreover, the accuracy of predictors greatly impacts the embedding capacity. Nevertheless, current predictors have several limitations, including a lack of accuracy and insufficient flexibility. To address these issues, we propose a high-precision multidirectional gradient predictor (MDGP). Based on this predictor, a novel region-based RDHEI method is proposed. Pixel prediction, image compression, data embedding, data extraction, and image recovery are conducted independently within image regions. Extensive experiments have demonstrated that the proposed MDGP predictor outperforms the current predictors in several metrics, including the average absolute errors, information entropy, and embedding capacity. The proposed RDHEI method demonstrates superior embedding capacity on the test images, and the data sets BOSSBase and BOWS2 outperforming the several state-of-the-art methods. Furthermore, it exhibits robust resilience to a variety of attacks, including perceptual attacks, statistical analysis, and patch removal attacks.

Image hiding aims to hide the secret data in the cover image for secure transmission. Recently, with the development of deep learning, some deep learning-based image hiding methods were proposed. However, most of them do not achieve outstanding hiding performance yet. To address this issue, we propose a new image hiding framework called CAE-NF, which consists of compressive autoencoders (CAE) and normalizing flow (NF). Specifically, CAE's encoder respectively maps the secret image and cover image into the corresponding feature vectors. Image hiding and recovery can be modelled as the forward and backward processes of NF since NF is an invertible neural network. NF maps two feature vectors to a stego-image by its forward process. On the recovery side, the stego-images are mapped to two feature vectors by NF's backward process. Finally, the secret image is recovered by CAE's decoder. The proposed framework can achieve a good trade-off between the stego-image quality and recovered secret image quality, and meanwhile, improve the hiding and recovery performances. The experimental results demonstrate that the proposed framework significantly outperforms some state-of-the-art methods in terms of invisibility, security, and recovery accuracy on various datasets.

Reversible data hiding in encrypted images (RDHEI) is an effective technology of protecting private data. In this paper, a high-capacity RDHEI method with asymmetric coding and bit-plane block compression is proposed. Our major contributions are twofold. (1) We propose an asymmetric coding technique for processing prediction error (PE) blocks before encryption. The proposed asymmetric coding technique does not generate the sign bit-plane and facilitates massive 0s converging on the high bit-planes. This is beneficial to reserve the embedding room. (2) We present a bit-plane block compression technique for improving the embedding capacity. This technique divides the PE codes in a block into two parts which are both compressed and thus contribute a large embedding room. Experimental results demonstrate that the average embedding rates of the proposed method are 4.156, 4.063 and 3.450 bpp on the BOSSBase, BOWS-2 and UCID datasets, respectively. Comparisons show that our average embedding rates on the three datasets are all bigger than those of some state-of-the-art methods.

Reversible data hiding (RDH) in encrypted images has emerged as an effective technique for securely storing and managing confidential images in the cloud. However, most RDH methods in shared images (RDHSI) are designed for uncompressed images and cannot be applied for JPEG images. To address this issue, we propose a novel RDH in shared JPEG images. Our method consists of JPEG image sharing and data hiding in JPEG shares, which are both conducted on JPEG bit-stream. Specifically, the DC appended bits (DCA) and AC appended bits (ACA) derived from the original JPEG bit-stream are shared by ( \(k\) , \(n\) ) threshold Chinese remainder theorem-based secret sharing (CRTSS) with two different constraints, one for DC sharing and another for AC sharing. The constraint of DC sharing ensures that the DC coefficient shares do not overflow. The constraint of AC sharing ensures the sizes of ACA shares are less than the sizes of the original ACA so that the embedding room can be vacated from each shared JPEG bit-stream. Each data-hider can embed the secret data into the personal JPEG share. The original JPEG image can be recovered losslessly from any \(k\) JPEG shares. The proposed sharing and data hiding are both well compatible with the JPEG standard. Experimental results demonstrate that the proposed method not only well preserves the file size whether the JPEG shares or marked JPEG shares but also achieves outstanding security performance and a high embedding capacity.

Digital images are easily corrupted during transmission. Most image denoising methods cannot perform well on restoring the secret image extracted from a corrupted stego image. To deal with this issue, we propose a new secret image restoration method with convex hull and elite opposition-based learning strategy. Specifically, the pixel distortion values of the corrupted secret image are calculated and used to classify the pixels into trustable pixels or untrusted pixels. For an untrusted pixel, a convex hull is generated by its context trustable pixels due to the irregular distribution of trustable pixels. The untrusted pixel in the convex hull is restored by the trustable pixels within the convex hull. The other untrusted pixels are restored using elite opposition-based learning strategy. The experimental results show that the proposed method outperforms some state-of-the-art methods regarding recovered secret image quality.

Reversible data hiding in encrypted images (RDHEI) is an essential data security technique. Most RDHEI methods cannot perform well in embedding capacity and security. To address this issue, we propose a new RDHEI method using Chinese remainder theorem-based secret sharing (CRTSS) and hybrid coding. Specifically, a hybrid coding is first proposed for RDH to achieve high embedding capacity. At the content owner side, a novel iterative encryption is designed to conduct block based encryption for perfectly preserving the spatial correlation of original blocks in their encrypted blocks. Then, the CRTSS with the constraints is exploited to generate multiple encrypted image shares, in which spatial correlations of the encrypted blocks are also preserved. Meanwhile, the CRTSS provides good security properties for the proposed method. Since there are strong spatial correlations in the blocks of each share, the data-hider can exploit the proposed hybrid coding to perform data embedding for improving capacity. On the receiver side, even if some shares are corrupted/missing, the original image can be losslessly recovered as long as enough uncorrupted marked shares are obtained. Experiment results show that the proposed RDHEI method outperforms some state-of-the-art methods, including some secret sharing (SS) based methods in terms of embedding capacity.

Abstract Hashing scheme is a high‐efficiency technique for processing massive images. Two critical metrics of the hashing scheme are discrimination and robustness, but most schemes do not get satisfied classification performance between them. This paper proposes a novel hashing scheme via image feature map and 2D PCA. First, the proposed scheme extracts local phase quantization (LPQ) features in the frequency domain and local ternary pattern (LTP) features in the spatial domain, and combines them to construct an image feature map. Second, the proposed scheme conducts dimension reduction via 2D PCA for learning features from the image feature map. Last, the learned features are compressed to generate the hash sequence. Performances are tested on open image datasets. The results demonstrate that the proposed scheme can make a good balance between discrimination and robustness. In addition, the classification and copy detection of the proposed scheme are both superior to those of some famous hashing schemes.

Reversible data hiding in encrypted images (RDHEI) is an effective technique of data security. Most state-of-the-art RDHEI methods do not achieve desirable payload yet. To address this problem, we propose a new RDHEI method with hierarchical embedding. Our contributions are twofold. (1) A novel technique of hierarchical label map generation is proposed for the bit-planes of plaintext image. The hierarchical label map is calculated by using prediction technique, and it is compressed and embedded into the encrypted image. (2) Hierarchical embedding is designed to achieve a high embedding payload. This embedding technique hierarchically divides prediction errors into three kinds: small-magnitude, medium-magnitude, and large-magnitude, which are marked by different labels. Different from the conventional techniques, pixels with small-magnitude/large-magnitude prediction errors are both used to accommodate secret bits in the hierarchical embedding technique, and therefore contribute a high embedding payload. Experiments on two standard datasets are discussed to validate the proposed RDHEI method. The results demonstrate that the proposed RDHEI method outperforms some state-of-the-art RDHEI methods in payload. The average payloads of the proposed RDHEI method are 3.4568 bpp and 3.6823 bpp for BOWS-2 dataset and BOSSbase dataset, respectively.

Abstract Image hashing is an efficient technique of many multimedia systems, such as image retrieval, image authentication and image copy detection. Classification between robustness and discrimination is one of the most important performances of image hashing. In this paper, we propose a robust image hashing with singular values of quaternion singular value decomposition (QSVD). The key contribution is the innovative use of QSVD, which can extract stable and discriminative image features from CIE L*a*b* color space. In addition, image features of a block are viewed as a point in the Cartesian coordinates and compressed by calculating the Euclidean distance between its point and a reference point. As the Euclidean distance requires smaller storage than the original block features, this technique helps to make a discriminative and compact hash. Experiments with three open image databases are conducted to validate efficiency of our image hashing. The results demonstrate that our image hashing can resist many digital operations and reaches a good discrimination. Receiver operating characteristic curve comparisons illustrate that our image hashing outperforms some state-of-the-art algorithms in classification performance.

Abstract Reversible data hiding (RDH) is a useful technique of data security. Embedding capacity is one of the most important performance of RDH for encrypted image. Many existing RDH algorithms for encrypted image do not reach desirable embedding capacity yet. To address this problem, a new RDH algorithm is proposed for encrypted image based on adaptive prediction error coding. The proposed RDH algorithm uses a block‐based encryption scheme to preserve spatial correlation of original image in the encrypted domain and exploits a novel technique called adaptive prediction error coding to vacate room for data embedding. A key contribution of the proposed RDH algorithm is the adaptive prediction error coding. It can efficiently vacate room from encrypted image block by adaptively coding prediction errors according to block content and thus contributes to a large embedding capacity. Many experiments on benchmark image databases are done to validate performance of the proposed RDH algorithm. The results show that the average embedding rates on the open databases of UCID, BOSSBase and BOWS‐2 are 1.7081, 2.4437 and 2.3083 bpp, respectively. Comparison results illustrate that the proposed RDH algorithm outperforms some state‐of‐the‐art RDH algorithms in embedding capacity.

Abstract Video hashing is a novel technique of multimedia processing and finds applications in video retrieval, video copy detection, anti-piracy search and video authentication. In this paper, we propose a robust video hashing based on discrete cosine transform (DCT) and non-negative matrix decomposition (NMF). The proposed video hashing extracts secure features from a normalized video via random partition and dominant DCT coefficients, and exploits NMF to learn a compact representation from the secure features. Experiments with 2050 videos are carried out to validate efficiency of the proposed video hashing. The results show that the proposed video hashing is robust to many digital operations and reaches good discrimination. Receiver operating characteristic (ROC) curve comparisons illustrate that the proposed video hashing outperforms some state-of-the-art algorithms in classification between robustness and discrimination.

Image hashing is an efficient technique of multimedia processing for many applications, such as image copy detection, image authentication, and social event detection. In this study, the authors propose a novel image hashing with visual attention model and invariant moments. An important contribution is the weighted DWT (discrete wavelet transform) representation by incorporating a visual attention model called Itti saliency model into LL sub‐band. Since the Itti saliency model can efficiently extract saliency map reflecting regions of attention focus, perceptual robustness of the proposed hashing is achieved. In addition, as invariant moments are robust and discriminative features, hash construction with invariant moments extracted from the weighted DWT representation ensures good classification performance between robustness and discrimination. Extensive experiments with open image datasets are done to validate the performances of the proposed hashing. The results demonstrate that the proposed hashing is robust and discriminative. Performance comparisons with some hashing algorithms are also conducted, and the receiver operating characteristic results illustrate that the proposed hashing outperforms the compared hashing algorithms in classification performance between robustness and discrimination.

Image hashing has attracted much attention of the community of multimedia security in the past years. It has been successfully used in social event detection, image authentication, copy detection, image quality assessment, and so on. This paper presents a novel image hashing with low-rank representation (LRR) and ring partition. The proposed hashing finds the saliency map by the spectral residual model and exploits it to construct the visual representation of the preprocessed image. Next, the proposed hashing calculates the low-rank recovery of the visual representation by LRR and extracts the rotation-invariant hash from the low-rank recovery by ring partition. Hash similarity is finally determined by L2 norm. Extensive experiments are done to validate effectiveness of the proposed hashing. The results demonstrate that the proposed hashing can reach a good balance between robustness and discrimination and is superior to some state-of-the-art hashing algorithms in terms of the area under the receiver operating characteristic curve.

Image encryption is a useful technique of image content protection. In this paper, we propose a novel image encryption algorithm by jointly exploiting random overlapping block partition, double spiral scans, Henon chaotic map, and Lü chaotic map. Specifically, the input image is first divided into overlapping blocks and pixels of every block are scrambled via double spiral scans. During spiral scans, the start-point is randomly selected under the control of Henon chaotic map. Next, image content based secret keys are generated and used to control the Lü chaotic map for calculating a secret matrix with the same size of input image. Finally, the encrypted image is obtained by calculating XOR operation between the corresponding elements of the scrambled image and the secret matrix. Experimental result shows that the proposed algorithm has good encrypted results and outperforms some popular encryption algorithms.

In this letter, a reversible data hiding (RDH) scheme by using adaptive pixel value prediction and adaptive embedding bin selection based on pixel-based pixel value ordering (PPVO) is proposed. Different from the previous PPVO based methods, each to-be-embedded pixel is predicted by its neighbor pixels, which are selected adaptively according to the complexity of its neighbor pixel values. Moreover, the value range of embedding bin is not constrained in our method. Especially, when the value of embedding bin is less than 0, its value is updated adaptively during the process of embedding and extracting secret bits. Our maximum embedding capacity (EC) is improved significantly due to unconstrained embedding bin. In addition, the optimal embedding bins can be selected to achieve highest visual quality under a given EC. Experimental results show that the proposed method outperforms some state-of-the-art PPVO based RDH methods.

We propose a novel perceptual image hashing based on weighted discrete wavelet transform (DWT) statistical features. This hashing converts input image into a normalized image by bi-linear interpolation and color space conversion, extracts edge image of the normalized image via Canny operator, and divides the edge image into non-overlapping blocks. For each block, a three-level 2D DWT is applied to obtain different sub-bands and the weighted sum of the DWT statistics of these sub-bands is calculated. Finally, image hash is generated by concatenating and quantizing these weighted DWT features. Similarity of image hashes is measured by Euclidean distance. The Copydays dataset and the Uncompressed Color Image Database (UCID) are both used to evaluate classification between robustness and discrimination. Receiver operating characteristics curve comparisons illustrate that our hashing is superior to some state-of-the-art algorithms in classification performance with respect to robustness and discrimination. The LIVE Image Quality Assessment Database is used to validate our application in reduced-reference image quality assessment. Experimental results show that our hashing has better performance in image quality assessment than two popular measures, i.e. peak signal-to-noise ratio and structural similarity.

Data hiding in encrypted image is a recent popular topic of data security. In this paper, we propose a reversible data hiding algorithm with pixel prediction and additive homomorphism for encrypted image. Specifically, the proposed algorithm applies pixel prediction to the input image for generating a cover image for data embedding, referred to as the preprocessed image. The preprocessed image is then encrypted by additive homomorphism. Secret data is finally embedded into the encrypted image via modular 256 addition. During secret data extraction and image recovery, addition homomorphism and pixel prediction are jointly used. Experimental results demonstrate that the proposed algorithm can accurately recover original image and reach high embedding capacity and good visual quality. Comparisons show that the proposed algorithm outperforms some recent algorithms in embedding capacity and visual quality.