Learning on multi-view data is a fundamental task, which integrates the information from different views to improve the final performance. It is also a basic task for learning on the long-tailed data in real applications, followed by the downstream tasks, i.e., classification. The existing works for trusted classification on multi-view data or long-tailed data usually aim to improve the final performance and dynamically consider the confidence of prediction for the data which is crucial in cost-sensitive domains. However, these methods pay few attentions to the pairwise trusted problem which considers the trusted pairs instead of trusted annotated data points. Besides, the problem of classification on long-tailed multi-view data has never been studied so far. In this work, we focus on the pairwise trusted problem on long-tailed multi-view classification and give a general framework, which considers the trusted pairs instead of trusted annotated data points. We then construct a specific example under the general framework and introduce a novel Enhanced Normal-Inverse Gamma distribution (ENIG). ENIG is a joint probabilistic distribution built on Dirichlet distribution and NIG. A novel combination rule based on ENIG for long-tailed multi-view data is also given, which adaptively integrates the long-tailed data from different views to achieve a consensus one at the level of evidence and effectively produces a trusted long-tailed multi-view classification result. Our method is robust and able to be dynamically aware of the uncertainty for the long-tailed data from each view. The accurate uncertainty can be induced by the proposed learning framework, leading to both robustness and reliability for classification on long-tailed multi-view data. Experimental results on different long-tailed multi-view datasets demonstrate the effectiveness of our method in terms of accuracy, robustness and reliability.

With the rapid development of large language models, the fine-tuning of downstream tasks has become a key problem. As a new learning paradigm, prompt learning can greatly reduce the number of updated parameters and effectively reduce the communication cost in federated learning scenarios by freezing the pre-trained model and fine-tuning soft prompt parameters. However, for the federated prompt tuning, the local prompt usually contains the user's personal information, and the text input of the user are easily leaked through the aggregation directly on the server. Meanwhile, the soft prompt is vulnerable to poisoning attacks launched by malicious users (such as backdoor). For the above problems, this paper proposes a privacy-preserving federated prompt fine-tuning against poisoning attacks. Specifically, we use homomorphic encryption to ensure the confidentiality of local prompts, and aggregate prompts under ciphertext to generate global prompts. Secondly, the proposed mechanism of backdoor detection is used to judge whether the prompt contains backdoors to resist the malicious attacks in the form of ciphertext. Both local and global prompts are invisible to the server during the entire training process. Finally, theoretical analysis and experimental results show that the accuracy error between the plaintext domain and the ciphertext domain is controlled within 2%-5% under backdoor attack, which effectively enhances the robustness of the model.

Recent advances have highlighted the potential of diffusion models in Video Anomaly Detection (VAD). Diffusion models are typically employed to generate negative instances to distinguish them from positive ones. However, the existing diffusion model architectures, generally based on the reconstruction of low-level noisy features, introduce spurious correlations due to shortcut learning, which undermines the robustness of anomaly detection. In this work, we leverage normal-specific representations to guide behavior restoration by aligning disentangled task-relevant representations within the diffusion model. we propose a normal representation-guided conditional diffusion model for unsupervised VAD by aligning normal-specific representations. Inspired by prior knowledge of anomaly discrimination, we decompose normal behavior features into normal-specific and VAD-irrelevant representations into independent channels based on contrastive learning. We introduce a group-supervised learning strategy in learning patch-wise generation guided by normal-specific representations. A gradient-based representation alignment loss enforces the alignment between normal semantics and the target patches. This process enables the diffusion model to understand normal patterns for anomaly detection. Extensive experimental results conducted on VAD benchmarks demonstrate the effectiveness of our methods.

Unrestricted adversarial attacks that modify the attributes or content of images have demonstrated significant potential. Among unrestricted adversarial attacks, color modification attacks that targeting image color have increasingly obtained attention from researchers. By perturbing the color information of images, color modification attacks can be more imperceptible to human vision compared with traditional adversarial perturbations constrained by$ L_{p}$-norm. Furthermore, the alteration of image attributes significantly disrupts the low-level features extracted by Deep Neural Networks (DNNs), effectively enhancing the transferability. However, current work focus solely on global image attributes, which limit the attack effectiveness. Therefore, this paper proposes Local-Global-Local (LGL), a novel color modification attack framework that incorporates spatial and color information of the image. The color distribution of the image is initialized at first, and then the color filter and spatial masks are used to adjust the global and local color distribution in more detail. Experimental results show that the proposed method achieves superior performance in terms of adversariality and transferability, while also demonstrating robustness against defense methods.

Multi-view clustering has gained increasing attention by utilizing the complementary and consensus information across views. To alleviate the computation cost for the existing multi-view clustering approaches on datasets with large scales, studies based on anchor have been presented. Although extensively adopted in the real scenarios, most of these works ignore to learn an integral subspace revealing the cluster structure with anchors from different views being aligned, where the centroid and cluster assignment matrix can be directly achieved based on the integral subspace. Moreover, these works neglect to perform the alignment among anchors and integral subspace learning in a unified model on the incomplete multi-view dataset. Then the mutual improvements among aligning anchors and learning integral subspace are not guaranteed in optimizing the objective function, which inevitably limit the representation ability of the model and result in the suboptimal clustering performance. In this paper, we propose a novel anchor learning method for incomplete multi-view dataset termed Scalable One-pass incomplete Multi-view clustEring by Aligning anchorS (SOME-AS). Specifically, we capture the complementary information among multiple views by building the anchor graph for each view on the incomplete dataset. The integral subspace reflecting the cluster structure is learned with the alignment among anchors from different views being considered. We build the cluster assignment and centroid representation with orthogonal constraint to approximate the integral subspace. Then the subspace itself and the partition are simultaneously taken into account in this manner. Besides, the mutual improvements among aligning anchors and learning integral subspace are able to be ensured. Experiments on several incomplete multi-view datasets validate the efficiency and effectiveness of SOME-AS.

Multi-view clustering aims to improve the final performance by taking advantages of complementary and consistent information of all views. In real world, data samples with partially available information are common and the issue regarding the clustering for incomplete multi-view data is inevitably raised. To deal with the partial data with large scales, some fast clustering approaches for incomplete multi-view data have been presented. Despite the significant success, few of these methods pay attention to learning anchors with high quality in a unified framework for incomplete multi-view clustering, while ensuring the scalability for large-scale incomplete datasets. In addition, most existing approaches based on incomplete multi-view clustering ignore to build the relation between anchor graph and similarity matrix in symmetric nonnegative matrix factorization and then directly conduct graph partition based on the anchor graph to reduce the space and time consumption. In this paper, we propose a novel fast incomplete multi-view clustering method for the data with large scales, termed Fast Incomplete Multi-view clustering by flexible anchor Learning (FIML), where graph construction, anchor learning and graph partition are simultaneously integrated into a unified framework for fast incomplete multi-view clustering. To be specific, we learn a shared anchor graph to guarantee the consistency among multiple views and employ a adaptive weight coefficient to balance the impact for each view. The relation between anchor graph and similarity matrix in symmetric nonnegative matrix factorization can also be built, i.e., each entry in the anchor graph can characterize the similarity between the anchor and original data sample. We then adopt an alternative algorithm for solving the formulated problem. Experiments conducted on different datasets confirm the superiority of FIML compared with other clustering methods for incomplete multi-view data.

As a leading unsupervised classification algorithm in artificial intelligence, multi-view subspace clustering segments unlabeled data from different subspaces. Recent works based on the anchor have been proposed to decrease the computation complexity for the datasets with large scales in multi-view clustering. The major differences among these methods lie on the objective functions they define. Despite considerable success, these works pay few attention to guaranting the robustness of learned consensus anchors via effective manner for efficient multi-view clustering and investigating the specific local distribution of cluster in the affine subspace. Besides, the robust consensus anchors as well as the common cluster structure shared by different views are not able to be simultaneously learned. In this paper, we propose Robust Consensus anchors learning for efficient multi-view Subspace Clustering (RCSC). We first show that if the data are sufficiently sampled from independent subspaces, and the objective function meets some conditions, the achieved anchor graph has the block-diagonal structure. As a special case, we provide a model based on Frobenius norm, non-negative and affine constraints in consensus anchors learning, which guarantees the robustness of learned consensus anchors for efficient multi-view clustering and investigates the specific local distribution of cluster in the affine subspace. While it is simple, we theoretically give the geometric analysis regarding the formulated RCSC. The union of these three constraints is able to restrict how each data point is described in the affine subspace with specific local distribution of cluster for guaranting the robustness of learned consensus anchors. RCSC takes full advantages of correlation among consensus anchors, which encourages the grouping effect and groups highly correlated consensus anchors together with the guidance of view-specific projection. The anchor graph construction, partition and robust anchor learning are jointly integrated into a unified framework. It ensures the mutual enhancement for these procedures and helps lead to more discriminative consensus anchors as well as the cluster indicator. We then adopt an alternative optimization strategy for solving the formulated problem. Experiments performed on eight multi-view datasets confirm the superiority of RCSC based on the effectiveness and efficiency.

Federated Learning (FL) is increasingly adopted in Internet of Things (IoT) ecosystems, where distributed devices collaboratively train machine learning models while preserving data privacy. Well-trained models have high commercial value. If stolen, it will severely harm the interests of the model owner. In FL, a free-rider client can avoid contributing data or computing resources by establishing a deceptive local model and illegally obtaining the valuable global model for free, undermining the central server’s interests. While existing model watermarking methods primarily concentrate on identifying deep learning model misuse, they fail to adequately tackle the issue of identifying free-riders. To address such an issue, this paper presents a box-free watermarking scheme that enables multiple clients who participated in the training to embed private watermarks within the jointly trained federated deep learning model, while the free rider cannot if he did not participate in the training. To avoid conflicts between different clients, each client selects a unique trigger class and embeds watermarks into the global model during the training process. Furthermore, we propose a memory-enhancing local updating strategy to effectively fuse different watermarks into the global model. The proposed method can assist the center in identifying free-rider clients while also safeguarding the FL model’s intellectual property rights. The efficiency of the embedded watermarks is validated by experiments conducted on different models, and the performance of the resilience across various training settings and the robustness against different watermark removal methods are also tested.

Existing steganalysis methods perform well under ideal conditions but encounter challenges in real-world scenarios with uncertain embedding rates. This paper proposes a novel steganalysis network based on refocusing learning to enhance detection accuracy for blind embedding rate contexts. The proposed network incorporates a detail gradient guided module (DGGM) to capture subtle spatial changes, which are integrated into multiple layers to ensure the model consistently focuses on these critical details. Additionally, a two-stage training strategy is employed. It is initially trained to obtain a pre-trained model, while the second stage optimizes the pre-trained convolutional kernels by refocusing learning. This approach enhances the feature extraction ability by indirectly strengthening connections between different channels. Experimental results demonstrate that the proposed method achieves strong detection performance across various spatial and JPEG domain steganographic algorithms with blind embedding rates, outperforming SRNet, EfficientNet-B4, and DATNet in detection accuracy.

To address the issues of privacy leakage and copyright infringement in screen-camera scenarios, we propose a robust image watermarking method, which incorporates kolmogorov–arnold network (KAN) blocks and a simulation-enhanced noise pool to resist screen-camera noise attacks. Specifically, we first modify the traditional convolutional blocks for processing high-dimensional features in the U-Net-based encoder to KAN blocks. This operation enhances the ability of encoder to model nonlinear relationships between complex features, while preserving global structure of the original image and minimizing the damage to local details caused by watermark embedding, thereby improving visual quality of the watermarked image. Additionally, to enhance the robustness of the proposed method against complex screen-camera noise attacks, a simulation-enhanced noise pool containing mathematical models and a deep noise simulation network, called NSim-Net, is designed. Especially, in NSim-Net, adversarial training between the simulator based on the improved U-Net and the discriminator based on PatchGAN effectively improves the ability to simulate complex noise. Experimental results demonstrate that, compared to typical screen-camera resilient watermarking methods, the watermarked image generated by the proposed method achieves a maximum peak signal-to-noise ratio (PSNR) improvement of 4.78 dB. Furthermore, based on our simulation-enhanced noise pool, the watermark extraction accuracy of the proposed method exceeds 98% under various screen-camera noise attacks.

Generative artificial intelligence has made great progress in enabling clients to create a variety of realistic visual content (such as images, videos and audios), where diffusion model as an emerging generative model can obtain higher quality images than generative adversarial networks (GANs). For resource-constrained devices, high-definition images can be generated by outsourcing the AI model to the server, but the client’s local prompts and generated images may contain private information, increasing the risk of privacy disclosure. In order to alleviate these concerns, we adopt the idea of split learning and homomorphic encryption technology to ensure the privacy of data and prevent the attacker from stealing. Specifically, adopt homomorphic encryption to ensure the confidentiality of the text embedding and prevent the server from obtaining the prompt and text embedding. Secondly, during the process of denoising, a secure cross-attention mechanism is designed for ciphertext based on the embedding matrix to ensure the normal follow-up steps and prevent denoising model parameters from leaking to clients. In addition, since the decoder is deployed locally, the image generated by the denoising model is latent spatial features rather than pixel spatial features, so the generated image is not visible to the server. Finally, through the theoretical analysis and quantitative index of experiments, it is proved that the image generated under ciphertext prompt is almost the same as the image generated under plaintext prompt, which indicates the security and effectiveness of the proposed protocol.

In recent years, due to the rich parameters contained in deep neural network (DNN) models, researchers have proposed DNN model steganography, using DNN models as carriers. In this paper, we propose a constructive DNN model steganography method based on parameter initialization. Unlike existing DNN model steganography methods that embed secret information into a pre-trained DNN model, we generate parameters containing secret information through parameter initialization for a DNN model structure, and the embedded secret information can still be extracted after model training. Specifically, we first generate the model parameters needed for the DNN model structure through a secret information-driven encoder, and then we jointly train the encoder and decoder to ensure the correct extraction of secret information. Additionally, we introduce a noise layer to simulate the model training process to guarantee the robustness of our method. Experimental results demonstrate that our method not only achieves high hiding capacity but also exhibits satisfactory stealthiness and robustness. Furthermore, our method is generalizable, which can be applied to various network structures, such as multilayer perceptron (MLP), convolutional neural networks (CNNs), recurrent neural networks (RNNs), and Transformers.

In recent years, neural network models have been widely used in many tasks, however, tampering operations from malicious attackers, e.g., backdoor attacks and parameter malicious tampering, can easily degrade the model performance or cause a malfunction. To protect the integrity of neural network model, in this paper, we propose a neural network watermarking scheme with hierarchical recoverability (NNWHR), which not only can identify and locate the tampered parameters, but also can recover the tampered parameters in a hierarchical way. Detailedly, the parameters of to-be-protected network layers are first sorted according to the parameter importances, which are calculated through a specifically designed strategy for parameter evaluation. Then, the reference sharing mechanism is used to generate more number of recovery bits and provide greater perfect recovery probabilities for the parameters with higher importances, which can also deal with larger tampering rates through bit interleaving. Finally, the recovery bits and the authentication bits of model parameters are incorporated as watermark bits and embedded into the model redundant space after scrambling. Experimental results show that our scheme can locate tampered model parameters and recover corresponding model performance with satisfactory accuracy, and can also be applied to eliminate backdoor attacks. In addition, our scheme exhibits satisfactory generalizability, which makes it applicable to various types of neural networks.

Ensemble clustering fuses a set of base clusterings and shows promising capability in achieving more robust and better clustering results. The existing methods usually realize ensemble clustering by adopting a co-association matrix to measure how many times two data points are categorized into the same cluster based on the base clusterings. Though great progress has been achieved, the obtained co-association matrix is constructed based on the combination of different connective matrices or its variants. These methods ignore exploring the inherent latent space shared by multiple connective matrices and learning the corresponding co-association matrices according to this latent space. Moreover, these methods neglect to learn discriminative connective matrices, explore the high-order relation among these connective matrices and consider the latent space in a unified framework. In this paper, we propose a Latent spacE leArning baseD Ensemble Clustering (LEADEC), which introduces the latent space shared by different connective matrices and learns the corresponding connective matrices according to this latent space. Specifically, we factorize the original multiple connective matrices into a consensus latent space representation and the specific connective matrices. Meanwhile, the orthogonal constraint is imposed to make the latent space representation more discriminative. In addition, we collect the obtained connective matrices based on the latent space into a tensor with three orders to investigate the high-order relations among these connective matrices. The connective matrices learning, the high-order relation investigation among connective matrices and the latent space representation learning are integrated into a unified framework. Experiments on seven benchmark datasets confirm the superiority of LEADEC compared with the existing representive methods.

Designing an end-to-end encryption method for images using the non-linear properties of deep neural networks (DNNs) has gradually attracted the attention of researchers. In this article, we introduce a new framework for DNN-based image encryption that embeds a plaintext image as a secret message into a random noise to obtain a ciphertext image. Based on this, we propose an end-to-end robust image encryption method based on the invertible neural network (INN), which can realize secure encryption and resistance to common image processing attacks. Specifically, the INN is exploited as the shared-parameter encoder and decoder to achieve end-to-end encryption and decryption. The ciphertext image can be obtained through the forward process of the INN by inputting the plaintext image and the key, while the decrypted image can be obtained through the backward process of the INN by inputting the ciphertext image and the key. To enhance the security of our method, we design an information reinforcement module to guarantee the encryption effect and the sensitivity of the key. In addition, to improve the robustness of our method, an attack layer is employed for noise simulation training. Experimental results show that our method not only can realize secure encryption but also can achieve the robustness such as resisting JPEG compression, Gaussian noise, scaling, mean filtering, and Gaussian blurring effectively.

Federated learning (FL) is widely used in neural network-based deep learning, which allows multiple users to jointly train a model without disclosing their data. However, the data quality of the users is not uniform, and some users with poor computing ability and outdated equipments called irregular ones may collect low-quality data and thus reduce the accuracy of the global model. In addition, the untrusted server may return wrong aggregation results to cheat the users. To solve these problems, we propose a verifiable privacy-preserving FL protocol with irregular users (VPFLI) based on single server. The protocol is privacy-preserving for the untrusted server and it is proved secure based on drop-tolerant homomorphic encryption. For low-quality datasets, their proportion would be decreased in the aggregation results in order to ensure the accuracy of the global model. Also, the aggregation results can be effectively verified by the users based on linear homomorphic hash. Moreover, VPFLI is proposed based on single server, which is more applicable in reality compare with the previous ones based on two non-colluding servers. The experiments show that VPFLI improves the accuracy of the model from 83.5% to 91.5% based on MNIST dataset compared to the traditional FL protocols.

Remote heart rate (HR) estimation via facial videos has emerged as an attractive application for healthcare monitoring. Long short-term memory (LSTM) is a kind of deep recurrent neural network architecture used for modeling sequential information, which can be utilized for remote HR estimation. Outsourcing computation is an emerging paradigm for enterprises or individuals with a huge volume of private data but limited computing power for data modeling and analysis, but the risks of privacy leakage cannot be ignored. Prior privacy-preserving LSTM-based protocols only protect sensitive data or model parameters, or approximate nonlinear functions with somewhat utility degradation. To mitigate the aforementioned issues, we propose a verifiable outsourcing protocol of LSTM training for HR estimation (VOLHR) based on batch homomorphic encryption, which not only ensures the confidentiality of the local data and model parameters but also guarantees the verifiability of the model predictions to resist the deceptive attacks. Theoretical analysis proves that VOLHR provides a higher level of privacy protection than the prior works. Computational cost analysis demonstrates that VOLHR can reduce the computational overhead greatly compared with the original LSTM models with different structures. To exhibit the practical utility, we implement VOLHR on two real-world data sets for HR estimation. Extensive evaluations demonstrate that VOLHR achieves almost the same performance as the original LSTM model and outperforms prior related protocols.

The Vehicular Social Networks (VSNs) provide passengers, drivers and vehicles with multiple services, such as safe driving, data sharing and traffic management. However, transmitting data in VSNs can expose information such as the user’s identity and location. Malicious users who tamper with shared data can even cause serious traffic accidents. Considering the privacy protection and secure transmission of shared data in VSNs, we propose a blockchain-based and fog computing-assisted data access control scheme (BFDAC). We combine the multi-authority CP-ABE algorithm with the consortium blockchain to avoid the security and trust issues in the form of centralized key management, and outsource part of the decryption calculations to roadside units (RSUs) as fog nodes to realize lightweight calculation for users. Our BFDAC scheme also supports user tracking and revocation, while users can also revoke shared data saved in the cloud. Security analysis shows that the BFDAC scheme can effectively protect the shared data. Experiments show that our BFDAC scheme reduces 32.8% in storage cost, 9.5% in encryption cost, and 57.1% in outsourced decryption cost compared to previous ones.

Object detection on remote sensing images (RSIs) is a critical component of remote sensing image processing techniques. Nonetheless, as the complexity of the model structure increases, more training data is required to prevent a severe decline in object detection performance. This requirement highlights the potential of few-shot object detection techniques. At present, few researches have been investigated for few-shot object detection on RSIs. To address this situation, we propose the saliency-guided multi-head distribution calibration network (SMDC-Net) by building upon the existing fine-tuning-based two-stage few-shot object detection approach. We employ an RSIs salient object detection (SOD) branch that leverages the feature of RSIs to predict the saliency maps. These predicted saliency maps are used as an attention map to augment the feature of RSIs. Meanwhile, to guarantee the ability of the network to identify objects within both the base and novel categories accurately, we design a multi-head detector which can recognize objects within base and novel categories separately while introducing a consistency loss to supervise the consistency of the multi-head predicted labels. We evaluate the performance of two widely recognized RSIs datasets (i.e., DIOR and NWPU VHR-10) within various experimental settings for comparison and ablation analysis. The results illustrate that SMDC-Net enhances the detection performance of novel categories approximately 3% compared to the state-of-the-art MM-RCNN, while preserving the performance of base categories.

Classic unsupervised anomaly detection learns normative patterns from normal behavior and assumes that unforeseen anomalous behavior will result in significant prediction deviations. However, anomaly detection in specific situations faces challenges in detecting ambiguous behavior in which the abnormal representation is not particularly intuitive. Existing anomaly detection approaches perform poorly for ambiguous behavior due to limited normative representational capacity, resulting in a narrow normality gap. We observe that the ambiguity of behavior comes from the contradiction between the properties of appearance and motion modalities. In this paper, we propose a novel memory-guided autoencoder named appearance-motion synergy autoencoder to detect anomalous behavior by event prediction. To address the above challenge, we leverage the synergy of the normative appearance-motion modalities to strengthen the representation of normative patterns and improve the detection of ambiguous behavior. Specifically, we design the memory networks with dynamic fusion mechanisms to integrate the correlated appearance-motion information and to remember normal patterns. A consistency measurement unit is designed to optimize the consistency of normative appearance-motion features via a joint distribution measurement pool. A larger normality gap in detecting ambiguous behavior in our approach enhances the abnormal detection capability. Extensive experiments demonstrate our superiority in detecting anomalous behavior.

Video watermarking based on frequency domain is proved to have good invisibility and robustness. However, most of the existing video watermarking schemes embed watermarks in the frequency domain based on subblock segmentation, while ignoring the variation relationship between video space and frequency domain features. Therefore, it is difficult to achieve robust authentication in complex application scenarios. In this paper, a ring subband is constructed in DT-CWT domain as the watermark embedding region by analyzing the relationship between video space and frequency domain characteristics under multiple attacks. Subsequently, the double watermark is embedded by modifying the DCT coefficient of the ring subband, with the copyright watermark alternately and repeatedly embedded within the ring subband, and the synchronous watermark is embedded in the outermost concentric circle. In addition, visual encryption (VC) and piecewise linear chaotic mapping (PLCM) methods are used to encrypt the watermark before it is embedded in the concentric rings, and two shared images are generated, one for the watermark embedding stage and the other for the watermark extraction stage. Experimental results demonstrate that the proposed scheme can resist common attacks, such as noise, JPEG compression, rotation, scaling, time synchronization attacks, and its robustness surpasses existing discrete wavelet transform (DWT) and DT-CWT based video watermarking schemes under complex attack scenarios.

Linguistic steganography (LS) aims to embed secret information into a highly encoded text for covert communication. It can be roughly divided to two main categories, i.e., modification based LS (MLS) and generation based LS (GLS). MLS embeds secret data by slightly modifying a given text without impairing the meaning of the text, whereas GLS uses a well trained language model to directly generate a text carrying secret data. A common disadvantage for MLS methods is that the embedding payload is very small, whose return is well preserving the semantic quality of the text. In contrast, GLS enables the data hider to embed a large payload, which has to pay the high price of uncontrollable semantics. In this article, we propose a novel LS method to modify a given text by pivoting it between two different languages and embed secret data using a semantic-aware information encoding strategy. Our purpose is to alter the expression of the given text, enabling a large payload to be embedded while keeping the semantic information unchanged. Experiments have shown that the proposed work not only achieves a large embedding payload, but also shows superior performance in maintaining the semantic consistency and resisting linguistic steganalysis.

Multi-view clustering usually attempts to improve the final performance by integrating graph structure information from different views and methods based on anchor are presented to reduce the computation cost for datasets with large scales. Despite significant progress, these methods pay few attentions to ensuring that the cluster structure correspondence between anchor graph and partition is built on multi-view datasets. Besides, they ignore to discover the anchor graph depicting the shared cluster assignment across views under the orthogonal constraint on actual bases in factorization. In this paper, we propose a novel Dual consensus Anchor Learning for Fast multi-view clustering (DALF) method, where the cluster structure correspondence between anchor graph and partition is guaranteed on multi-view datasets with large scales. It jointly learns anchors, constructs anchor graph and performs partition under a unified framework with the rank constraint imposed on the built Laplacian graph and the orthogonal constraint on the centroid representation. DALF simultaneously focuses on the cluster structure in the anchor graph and partition. The final cluster structure is simultaneously shown in the anchor graph and partition. We introduce the orthogonal constraint on the centroid representation in anchor graph factorization and the cluster assignment is directly constructed, where the cluster structure is shown in the partition. We present an iterative algorithm for solving the formulated problem. Extensive experiments demonstrate the effectiveness and efficiency of DALF on different multi-view datasets compared with other methods.

Multi-view clustering has gained great progress recently, which employs the representations from different views for improving the final performance. In this paper, we focus on the problem of multi-view clustering based on the Markov chain by considering low-rank constraints. Since most existing methods fail to simultaneously characterize the relations among different entries in a tensor from the global perspective and describe local structures of similarity matrices of a tensor, we propose a novel Flexible Tensor Learning for Multi-view Clustering with the Markov chain (FTLMCM) to solve this problem. We also construct transition probability matrices based on the Markov chain to fully utilize the connection between the Markov chain and spectral clustering. Specifically, the low-rank constraints of the tensor, the frontal slices and the lateral slices of the tensor are imposed on the objective function of the proposed method to achieve these goals. Besides, these three constraints can be optimized jointly to achieve mutual refinement. FTLMCM also uses the tensor rotation to better explore the relationships among different views. We formulate FTLMCM as a problem of low-rank tensor recovery and solve it with the augmented Lagrangian multiplier. Experiments on six different benchmark data sets under six metrics demonstrate that the proposed method is able to achieve better clustering performance.

Due to the limited number of stable image feature descriptors and the simplistic concatenation approach to hash generation, existing hashing methods have not achieved a satisfactory balance between robustness and discrimination. To this end, a novel perceptual hashing method is proposed in this paper using feature fusion of fractional-order continuous orthogonal moments (FrCOMs). Specifically, two robust image descriptors, i.e., fractional-order Chebyshev Fourier moments (FrCHFMs) and fractional-order radial harmonic Fourier moments (FrRHFMs), are used to extract global structural features of a color image. Then, the canonical correlation analysis (CCA) strategy is employed to fuse these features during the final hash generation process. Compared to direct concatenation, CCA excels in eliminating redundancies between feature vectors, resulting in a shorter hash sequence and higher authentication performance. A series of experiments demonstrate that the proposed method achieves satisfactory robustness, discrimination and security. Particularly, the proposed method exhibits better tampering detection ability and robustness against combined content-preserving manipulations in practical applications.

In this article, an effective print-camera (P-C) resistant image watermarking scheme is proposed. To achieve watermark robustness, most of existing works try to simulate P-C noise by a sophisticated math model. However, the diversity of P-C noises in the real world is ignored, and the watermarked image may not attain a good balance between high robustness and low distortion. To address the problem, we construct an efficient end-to-end network architecture for watermark embedding and extraction. To be specific, a deep noise simulation network (NSN) is designed to simulate the fusion process of real P-C noises, which can help to generate high-robust watermarked image. Also, a multitask loss function based on just-noticeable-difference (JND) is proposed to conduct constrained learning for residual image containing watermark information, thus, the distortion of generated watermarked image can be significantly reduced. Experimental results show that our scheme can achieve high robustness against P-C process while maintaining a satisfactory watermark capacity and visual quality of watermarked image.

Despite the great success of deep neural networks for style transfer tasks, the entanglement of content and style in images leads to more style information not being captured. To tackle this problem, a novel style disentanglement network is proposed to transfer multi-source style elements. Specifically, we specialize in designing a learnable content style separation module, which can efficiently extract content and style components from images in the latent space. This method differs from the previous approaches by predefining content and style layers in the network. Under the condition of content and style separation, we continue to propose the multi-style swap module, which allows the content image to match more style elements. Additionally, by introducing alternate training strategies for the main and auxiliary decoders as well as style disentanglement loss, the stylized results look very similar to the original artworks. Experimental results demonstrate the superiority of our proposed method compared with existing schemes.

Traditionally, art images have to be restored by professionals for a very long time. It is also possible to maintain the artistic value of damaged art images by digitizing them and restoring them through computer-aided means. However, existing advanced image inpainting methods are mainly intended for natural images and are not suitable for art images. Thus, we propose a novel style-guided dual-branch inpainting network (SDI-Net) to address the above-mentioned issue. Specifically, our SDI-Net consists of a style reconstruction (SR) branch and a style inpainting (SI) branch, in which the SR branch provides intermediate supervision (style and content supervision) for the SI branch. The SI branch performs art image inpainting with a coarse-to-fine approach. At the coarse inpainting stage, the content and style of art image are separated and preliminarily inpainted under the supervision of SI branch. In addition, we propose a class style learning (CSL) module to inpaint the style feature guided by the style label, which can provide more effective brushstrokes from the same class of art images. The coarse inpainted results can be obtained by fusing the inpainted style feature with the inpainted content feature. At the fine inpainting stage, a style attention (SA) module is proposed in the decoder to further refine the coarse inpainted results. We employ the style loss, the content loss, the multi-class style adversarial loss, and the reconstruction loss to jointly train the proposed SDI-Net. A variety of experiments demonstrate the effectiveness of the proposed method, which allows the filled brushstrokes to appear as realistic as possible.

Outsourced inference services have greatly promoted the popularization of deep learning, and neural network models can help users customize a series of personalized applications, e.g., face recognition, image classification, etc. However, untrustworthy service providers also bring a variety of security issues, such as data privacy, network model privacy, etc. Meanwhile, the malicious example will result in the output of model incorrectly. For the above concerns, this paper proposes a privacy-reserving neural network inference against adversarial example attack (PPNNI) under two non-colluding cloud servers, where a series of efficient security protocols are designed to realize private prediction based on the additive secret sharing protocol. In the semi-honest model, the servers implement the neural network's private inference in the context of an unknown input and network model. Moreover, in order to prevent malicious examples from affecting the model accuracy, we produce a method of kernel density estimation and uncertainty value in the last layer of the hidden layer to determine whether the input is adversarial examples in the domain of ciphertext. The proposed PPNNI protocol is the first effort to efficiently detect adversarial attack behaviors under the ciphertexts. The theoretical analysis illustrate that the protocol can guarantee the privacy of input data, model parameters and the inference result. The results of the experiments demonstrate that the model accuracy are about 89% and 83% respectively with malicious examples on MNIST and CIFAR10 in the domain of ciphertext and they are nearly same as 91% and 85% in the domain of plaintext, which shows the effectiveness of our protocol.

Thriving underwater applications demand efficient extreme compression technology to realize the transmission of underwater images (UWIs) in very narrow underwater bandwidth. However, existing image compression methods achieve inferior performance on UWIs because they do not consider the characteristics of UWIs: (1) Multifarious underwater styles of color shift and distance-dependent clarity, caused by the unique underwater physical imaging; (2) Massive redundancy between different UWIs, caused by the fact that different UWIs contain several common ocean objects, which have plenty of similarities in structures and semantics. To remove redundancy among UWIs, we first construct an exhaustive underwater multi-scale feature dictionary to provide coarse-to-fine reference features for UWI compression. Subsequently, an extreme UWI compression network with reference to the feature dictionary (RFD-ECNet)1is creatively proposed, which utilizes feature match and reference feature variant to significantly remove redundancy among UWIs. To align the multifarious underwater styles and improve the accuracy of feature match, an underwater style normalized block (USNB) is proposed, which utilizes underwater physical priors extracted from the underwater physical imaging model to normalize the underwater styles of dictionary features toward the input. Moreover, a reference feature variant module (RFVM) is designed to adaptively morph the reference features, improving the similarity between the reference and input features. Experimental results on four UWI datasets show that our RFD-ECNet is the first work that achieves a significant BDrate saving of 31% over the most advanced VVC.

Neural style transfer (NST) has attracted many research interests recent years. The existing NST schemes could only generate one stylized image from a content-style image pair. They are weak in creating diverse and personalized artistic styles. On the other hand, the stylized images could easily be stolen and illegally redistributed when shared online, which has not been addressed at all in the existing NST schemes. In this paper, we propose a personalized and watermark-guided style transfer network (PWST-Net) to tackle the aforementioned issues. Our PWST-Net could generate diverse stylized images from a content-style image pair using different personalization keys. Once the style transfer is done, our stylized images are with watermarks naturally embedded for copyright protection. We propose a novel style encoder in our PWST-Net to progressively generate the stylized images, which contains a Guided Fusion (GF) block and a Style Transformation (ST) block. The GF block generates a coarse stylized image based on a personalized direction field that is specific to a personalization key and the style image. The ST block refines the coarse stylized image into the final stylized image. It embeds a watermark into the deep feature space of the stylized image during the style transfer. To make the stylized images more diverse, we further propose a new personalization loss for training our PWST-Net. Various experiments demonstrate the effectiveness of our proposed method for generating personalized and watermarked stylized images, which also outperforms the state-of-the-art NST schemes in terms of artistic visual appearance.

Neural style transfer (NST) can quickly produce impressive artistic images, which allows ordinary people to become painter. The brushstrokes of stylized images created by the current NST methods are often unpredictable, which does not conform to the logic of the artist's drawing. At the same time, the style distribution of the generated stylized image texture differs from the real artwork. In this paper, we propose an interactive image style transfer network (IIST-Net) to overcome the above limitations. Our IIST-Net can generate stylized results for brushstrokes in arbitrary directions guided by graffiti curves. The style distribution of these stylized results is closer to the real-life artwork. Specifically, we design an Interactive Brush-texture Generation (IBG) module in IIST-Net to progressively generate controllable brush-textures. Then, two encoders are introduced to embed the interactive brush-textures into the content image in the deep space for producing the fused content feature map. The Multilayer Style Attention (MSA) module is proposed to further distill multi-scale style features and transfer them to the fused content feature map for obtaining the final stylized feature map with controllable brushstrokes. Additionally, we adopt the content loss, style loss, adversarial loss and contrastive loss to jointly supervise the proposed network. Experimental comparisons have demonstrated the effectiveness of our proposed method for creating controllable and realistic stylized images.

Since the significant advancements and open-source support of latent diffusion models (LDMs) in the field of image generation, numerous researchers and enterprises start fine-tuning the pre-trained models to generate specialized images for different objectives. However, the criminals may turn their attention to generate images by LDMs and then carry out illegal activities. The watermarking technique is a typical solution to deal with this problem. But, the post-hoc watermarking methods can be easily escaped to obtain the non-watermarked images, and the existing watermarking methods designed for LDMs can only embed a fixed message, i.e., the to-be-embedded message cannot be changed unless retraining the model. Therefore, in this work, we propose an end-to-end watermarking method based on the encoder-decoder (ENDE) and message-matrix. The message can be embedded into generated images through fusing the message-matrix and intermediate outputs in the forward propagation of image generation based on LDM. Thus, the message can be flexibly changed by utilizing the message-encoder to generate message-matrix, without training the LDM again. On the other hand, the security mechanism in our watermarking method can defeat the attack that the users may escape the message-matrix usage during image generation. A series of experiments demonstrate the effectiveness and the superiority of our watermarking method compared with SOTA methods.

Few-shot semantic segmentation is a technique that is receiving increasing attention. The aim of this approach is to enable models to segment objects with a few support images (usually 1, 5, 10, etc.). At present, few-shot semantic segmentation has made great progress in the field of Natural Scene Image (NSI), but these methods cannot be applied directly to the field of Remote Sensing Image (RSI). In order to overcome this challenge, we propose a novel semantic segmentation network structure that integrates prototype information with global edge information to achieve more accurate prototype matching results. In addition, we design a comprehensive weighted loss function to monitor the training process to help overcome the challenges. Results of the performance comparison with state-of-the-art few-shot semantic segmentation methods demonstrate the superiority of the proposed method.

Subspace clustering is useful for clustering data points according to the underlying subspaces. Many methods have been presented in recent years, among which Sparse Subspace Clustering (SSC), Low-Rank Representation (LRR) and Least Squares Regression clustering (LSR) are three representative methods. These approaches achieve good results by assuming the structure of errors as a prior and removing errors in the original input space by modeling them in their objective functions. In this paper, we propose a novel method from an energy perspective to eliminate errors in the projected space rather than the input space. Since the block diagonal property can lead to correct clustering, we measure the correctness in terms of a block in the projected space with an energy function. A correct block corresponds to the subset of columns with the maximal energy. The energy of a block is defined based on the unary column, pairwise and high-order similarity of columns for each block. We relax the energy function of a block and approximate it by a constrained homogenous function. Moreover, we propose an efficient iterative algorithm to remove errors in the projected space. Both theoretical analysis and experiments show the superiority of our method over existing solutions to the clustering problem, especially when noise exists.

Most of the existing image steganography methods are content adaptive steganography, which prefer to modify the pixel values of texture information-rich regions, making them more difficult to be detected. Many steganography detectors have been developed using a CNN network with selected channels. However, the utilization and processing of the prior information inside the network may not be completely adequate. To address such problem, we propose a strategy based on a spatial and channel multiple attention mechanism for improving the accuracy of the network. To further exploit the texture information of the image itself, spatial attention is used throughout the network, which is achieved by fusing the Sobel operator features with the embedded probability map in a suitable way. On the other hand, channel attention uses the relationship between two-dimensional DCT and global average pooling to weight the features. Experimental results show that our strategy has competitive performance in both spatial and DCT domains on the ALASKA #2 dataset.

Watermarking by Zernike moments has been proven to be effective in providing high rotational resistance. However, due to the high computational complexity, the conventional video watermarking methods using Zernike moments are developed for videos with low resolution. Moreover, according to the properties of Zernike moments, only the matrices of equal height and width can be calculated since the inscribed circle of the original image matrix is selected as the area to be processed, but most of the available videos on the Internet do not meet such requirement. To solve the above problem, this paper proposes a fast watermarking method based on Zernike moments for high resolution videos to resist various attacks. In the proposed method, the frames of a video sequence are firstly grouped, from which a certain number of frame pairs are then selected for watermark embedding. For each frame pair to be embedded, we partition one frame into a set of disjoint blocks and apply singular value decomposition to each block to obtain a square feature matrix. Thereafter, by calculating all the Zernike moments, secret information is embedded into the selected Zernike moments to achieve superior robustness while keeping imperceptibility. Finally, according to the video encoding framework, we overwrite the frame difference of the frame pair by the watermark to resist compression and transcoding attacks. Furthermore, we propose two optional methods for compensating the special cases of rotation and scaling attacks during watermark detection. Experiments demonstrate the advantage of our method over the existing robust watermarking methods.

Multiview clustering has received great attention and numerous subspace clustering algorithms for multiview data have been presented. However, most of these algorithms do not effectively handle high-dimensional data and fail to exploit consistency for the number of the connected components in similarity matrices for different views. In this article, we propose a novel consistency-induced multiview subspace clustering (CiMSC) to tackle these issues, which is mainly composed of structural consistency (SC) and sample assignment consistency (SAC). To be specific, SC aims to learn a similarity matrix for each single view wherein the number of connected components equals to the cluster number of the dataset. SAC aims to minimize the discrepancy for the number of connected components in similarity matrices from different views based on the SAC assumption, that is, different views should produce the same number of connected components in similarity matrices. CiMSC also formulates cluster indicator matrices for different views, and shared similarity matrices simultaneously in an optimization framework. Since each column of similarity matrix can be used as a new representation of the data point, CiMSC can learn an effective subspace representation for the high-dimensional data, which is encoded into the latent representation by reconstruction in a nonlinear manner. We employ an alternating optimization scheme to solve the optimization problem. Experiments validate the advantage of CiMSC over 12 state-of-the-art multiview clustering approaches, for example, the accuracy of CiMSC is 98.06% on the BBCSport dataset.

Content similarity is a representative property of natural images, for example, similar regions, which is utilized by modern steganalysis. Existing JPEG steganographic methods mainly focus on the complexity of content but ignore content similarity. This article investigates content similarity to improve the undetectability of JPEG steganography. Specifically, the content similarity of DCT blocks and the 64 parallel channels is used to design the distortion function. Given a JPEG image, initial embedding costs are assigned for quantized DCT coefficients using an appropriate algorithm among the existing distortion functions. Then, the similarities of blocks and channels are used to update the initial embedding costs, respectively. After combination, the final distortion function can be obtained. Using syndrome trellis coding (STC), which achieves minimal embedding distortion with respect to a given distortion function, secret data are embedded into the cover image with a final distortion function. Experimental results show that our scheme achieves better undetectability than current state-of-the-art JPEG steganographic methods.

Face recognition has been extensively employed in practice, such as attendance system and public security. Linear discriminant analysis (LDA) algorithm is one of the most significant ones in the field of face recognition, but it is very difficult for many clients to employ it in their resource-constrained devices (e.g., smartphones and notebook computers). Outsourcing computation provides a promising method for clients to perform heavy tasks with limited computing power. In this paper, we design a protocol of outsourcing LDA-based face recognition to an untrusted cloud, which can help the client to complete the operations of matrix inversion (MI), matrix multiplication (MM) and eigenvalue decomposition (ED) simultaneously. The proposed outsourcing protocol can hide the private data of the client from the cloud. More importantly, the client can verify whether the outsourcing results are correct or not with probability one and so it is impossible for the server to deceive the client. In addition, the proposed protocol greatly decreases the computational complexity of the client thus enabling the client to complete LDA algorithm efficiently. Finally, we implement the protocol and give a comprehensive evaluation. The experimental results demonstrate that the client obtain great computing savings and the face recognition accuracy in the proposed protocol is almost identical to the original LDA algorithm.

Existing cover selection methods for steganography mainly focus on embedding distortion of each image, but ignore the similarity between images. When the cover images are similar, a number of relevant samples are provided to steganalysis, which is disadvantageous to steganography. This paper proposes a new cover selection method to joint image similarity and embedding distortion. Due to the difference between steganography and other image processing tasks, e.g., image reconstruction, image recognition, we propose a customized method to calculate image similarity for steganography based on SVD (singular value decomposition). Importantly, the small singular values (instead of the large ones) are employed, since it is suitable for the properties of steganography. In addition, embedding distortion is calculated by the current distortion minimization framework. The obtained image similarity and embedding distortion are combined to form a new cover selection strategy. As a result, the properties of batch images can be fully used. Experimental results show that our scheme outperforms the state-of-the-art cover selection methods when they are checked by modern steganalytic tools.

Data in real world are usually characterized in multiple views, including different types of features or different modalities. Multi-view learning has been popular in the past decades and achieved significant improvements. In this paper, we investigate three challenging problems in the field of incomplete multi-view representation learning, namely, i) how to reduce the influences produced by missing views in multi-view dataset, ii) how to learn a consistent and informative representation among different views and iii) how to alleviate the impacts of the inherent noise in multi-view data caused by high-dimensional features or varied quality for different data points. To address these challenges, we integrate these three tasks into a problem and propose a novel framework termed Noise-aware Incomplete Multi-view Learning Networks (NIM-Nets). NIM-Nets fully utilize incomplete data from different views to produce a multi-view shared representation which is consistent, informative and robust to noise. We model the inherent noise in data by defining the distribution $\Gamma $ and assuming that each observation in the incomplete dataset is sampled from the distribution $\Gamma $ . To the best of our knowledge, this is the first work to unify learning the consistent and informative representation, alleviating the impacts of noise in data and handling the view-missing patterns in multi-view learning into a framework. We also first give a definition of robustness and completeness for incomplete multi-view representation learning. Based on NIM-Nets, we present joint optimization models for classification and clustering, respectively. Extensive experiments on different datasets demonstrate the effectiveness of our method over the existing work based on classification and clustering tasks in terms of different metrics.

Symmetric nonnegative matrix factorization (SNMF) is effective to cluster nonlinearly separable data, which uses the constructed graph to capture the structure of inherent clusters. Nevertheless, many SNMF-based clustering approaches implicitly enforce either the sparseness constraint or the smoothness constraint with the limited supervised information in the form of cannot-link or must-link in a semi-supervised manner, which may not be quite satisfactory in many applications where sparseness and smoothness are demanded explicitly and simultaneously. In this paper, we propose a new semi-supervised SNMF-based approach termed Semi-supervised Structured SNMF-based clustering (S3NMF). The method flexibly enforces the block-diagonal structure to the similarity matrix, where the sparseness and smoothness are simultaneously considered, so that we can obtain the desirable assignment matrix by simultaneously learning similarity and assignment matrices in a constrained optimization problem. We formulate S3NMF with a semi-supervised manner and utilize the indirect constraints of sparseness and smoothness by cannot-link and must-link. To effectively solve S3NMF, we present an alternating iterative algorithm with theoretically proved convergence to seek for the solution of the optimization problem. Experiments on five benchmark data sets show better performance and satisfactory stability of the proposed method.

As the underlying technology of cryptocurrencies, blockchain has gained a lot of attention in recent years. However, the storage problem needs to be solved with the increasing number of blocks in the blockchain network. Cloud storage optimization is an effective way to solve the storage issue, which selects and stores parts of blocks to the cloud. Precisely, block selection can be described as a multiobjective optimization problem (MOP) and solved by evolutionary algorithms (EAs). To obtain well results of block selection, an improved NSGA-III algorithm based on deep Q-networks (DQN), termed NSGA-DQN, is proposed in this paper, which aims to maintain well convergence and diversity of the population. This way, a set of suitable solutions is obtained to determine the number of blocks stored to the cloud, and the storage problem can be solved effectively. To be specific, DQN creates a decision-making agent to maximize the expected reward by learning a policy that evaluates$Q$values of each action in each state. In the proposed selection mechanism, the reward values are set according to the convergence and diversity of the population, and the actions correspond to the individuals. This way, our method can determine a set of individuals that maximizes the convergence and diversity of the population. In addition, an adaptive maximum reward enhancement module (AMREM) is developed to further enhance the maximum expected reward by updating the new better reward and modifying the replay memory. We conduct the experimental study on block selection, and the results demonstrate that the proposed algorithm is superior to five state-of-the-art algorithms.

Detection of manipulated face images has attracted a lot of interest recently. Various schemes have been proposed to tackle this challenging problem, where the patch-based approaches are shown to be promising. However, the existing patch-based approaches tend to treat different patches equally, which do not fully exploit the patch discrepancy for effective feature learning. In this paper, we propose a Patch Diffusion (PD) module which can be integrated into the existing face manipulation detection networks to boost the performance. The PD consists of Discrepancy Patch Feature Learning (DPFL) and Attention-Aware Message Passing (AMP). The DPFL effectively learns the patch features by a newly designed Pairwise Patch Loss (PPLoss), which takes both the patch importance and correlations into consideration. The AMP diffuses the patches through attention-aware message passing in a graph network, where the attentions are explicitly computed based on the patch features learnt in DPFL. We integrate our PD module into four recent face manipulation detection networks, and carry out the experiments on four popular datasets. The results demonstrate that our PD module is able to boost the performance of the existing networks for face manipulation detection.

Recent advances in linguistic steganalysis have successively applied CNN, RNN, GNN and other efficient deep models for detecting secret information in generative texts. These methods tend to seek stronger feature extractors to achieve higher steganalysis effects. However, we have found through experiments that there actually exists significant difference between automatically generated stego texts and carrier texts in terms of the conditional probability distribution of individual words. Such kind of difference can be naturally captured by the language model used for generating stego texts. Through further experiments, we conclude that this ability can be transplanted to a text classifier by pre-training and fine-tuning to improve the detection performance. Motivated by this insight, we propose two methods for efficient linguistic steganalysis. One is to pre-train a language model based on RNN, and the other is to pre-train a sequence autoencoder. The results indicate that the two methods have different degrees of performance gain compared to the randomly initialized RNN, and the convergence speed is significantly accelerated. Moreover, our methods achieved the best performance compared to related works, while providing a solution for real-world scenario where there are more cover texts than stego texts.

With the rapid development of neural network, a vast number of neural network models have been developed in recent years, which condense numerous manpower and hardware resource. However, the original models are at risk of being pirated by the adversary to obtain illegal profits. On the other hand, malicious tampering on models, such as implanting the vulnerability and backdoor, may cause catastrophic consequences. We propose a model hash generator method to protect neural network models. Detailedly, our model hash sequence is composed of two parts: one is the model piracy identification hash, which is based on the dynamic convolution and a dual-branch network; the other is the model tampering localization hash, which can help the model owner to accurately detect the tampered locations for further recovery. Experimental results demonstrate the effectiveness of the proposed method for neural network model protection.

A lot of excellent neural network models are valuable wealth to the field of artificial intelligence, which may be plagiarized and distributed without authorization. For this reason, few research establishments and industries reveal the internals of their neural network models. To authenticate suspicious pirated models, this letter proposes a gray-box hashing method for the neural network models that designed for image classification. In the proposed method, the hash sequence of original model can be extracted without knowing both the structure and weight parameters except the vectors in output layer. To the best of our knowledge, this is the first work focusing on gray-box perceptual model hashing to identify and authenticate neural network models. Experimental results show that our method performs satisfactory perceptual robustness and discrimination capability, and can effectively classify perceptual similar versions of the original model and distinct models.

Recently, with the application of deep learning in the remote sensing image (RSI) field, the classification accuracy of the RSI has been dramatically improved compared with traditional technology. However, even the state-of-the-art object recognition convolutional neural networks are fooled by the universal adversarial perturbation (UAP). The research on UAP is mostly limited to ordinary images, and RSIs have not been studied. To explore the basic characteristics of UAPs of RSIs, this paper proposes a novel method combining an encoder-decoder network with an attention mechanism to generate the UAP of RSIs. Firstly, the former is used to generate the UAP, which can learn the distribution of perturbations better, and then the latter is used to find the sensitive regions concerned by the RSI classification model. Finally, the generated regions are used to fine-tune the perturbation making the model misclassified with fewer perturbations. The experimental results show that the UAP can make the classification model misclassify, and the attack success rate of our proposed method on the RSI data set is as high as 97.09%.

Federated learning (FL) is a distributed machine learning framework, which allows multiple users to collaboratively train and obtain a global model with high accuracy. Currently, FL is paid more attention by researchers and a growing number of protocols are proposed. This article first analyzes the security vulnerabilities of the VerifyNet and VeriFL protocols, and proposes a new aggregation protocol for FL. We use additive homomorphic encryption and double masking to simultaneously protect the user’s local model and the aggregated global model while most of the existing protocols only consider the privacy of the local model. Also, linear homomorphic hash and digital signature are used to achieve traceable verification, which means the users can not only verify the aggregation results, but also be able to identify the wrong epoch if the results are wrong. In summary, our protocol can realize the privacy of the local model and global model and achieve verification traceability even if the cloud server colludes with malicious users. The experimental results show that the proposed protocol improves the security of FL without decreasing the efficiency of the users and classification accuracy of the training model.

In this letter, we propose a novel linguistic steganographic method that directly conceals a token-level secret message in a seemingly-natural steganographic text generated by the off-the-shelf BERT model equipped with Gibbs sampling. Compared with all modification based linguistic steganographic methods, the proposed method does not modify a given cover text. Instead, the proposed method utilizes the secret message to directly generate the steganographic text. Compared with mainstream generation based linguistic steganographic methods, the proposed method enables the receiver to collect the tokens of the specific positions to directly constitute the secret message, without a complex decoding process and much side information shared between the sender and the receiver. Experimental results show that the proposed method can generate fluent, highly readable steganographic texts, while enjoying pretty good anti-steganalysis ability. This work has great application potential in real-time covert communication.

This article proposes a repeatable data hiding framework for digital images, in which the distortion caused by data hiding is invariable no matter how many times the embedding operation is executed. As a result, the usability of images can be always guaranteed. To achieve repeatable data hiding, we deduce the theoretical modification probabilities of the elements in cover image (an image used for data hiding). Then we design an embedding framework to make the practical modification probabilities of cover elements equal to the deduced ones. When additional data is embedded into cover image with the deduced probabilities, the modification-trace of current embedding is replaced by the latter embedding. That means multiple embeddings do not produce new modification-trace, so that the distortion caused by data hiding is invariable. Therefore, the repeatability of data hiding can be guaranteed. Finally, we describe some applications of repeatable data hiding, e.g., logistics management, authentication database management, and steganography, to show the practicability of our framework.

Multi-view clustering aims at simultaneously obtaining a consensus underlying subspace across multiple views and conducting clustering on the learned consensus subspace, which has gained a variety of interest in image processing. In this paper, we propose the Semi-supervised Structured Subspace Learning algorithm for clustering data points from Multiple sources (SSSL-M). We explicitly extend the traditional multi-view clustering with a semi-supervised manner and then build an anti-block-diagonal indicator matrix with small amount of supervisory information to pursue the block-diagonal structure of the shared affinity matrix. SSSL-M regularizes multiple view-specific affinity matrices into a shared affinity matrix based on reconstruction through a unified framework consisting of backward encoding networks and the self-expressive mapping. The shared affinity matrix is comprehensive and can flexibly encode complementary information from multiple view-specific affinity matrices. An enhanced structural consistency of affinity matrices from different views can be achieved and the intrinsic relationships among affinity matrices from multiple views can be effectively reflected in this manner. Technically, we formulate the proposed model as an optimization problem, which can be solved by an alternating optimization scheme. Experimental results over seven different benchmark datasets demonstrate that better clustering results can be obtained by our method compared with the state-of-the-art approaches.

Neural style transfer has been developed in recent years, where both performance and efficiency have been greatly improved. However, most existing methods do not transfer the brushstrokes information of style images well. In this article, we address this issue by training a multi-granularity brushstrokes network based on a parallel coding structure. Specifically, we first adopt the content parsing module to obtain the spatial distribution of content image and the smoothness of different regions. Then, different brushstrokes features are transformed by a multi-granularity style-swap module guided by the region content map. Finally, the stylized features of the two branches are fused to enhance the stylized results. The multi-granularity brushstrokes network is jointly supervised by a new multi-layer brushstroke loss and pre-existing loss. The proposed method is close to the artistic drawing process. In addition, we can control whether the color of the stylized results tend to be the style image or the content image. Experimental results demonstrate the advantage of our proposed method compare with the existing schemes.

Face anti-spoofing is an important step for secure face recognition. One of the main challenges is how to learn and build a general classifier that is able to resist various presentation attacks. Recently, the patch-based face anti-spoofing schemes are shown to be able to improve the robustness of the classifier. These schemes extract subtle liveness cues from small local patches independently, which do not fully exploit the correlations among the patches. In this paper, we propose a Patch-based Compact Graph Network (PCGN) to diffuse the subtle liveness cues from all the patches. Firstly, the image is encoded into a compact graph by connecting each node with its backward neighbors. We then propose an asymmetrical updating strategy to update the compact graph. Such a strategy aggregates the node based on whether it is a sender or receiver, which leads to better message-passing. The updated graph is eventually decoded for making the final decision. We conduct the experiments on four public databases with four intra-database protocols and eight cross-database protocols, the results of which demonstrate the effectiveness of our PCGN for face anti-spoofing.

In this paper, we propose a privacy protection scheme using image dual-inpainting and data hiding. In the proposed scheme, the privacy contents in the original image are concealed, which are reversible that the privacy content can be perfectly recovered. We use an interactive approach to select the areas to be protected, that is, the protection data. To address the disadvantage that single image inpainting is susceptible to forensic localization, we propose a dual-inpainting algorithm to implement the object removal task. The protection data is embedded into the image with object removed using a popular data hiding method. We further use the pattern noise forensic detection and the objective metrics to assess the proposed method. The results on different scenarios show that the proposed scheme can achieve better visual quality and antiforensic capability than the state-of-the-art works.

QR and LU factorizations are two basic mathematical methods for decomposition and dimensionality reduction of large-scale matrices. However, they are too complicated to be executed for a limited client because of big data. Outsourcing computation allows a client to delegate the tasks to a cloud server with powerful resources and therefore greatly reduces the client’s computation cost. However, the previous methods of QR and LU outsourcing factorizations need multiple interactions between the client and cloud server or have low accuracy and efficiency in large-scale matrix applications. In this paper, we propose a noninteractive and efficient outsourcing algorithm of large-scale QR and LU factorizations. The proposed scheme is based on the specific perturbation method including a series of consecutive and sparse matrices, which can be used to protect the original matrix and obtain the results of factorizations. The generation and inversion of sparse matrix has small workloads on the client’s side, and the communication cost is also small since the client does not need to interact with the cloud server in the outsourcing algorithms. Moreover, the client can verify the outsourcing result with a probability of approximated to 1. The experimental results manifest that as for the client, the proposed algorithms reduce the computational overhead of direct computation successfully, and it is most efficient compare with the previous ones.

Content-adaptive steganography embeds information into the cover image adaptively under the guidance of embedding probabilities (also known as selection channel) of the image elements, which can also be obtained by the steganalyzer. Therefore, some adaptive steganalysis schemes with selection channel have been proposed to detect content-adaptive steganography. Existing selection-channel-aware CNN-based steganalyzers usually incorporate the embedding probability into the first convolutional layer. They may fail to make full use of embedding probability information because this information incorporated into the first layer may disappear when it propagates to deep convolutional layers. To address this issue, we propose embedding probability guided module to adaptively enhance the features from different levels of network. Moreover, these embedding probability guided features from different levels are progressively integrated to jointly make final decisions. Experimental results prove that our approach can outperform existing selection-channel-aware approaches in both the spatial domain and JPEG domain.

Convolutional neural networks (CNNs) are widely used in many aspects and achieve excellent results. Due to the authorization from different users, we need to consider the right management caused by multi-user. This paper proposes a novel concept of hierarchical authorization of CNNs, and it can help owners control the output results according to accesses. To realize this idea, we refer to differential privacy and use the Laplace mechanism to perturb the output of the model to vary degrees. In experiments, we run on CIFAR-10 and MNIST datasets using ResNet and VGG, and the results show our method has significant grading effects.

As the deep hashing technique has been widely used in large-scale image retrieval, the corresponding security issue is getting more and more attention. Recent studies have found that deep image classifiers are vulnerable to adversarial example attacks and produce misleading classifications. Therefore, in order to study the robustness of deep hashing based retrieval system to adversarial example, in this letter, we propose a novel adversarial example generation algorithm, non-targeted deep hashing attack (NDHA), which uses the anchor-moving strategy to continuously modify anchor image to cross the search correlation boundary and maximize Hamming distance between the hash codes of adversarial example and query image. The generated adversarial example can make the retrieved result semantically irrelevant to query image. Extensive experiments show that the proposed NDHA can efficiently produce imperceptible perturbation, which is effective for attacking deep hashing based retrieval systems.

Recent linguistic steganalysis methods model texts as sequences and use deep learning models to extract discriminative features for detecting the presence of secret information in texts. However, natural language has a complex syntactic structure and sequences have limited representation ability for text modeling. Moreover, previous methods tend to extract features from local continuous word sequences, which cannot effectively model global characteristics. In this paper, we present a linguistic steganalysis method with graph neural network. In the proposed method, texts are translated as directed graphs with the associated information, where nodes denote words and edges show associations between the words. By training a graph convolutional network for feature extraction, each node of a graph can collect contextual information to update self-expression, accordingly effectively solving the problem of poor representation of polysemous words. Meanwhile, we adopt a globally-shared matrix to record correlation strengths between words so that each text can effectively utilize the global information to obtain the better self-representation. Experimental results have shown that the proposed work achieves the state-of-the-art performance comparing with the previous works.

Outsourcing computation allows an outsourcer with limited resource to delegate the computation load to a powerful server without the exposure of true inputs and outputs. It is well known that modular exponentiation is one of the most expensive operations in public key cryptosystems. Currently, most of outsourcing algorithms for modular exponentiation are based on two untrusted servers or have small checkability with single server. In this paper, we first propose an efficient outsourcing algorithm of modular exponentiation based on two untrusted servers, where the outsourcer can detect the error based on Euler theorem with a probability of 1 if one of the servers misbehaves. We then present an outsourcing algorithm of modular exponentiation with single server, and the outsourcer can also check the failure with a probability of 1. Therefore, the proposed algorithm with single server improves efficiency and checkability simultaneously compare with the previous ones. Finally, we provide the experimental evaluations to demonstrate that the proposed two algorithms are the most efficient ones in all of the outsourcing algorithms for an outsourcer.

In this paper, a novel perceptual image hashing scheme based on convolutional neural network (CNN) with multiple constraints is proposed, in which our deep hashing network learns the process of features extraction automatically according to the training target and then generates the final hash sequence. The combination of convolutional and pooling layers is to reduce the size of input image while deepening the channels. Then, we construct two pairs of constraints and integrate them into an overall constraint function through a strategy of weight allocation. In order to guarantee the robustness and discrimination of deep hashing network simultaneously, a new training method is developed to adjust the training set structure dynamically according to the changes of constraint values. Experimental results show that the proposed deep hashing network can achieve a satisfactory balance between perceptual robustnzess and discrimination while maintaining security. Based on the large-scale test set, receiver operating characteristic (ROC) curves,${F}_{1}$scores and equal error rate (EER) demonstrate the superiority of our scheme in terms of content authentication compared with some state-of-the-art schemes.

With the rise of cryptocurrencies, blockchain, which is the underlying technology of them, has gained more attention and been used in the Internet of Things (IoT) and other fields. However, there are bottlenecks that hinder its application, such as the storage capacity. Due to the large number of IoT devices which always act as data generators in many systems, the transactions will be generated at a high rate. The storage problem will be more serious in IoT. In this article, to expand the capacity of blockchain, for each peer, we select old blocks which are created previously and less likely to be queried and store them in the cloud. Based on this idea, we develop objective functions related to query probability, storage cost, and local space occupancy, which naturally narrows down the problem to block selection. The results can be obtained by solving a multiobjective optimization problem. We design a nondominated sorting genetic algorithm with clustering (NSGA-C), which changes the method of selecting individuals from the critical dominance layer by adding clustering to ensure diversity. A suitable solution can be selected from the Pareto set to fulfil the requirements of different users. We then compare the algorithm with the improved NSGA-II and NSGA-III, which both add integer constraints to the decision variables. The results show that our method is better than NSGA-II and NSGA-III in terms of local space occupancy in the blockchain application. In addition, it can also effectively avoid the risk of block overflow.

Steganography plays an important role in information hiding. With the development of steganalysis, traditional steganography faces more detection threat. It is necessary to improve security of current steganographic methods. One effective way is to generate suitable covers for steganography, which can be achieved by adversarial learning. In this letter, we propose a new approach for quickly constructing high-quality adversarial images. Compared with original images, the generative adversarial images are more suitable for carrying secret information. According to the characteristics of steganography, we design a new loss function in adversarial attacks, which makes the adversarial images obtain the similar classification results before and after steganography. In addition, to further improve security of the adversarial images, we also make use of the zero-sum idea of generative adversarial networks. Experimental results show that the proposed method can significantly enhance security of steganography.

High-dimensional feature extraction based on co-occurrence matrix improves the detection performance of steganalysis, but it is difficult to be realized for massive image data by an analyzer with limited computational ability. We solve this problem by verifiable outsourcing computation, which allows a computationally weak client to outsource the evaluation of a function to a powerful but untrusted server. In this paper, we propose a verifiable outsourcing scheme of feature extraction based on co-occurrence matrix with single untrusted cloud server. The original images are protected from the server by using a projection of one to many with trapdoor, which can be realized by a symmetric probabilistic encryption scheme we present. The analyzer can obtain true results of feature extraction and detect any failure with a probability of 1 if the server misbehaves. Finally, we provide the simulations on the outsourcing of extracting ccJRM features in cloud computing. The theory analysis and experiment result also show that the proposed outsourcing scheme could greatly decrease the computation cost of the analyzer without exposure of the original images and extraction results.

Steganalysis is a technique for detecting the existence of secret information hidden in digital media. In this paper, we propose a novel scheme for JPEG steganalysis. In this scheme, we first design the diverse base filters which are able to obtain the image residuals from various directions. Then, we propose a cascade filter generation strategy to construct a set of high order cascade filters from the base filters. We further select the cascade filters with the maximum diversity. The selected filters are convolved with the decompressed JPEG image to obtain residuals which capture the subtle embedding traces. The residuals, termed as the maximum diversity cascade filter residual, are eventually used to extract features to train an ensemble classifier for classification. The experiments are carried out on the detection of stego-images generated using common JPEG steganographic schemes, the results of which demonstrate the effectiveness of the proposed scheme for JPEG steganalysis.

With the widespread use of biometrics recognition systems, it is of paramount importance to protect the privacy of biometrics. In this paper, we propose to protect the fingerprint privacy by the artificial fingerprint, which is generated based on three pieces of information, i) the original minutiae positions; ii) the artificial fingerprint orientation; and iii) the artificial minutiae polarities. To make it real-look alike and diverse, we propose to generate the artificial fingerprint orientation by a model taking both the global and local fingerprint orientation into account. Its parameters can be easily guided by an user specific key with simple constraints. The artificial minutiae polarities are generated from the same key, where a block based and a function based approach are proposed for the minutiae polarities generation. These information are properly integrated to form a real-look alike artificial fingerprint. It is difficult for the attacker to distinguish such a fingerprint from the real fingerprints. If it is stolen, the complete fingerprint minutiae feature will not be compromised, and we can generate a different artificial fingerprint using another key. Experimental results show that the artificial fingerprint can be recognized accurately.

This paper proposes a new steganography method for hiding data into dynamic GIF (Graphics Interchange Format) images. When using the STC framework, we propose a new algorithm of cost assignment according to the characteristics of dynamic GIF images, including the image palette and the correlation of interframes. We also propose a payload allocation algorithm for different frames. First, we reorder the palette of GIF images to reduce the modifications on pixel values when modifying the index values. As the different modifications on index values would result in different impacts on pixel values, we assign the elements with less impact on pixel values with small embedding costs. Meanwhile, small embedding costs are also assigned for the elements in the regions that the interframe changes are large enough. Finally, we calculate an appropriate payload for each frame using the embedding probability obtained from the proposed distortion function. Experimental results show that the proposed method has a better security performance than state-of-the-art works.

Currently many important advances in digital media field have been achieved through the ensemble method. In image steganalysis, the application of classifiers has evolved from the early single classifier to the ensemble classifiers. The performance of the ensemble classifier is better than that of a single classifier, but the classifiers may have a certain degree of redundancy. Therefore, it is of great significance to study how to reduce the number of the ensemble classifiers under the premise of ensuring the classification performance. In this letter, we propose a selective ensemble method in image steganalysis based on deep Q network, which combines reinforcement learning with convolutional neural network and are seldom seen in ensemble pruning. This method improves the generalization performance of the model, and reduces the size of ensemble as well. The experimental results show that the proposed method has a certain degree of effect on the ensemble classification optimization of image steganalysis in both spatial and frequency domains.

The development of multimedia and hardware technologies has led to a great number of industrial video applications, such as virtual reality, high-definition video surveillance, and remote monitoring. As complex communication environments and heterogeneous networks are common in industrial applications, industrial videos are required to support a diverse range of display resolutions and transmission channel capacities. Scalable high-efficiency video coding (SHVC) standards provide the tools to meet this requirement. However, it is highly computationally expensive. Coding complexity has a great impact on SHVC performance in industrial applications. Many of these applications are sensitive to time delay and have limited power. Thus, improvements are required to ensure the practical usability of SHVC encoders. In SHVC encoders, intra/interprediction of variable coding unit (CU) sizes is independently performed for the base and enhancement layers (ELs). There are many interlayer similarities that can be exploited to speed up the procedure for EL coding. In this paper, we propose a feedforward neural network aided model for CU size and mode decisions for SHVC, which utilizes base layer coding information and the coding data of spatiotemporal neighboring CUs to decide which CU sizes or prediction modes can be bypassed for certain EL CUs. Two feedforward neural network based learning models are built for CU classification, which are introduced in the procedures for CU size and mode decisions, respectively. According to the analysis from a large number of video sequences, the representative features are directly extracted from the coding information of previously coded neighboring CUs to avoid computational overheads. After the training is finished, these two models are designed and integrated to build classifiers. Then, two online classification approaches are designed for the CU size and mode decision procedures to classify each CU's type. Finally, different candidate CU sizes and prediction modes are adaptively assigned for each type of CU. This approach outperforms the state-of-the-art fast SHVC/high-efficiency video coding (HEVC) algorithms with approximately 19-42% coding time savings or better compression efficiency, which will be beneficial for the realization of real-time scalable video coding.

The scalable video coding extensions of the High Efficient Video Coding (HEVC) standard (SHVC) have adopted a new quadtree-structured coding unit (CU). The SHVC test model (SHM) needs to test seven intermode sizes and one intramode size at depth levels of “0,” “1,” “2,” and four intermode sizes and two intramode sizes at a depth level of “3” for interframe CUs. It checks all possible depth levels and prediction modes to find the one with the lowest rate distortion cost using the Lagrange multiplier method in the mode decision procedure to achieve high coding efficiency at the expense of computational complexity. Furthermore, it utilizes the conventional approach for the base layer (BL) and enhancement layer (EL) coding to support SNR/spatial scalable coding. Both the intralayer and interlayer predictions should be performed for each EL CU. Although there is a large amount of interlayer redundancy that can be exploited to speed up the EL encoding, the mode decision procedure is independently performed for the BL and the ELs. In this paper, we propose a content-adaptive mode decision algorithm to reduce the SHVC complexity at the ELs. When the major characteristics of the CUs, such as mode complexity and motion activity, can be estimated early and used for adjusting the mode decision procedure, unnecessary mode and CU size searches can be avoided. First, an experimental analysis is performed to study the interlayer and spatiotemporal correlations in the coding information and the interlevel correlations among the quadtree structures. Based on these correlations, three parameters, including the conditional probability of a SKIP/Merge mode, motion activity, and mode complexity, are defined to describe the video content and are further utilized to adaptively adjust the EL mode decision procedure. The experimental results show that the proposed algorithm can reduce the coding time for ELs by 62%-67% with less than a 1.5% Bjontegaard rate increase compared to the original SHVC encoder.

The scalable extension of the high-efficiency video coding (SHVC) system adopts a hierarchical quadtree-based coding unit (CU) that is suitable for various texture and motion properties of videos. Currently, the test model of SHVC identifies the optimal CU size by performing an exhaustive quadtree depth-level search, which achieves a high compression efficiency at a heavy cost in terms of the computational complexity. However, many interactive multimedia applications, such as remote monitoring and video surveillance, which are sensitive to time delays, have insufficient computational power for coding high-definition (HD) and ultra-high-definition (UHD) videos. Therefore, it is important, yet challenging, to optimize the SHVC coding procedure and accelerate video coding. In this article, we propose a fast CU quadtree depth-level decision algorithm for inter-frames on enhancement layers that is based on an analysis of inter-layer, spatial, and temporal correlations. When motion/texture properties of coding regions can be identified early, a fast algorithm can be designed for adapting CU depth-level decision procedures to video contents and avoiding unnecessary computations during CU depth-level traversal. The proposed algorithm determines the motion activity level at the treeblock size of the hierarchical quadtree by utilizing motion vectors from its corresponding blocks at the base layer. Based on the motion activity level, neighboring encoded CUs that have larger correlations are preferentially selected to predict the optimal depth level of the current treeblock. Finally, two parameters, namely, the motion activity level and the predicted CU depth level, are used to identify a subset of candidate CU depth levels and adaptively optimize CU depth-level decision processes. The experimental results demonstrate that the proposed scheme can run approximately three times faster than the most recent SHVC reference software, with a negligible loss of compression efficiency. The proposed scheme is efficient for all types of scalable video sequences under various coding conditions and outperforms state-of-the-art fast SHVC and HEVC algorithms. Our scheme is a suitable candidate for interactive HD/UHD video applications that are expected to operate in real-time and power-constrained scenarios.

3D-high efficiency video coding (HEVC) is developed for the compression of the multi-view video plus depth format, which is based on the latest generation of video coding standard, HEVC. It further adopts several new intra prediction modes, depth-modeling modes (DMMs) in intra candidate modes for a better representation of edges in depth maps, which introduces a drastic increase in the computational complexity. The procedure of depth intra mode decision together with DMMs and existing intra modes is a very time consuming part due to huge complexity of full rate distortion (RD) cost calculation. In this paper, a low complexity intra mode selection algorithm is proposed to reduce complexity of depth intra prediction in both intra-frames and inter-frames. An experimental analysis is first performed to study the inter-view correlation and the inter-component (texture video and its associated depth) correlation in intra coding information such as the intra mode and RD cost. All intra modes available in 3D-HEVC are classified into three activity classes assigned with different mode-weight factors, and the coding mode complexity of a coding unit (CU) is defined according to the intra mode information from available spatiotemporal, inter-view, and inter-component neighboring coded CUs. The coding mode complexity analysis is utilized to assign different candidate intra modes for different types of CUs. The optimal intra prediction mode and the RD cost value in current CU depth level are further used to skip unnecessary intra prediction sizes. Experimental results show that the proposed fast depth intra coding algorithm achieves 61% complexity reduction on intra prediction, while incurring a 0.2% Bjontegaard metric increase for coded and synthesized views compared to the test model of 3D-HEVC.

This paper proposes a method of reversible data hiding in encrypted images (RDH-EI) based on progressive recovery. Three parties are involved in the framework, including the content owner, the data-hider, and the recipient. The content owner encrypts the original image using a stream cipher algorithm and uploads a ciphertext to the server. The data-hider on the server divides the encrypted image into three channels and, respectively, embeds different amount of additional bits into each one to generate a marked encrypted image. On the recipient side, additional message can be extracted from the marked encrypted image, and the original image can be recovered without any errors. While most of the traditional methods use one criterion to recover the whole image, we propose to do the recovery by a progressive mechanism. Rate-distortion of the proposed method outperforms state-of-the-art RDH-EI methods.

Extreme learning machine (ELM), proposed by Huang et al., was developed for generalized single hidden layer feedforward networks with a wide variety of hidden nodes. ELMs have been proved very fast and effective especially for solving function approximation problems with a predetermined network structure. However, it may contain insignificant hidden nodes. In this paper, we propose dynamic adjustment ELM (DA-ELM) that can further tune the input parameters of insignificant hidden nodes in order to reduce the residual error. It is proved in this paper that the energy error can be effectively reduced by applying recursive expectation-minimization theorem. In DA-ELM, the input parameters of insignificant hidden node are updated in the decreasing direction of the energy error in each step. The detailed theoretical foundation of DA-ELM is presented in this paper. Experimental results show that the proposed DA-ELM is more efficient than the state-of-art algorithms such as Bayesian ELM, optimally-pruned ELM, two-stage ELM, Levenberg-Marquardt, sensitivity-based linear learning method as well as the preliminary ELM.

This paper proposes a novel scheme of compressing encrypted images with auxiliary information. The content owner encrypts the original uncompressed images and also generates some auxiliary information, which will be used for data compression and image reconstruction. Then, the channel provider who cannot access the original content may compress the encrypted data by a quantization method with optimal parameters that are derived from a part of auxiliary information and a compression ratio-distortion criteria, and transmit the compressed data, which include an encrypted sub-image, the quantized data, the quantization parameters and another part of auxiliary information. At receiver side, the principal image content can be reconstructed using the compressed encrypted data and the secret key. Experimental result shows the ratio-distortion performance of the proposed scheme is better than that of previous techniques.

This work proposes a JPEG steganalytic scheme based on high-dimensional features and Bayesian ensemble classifier. The proposed scheme employs 15700 dimension features calculated from the co-occurrence matrices of DCT coefficients and coefficient differences, which indicate the intra-block and inter-block dependencies of image content. Furthermore, a number of sub-classifiers trained on the features are integrated as an ensemble classifier with a Bayesian mechanism, which is used to give optimal decisions for suspicious images. Experimental results show that both the high-dimensional features and the Bayesian mechanism contribute to the extended scheme, and the performance of the extended scheme is better than those of previous schemes.

This paper proposes a novel scheme of scalable coding for encrypted images. In the encryption phase, the original pixel values are masked by a modulo-256 addition with pseudorandom numbers that are derived from a secret key. After decomposing the encrypted data into a downsampled subimage and several data sets with a multiple-resolution construction, an encoder quantizes the subimage and the Hadamard coefficients of each data set to reduce the data amount. Then, the data of quantized subimage and coefficients are regarded as a set of bitstreams. At the receiver side, while a subimage is decrypted to provide the rough information of the original content, the quantized coefficients can be used to reconstruct the detailed content with an iteratively updating procedure. Because of the hierarchical coding mechanism, the principal original content with higher resolution can be reconstructed when more bitstreams are received.

This paper proposes a novel watermarking scheme with flexible self-recovery quality. The embedded watermark data for content recovery are calculated from the original discrete cosine transform (DCT) coefficients of host image and do not contain any additional redundancy. When a part of a watermarked image is tampered, the watermark data in the area without any modification still can be extracted. If the amount of extracted data is large, we can reconstruct the original coefficients in the tampered area according to the constraints given by the extracted data. Otherwise, we may employ a compressive sensing technique to retrieve the coefficients by exploiting the sparseness in the DCT domain. This way, all the extracted watermark data contribute to the content recovery. The smaller the tampered area, the more available watermark data will result in a better quality of recovered content. It is also shown that the proposed scheme outperforms previous techniques in general.

This paper proposes two novel self-embedding watermarking schemes based upon a reference sharing mechanism, in which the watermark to be embedded is a reference derived from the original principal content in different regions and shared by these regions for content restoration. After identifying tampered blocks, both the reference data and the original content in the reserved area are used to recover the principal content in the tampered area. By using the first scheme, the original data in five most significant bit layers of a cover image can be recovered and the original watermarked image can also be retrieved when the content replacement is not too extensive. In the second scheme, the host content is decomposed into three levels, and the reference sharing methods with different restoration capabilities are employed to protect the data at different levels. Therefore, the lower the tampering rate, the more levels of content data are recovered, and the better the quality of restored results.

When hiding information in an image for self recovery, the amount of embedding data affects the embedding influence and the recovery quality. The purpose of this paper is to reduce the amount of embedding data while maintaining good recovery quality. We propose an approach to generate reference data from the original image by encoding different types of blocks into different number of bits. In reconstructing the reference image, a fast inpainting method is used to recover the contents of corrupted regions accompanied with the extracted bits.

One of the open problems in neural network research is how to automatically determine network architectures for given applications. In this brief, we propose a simple and efficient approach to automatically determine the number of hidden nodes in generalized single-hidden-layer feedforward networks (SLFNs) which need not be neural alike. This approach referred to as error minimized extreme learning machine (EM-ELM) can add random hidden nodes to SLFNs one by one or group by group (with varying group size). During the growth of the networks, the output weights are updated incrementally. The convergence of this approach is proved in this brief as well. Simulation results demonstrate and verify that our new approach is much faster than other sequential/incremental/growing algorithms with good generalization performance.

Online gradient methods are widely used for training feedforward neural networks. We prove in this paper a convergence theorem for an online gradient method with variable step size for backward propagation (BP) neural networks with a hidden layer. Unlike most of the convergence results that are of probabilistic and nonmonotone nature, the convergence result that we establish here has a deterministic and monotone nature.

We propose a novel blind non-additive robust watermarking scheme in this paper. The energy of the watermark is spread to all regions of the host data instead of some individual elements, which entitles the watermark with imperceptibility and high robustness. A class of 1-D Markov chaotic maps employed to perform host data element classification and watermark encryption ensures the security of the system. To prove the validity of this proposed scheme, some analyses and brief objective comparisons with the popular spread spectrum (SS) scheme are also presented. Simulation results show that our scheme can survive severe processing such as high-ratio JPEG compression, Gaussian noise pollution and histogram equalization.

In this paper, a novel algorithm is proposed to embed and detect digital watermarks. It is different from other algorithms, because the algorithm embeds digital bits in the relationship among pixels, whereas most other algorithms embed digital bits in terms of pixel position. In the embedding phase one discrete chaotic map is used to create random sequences to encrypt watermarking bits, the other discrete chaotic map is used to permute the original image. Watermarking detection resorts to looking for a path with the minimal distance like the Viterbi algorithm, and it is performed without the original image. Experimental results show that the algorithm can effectively resist the influence of Gaussian noise, JPEG compression, cropping operation, and can recover the original watermarks well.