With the increasing commercialization of the latent diffusion-based text-to-audio generation, model attribution has become a critical challenge. Embedding watermarks in generated audio is an effective way to distinguish synthetic from natural audio. However, existing watermarking methods often suffer from limited robustness or require additional training, limiting their scalability in practical applications. In this paper, we propose an anchor-based inversion optimization framework. The method embeds a watermark into the model's initial latent vector, designated as a pivotal anchor, and extracts the watermark through inversion. To mitigate error accumulation and enhance robustness during inversion, we leverage the temporal consistency and distributional similarity of diffusion models, formulating watermark extraction as a time-series optimization problem. Specifically, given a suspicious audio sample and a candidate model with a predefined anchor, we first perform unguided denoising diffusion on the anchor to generate an intermediate latent trajectory as the anchor sequence. Then, we optimize the inversion process to align the inverted trajectory with the anchor sequence, thereby reducing accumulated errors. During optimization, we adopt Soft Dynamic Time Warping as the loss function. Its flexible temporal alignment capability ensures that correct attribution is achieved only when the anchor matches the target audio. Experimental results show that our method enables training-free attribution while preserving audio quality and achieving strong robustness.

Image steganography conceals secret data within a digital image while preserving its innocent appearance. The advent of artificial intelligence generative models has given rise to a new paradigm known as generative image steganography, which hides secret data directly into the image generation process. However, existing generative image steganographic methods are typically only applicable to unquantized stego images, severely limiting their practicality in real-world scenarios. To address this limitation, we propose a generative image steganography with minimum-distance guidance based on a diffusion model, called MDStega. During the hiding phase, MDStega designs a secret data-driven residual image sampling mechanism, which establishes a dynamic mapping relationship between discrete secret data and continuous probability distributions, strictly preserving the distribution consistency between stego images and normally generated images. During the extraction phase, the minimum-distance guidance rule effectively suppresses the interference caused by stego image quantization on the extraction accuracy of secret data. Furthermore, MDStega does not require fine-tuning pre-trained models or training additional models, which significantly reduces computational overhead and training time. Experimental results demonstrate that MDStega is superior to state-of-the-art methods by not only ensuring secure concealment at 3 bits per pixel (bpp) in PNG format but also achieving a recovery accuracy of up to 99%, demonstrating strong practical potential.

Wildcard Keyword Searchable Encryption (WKSE) has grown into a ubiquitous tool. It enables clients to search desired files with wildcard expressions. Although promising, previous schemes confront three barriers: (1) An adversary can launch a correlation attack to acquire the similarity between keywords. (2) The WKSE schemes exhibit false positives which can lead to wrong search results. (3) Existing feature extraction strategies limit the flexibility of search expressions. In this paper, we propose a Multi-Character Searchable Encryption scheme (MCSE) that overcomes the aforementioned barriers. To resist correlation attacks, we design the randomize pad model to encrypt the vector. To eradicate false positives, we apply the vector space model and complete feature extraction strategies so that a feature set uniquely identifies a keyword or expression. To enhance search flexibility, we introduce three distinct feature extraction strategies for keyword expressions, wildcard expressions, and logical expressions, enabling effective multi-character search. These strategies enable indexes to accom modate the search of diverse expressions. Finally, we prove that MCSE is indistinguishable against chosen-feature attacks and implement MCSE on two real datasets. Compared with state-of the-art schemes, the experiment results show that MCSE achieves good performance.

Image segmentation has found widespread applications in computer vision, particularly in fields such as medical image analysis, autonomous driving, and video surveillance. However, as the complexity of segmentation tasks increases, new vision foundation models have emerged, notably the Segment Anything Model (SAM) series. By leveraging large-scale training and cross-domain generalization, SAM2 has significantly enhanced the performance of image segmentation. However, despite these advancements, SAM2 still generates class-agnostic segmentation results in the absence of manual prompts. To enhance the SAM2's performance in these scenarios, we propose an effective framework, termed SAM2-Cap. Specifically, we integrate the backbone of SAM2 with a Capsule Autoencoder, exploiting the inherent properties of capsule networks to effectively capture and decode the spatial hierarchical relationships between the parts and objects. This improves the modeling of object shapes and spatial relationships, enhancing the understanding and segmentation accuracy of complex objects while reducing the reliance on prompt information. Extensive experiments on Prostate MRI segmentation, Polyp segmentation and Cityscapes dataset, demonstrate our method achieves competitive results compared to state-of-the-art models.

Estimating 3D human poses from monocular videos is a challenging task, primarily due to self-occlusion. Many existing methods struggle with unseen viewpoints as they rely on large amounts of data rather than enhancing their generalization ability across different viewpoints. To overcome this limitation, we propose a novel approach using a capsule autoencoder integrated with the shifted-windows model (SwinCAE), which can enhance prediction accuracy by effectively capturing the spatial hierarchical relationship between the parts and objects. Furthermore, we build a Parallel Double Attention with Shifted Windows module to enhance computational efficiency and modeling capacity. Additionally, we construct a Multi-Attention Collaborative module to capture diverse information, including both coarse and fine details. Through the collaboration of these modules, the model representation is significantly improved, resulting in a more accurate generated pose. Extensive experiments demonstrate that SwinCAE achieves better or comparable results to state-of-the-art models about 3D human pose estimation task.

Deep neural network (DNN)-based vehicular cameras, capable of detecting multiple road objects with high accuracy, enable critical autonomous driving applications such as lane changing and overtaking. However, their vulnerability to adversarial attacks exposes them susceptible to potential security risks. Recent studies indicate that adversarial patches can impair detector functionality by modifying vehicle perception attributes, such as color and distance. Currently, the design of effective adversarial patch attacks targeting vehicle bounding boxes has not been thoroughly investigated. Incorrect bounding boxes lead to the detector marking inaccurate vehicle locations, causing the vehicular system to make erroneous decisions (e.g., path deviation). In this paper, we propose BPATCH, an adversarial patch attack targeting the perception of vehicular bounding boxes. Our attack targets DNN-based camera detectors, leading to inaccurate recognition of both the size and location of the bounding box. We design a two-stage training process, where a random patch is first transformed and applied based on various factors for robustness, and then the output is used to optimize meaningful vehicular stickers for stealthiness in practical applications. We in particular introduce new loss functions to enhance the effectiveness of the attack. Extensive experiments on two datasets and the real-world application demonstrate the effectiveness of our method across various DNN-based models and stickers.

Generative models have demonstrated remarkable capabilities in synthesizing realistic content, creating new opportunities for secure communication through steganography---the practice of embedding covert messages within seemingly innocuous data. While prefix-based steganography, which encodes secret messages into shared probability intervals during generative sampling, has emerged as a promising paradigm for provably secure communication, its practical adoption remains constrained by inherent tradeoffs between security, capacity, and efficiency. To address these challenges, we propose two enhancements. The first enhancement optimizes quantization distortion in existing frameworks to minimize KL divergence, thereby enhancing theoretical security. The second redesigns the sampling mechanism via distribution coupling to amplify steganographic capacity, achieving this without incurring substantial computational overhead. Experimental validation on text generation task confirms our enhancements substantially outperform previous implementations, demonstrating notable capacity improvements, marked security enhancements, and efficiency gains on consumer-grade hardware. Cross-task comparisons with popular provably secure steganography further establish the proposed enhancements as achieving superior security-capacity-efficiency tradeoffs across diverse generative scenarios, advancing the practical deployment of provably secure steganography systems.

Due to the steganography of hiding images requires that the secret message be a full-size image, to improve the universality of steganography and decoding accuracy than hiding images, this paper presents image steganography with the deep orthogonal fusion of multi-scale channel attention (SOFMC). To achieve this, the secret data is reshaped into a three-dimensional tensor and fused into shallow and deep representations of the cover image. The deep orthogonal fusion of multi-scale channel attention (OFMC) is designed to calibrate the relationships among the channels. The OFMC can yield the channel attention vectors of the feature map at different scales and use orthogonal fusion to integrate the above channel information of different receptive fields. In the specific implementation, we encapsulate OFMC into the blocks of the basic network in the generator and extractor. The designed blocks are highly flexible and can be cascaded by dense connection as needed. Experimental results demonstrate that SOFMC is superior to previous methods in the decoding accuracy, security, and quality of stego images.

Generative image steganography has gained significant attention due to its ability to hide secret data during image generation. However, existing generative image steganography methods still face challenges in terms of controllability, usability, and robustness, making it difficult to apply real-world scenarios. We propose a practical and robust generative image steganography based on Latent Diffusion Models, called LDStega. LDStega takes controllable condition text as input and designs an encoding strategy in the reverse process of the Latent Diffusion Models to couple latent space generation with data hiding. The encoding strategy selects a sampling interval from a candidate pool of truncated Gaussian distributions guided by secret data to generate the stego latent space. Subsequently, the stego latent space is fed into the Decoder to generate the stego image. The receiver extracts the secret data from the globally Gaussian distribution of the lossy-reconstructed latent space in the reverse process. Experimental results demonstrate that LDStega achieves high extraction accuracy while controllably generating image content and saving the stego image in the widely used PNG and JPEG formats. Additionally, LDStega outperforms state-of-the-art techniques in resisting common image attacks.

LiDAR significantly contributes to autonomous driving systems (ADSs) through its perception, prediction and decision layers. Recent research has focused on implementing adversarial attack on LiDAR-based ADS by generating perturbed point cloud adversarial samples. However, most state-of-the-art attacks focus on stationary scenarios, making it difficult to apply them in dynamic scenarios with multiframe point clouds. In this article, we introduce SpotAttack, a novel adversarial attack that targets specific areas of a vehicle’s surface using distributed patches. Unlike traditional adversarial mechanism that mislead the object classification through pixel perturbations, our designed spots decrease the reflectivity of LiDAR rays, causing the point clouds in these patches to be obscured. As a result, the 3-D object detection network will produce incorrect pose estimations based on the adversarial point cloud samples. To ensure the effectiveness of SpotAttack in dynamic scenarios, we establish a position matrix for multiobjective optimization and adopt genetic algorithm (GA) to address the nondifferentiable issue in spots generation. We conduct extensive experiments in three typical scenarios, and the results demonstrate that the proposed attack can manipulate LiDAR perception and influence ADS decision making.

Advancements in speech synthesis technology bring generated speech closer to natural human voices, but they also introduce a series of potential risks, such as the dissemination of false information and voice impersonation. Therefore, it becomes significant to detect any potential misuse of the released speech content. This letter introduces an active strategy that combines audio watermarking with the HiFi-GAN vocoder to embed an invisible watermark in all synthesized speech for detection purposes. We first pre-train a watermark extraction network as the watermark extractor, and then use the watermark extraction loss and speech quality loss of the extractor to adjust the HiFi-GAN generator to ensure that the watermark can be extracted from the synthesized speech. We evaluate the imperceptibility and robustness of the watermark across various speech synthesis models. The experimental results demonstrate that our method effectively withstands various attacks and exhibits excellent imperceptibility. Moreover, our method is universal and compatible with various vocoder-based speech synthesis models.

Generative steganography has become a popular research topic in the field of generative AI, including generative image and synthetic speech steganography. However, music files have different statistical properties and knowledge representation compared to image and speech files, and the reversible transform between secret message and music is also challenging. Therefore, the existing generative steganographic methods that are effective for image/speech may not be directly effective for music. In this paper, we propose a generative music steganography method, named MIDI-GAN, to generate a secret message as an artificial stego MIDI file using generative adversarial networks (GANs). The created stego MIDI file is small in size, has sweet melodies, and is undetectable to deep learning-based steganalyzers. Unlike the previous generative image/speech steganography, the stego MIDI can also be presented as a sequence of chord numbers, making it difficult for anyone to detect and see grounds for suspicion. Moreover, these chord numbers can be transmitted as any other digital or physical medium to evade detection. Specifically, MIDI-GAN comprises a generator, a discriminator, and an extractor. The generator synthesizes a stego MIDI file from the secret message, while the discriminator ensures that the stego MIDI file approaches the authentic rather than the synthetic MIDI file as much as possible in statistical distribution. The extractor recovers the secret message from the stego MIDI file or chord sequence. Experimental results demonstrate that MIDI-GAN has high concealment and security, as the stego MIDI generated by our method is closely similar to the authentic MIDI files and maintains excellent anti-detection ability against deep learning-based steganalysis.

Wildcard Keyword Searchable Encryption (WKSE) enables users to search desired encrypted files with wildcard queries. Previous schemes only enabled single-character wildcard queries or restricted multi-character wildcard queries. Even if the two types of queries are supported by several schemes, they are vulnerable to correlation attacks and composition attacks. In this paper, we propose a WKSE scheme Secure Flexible Wildcard Queries (SFWQ) that supports highly flexible wildcard queries and resists correlation and composition attacks. Specifically, we adopt the interval matching method instead of traditional position matching, so that SFWQ supports a variety of queries, including single-character wildcard queries, multi-character wildcard queries, and mixed wildcard queries that the combination of both single-character and multi-character wildcards within the same query. Moreover, the number and position of wildcards within wildcard keywords are adjustable according to user preference. To resist the correlation attack and composition attack, we leverage key aggregate searchable encryption (KASE) and key exchange protocol to process characters so that even the same characters of the same keyword behave as different ciphertexts. We define a security model for WKSE which catches the correlation attack and composition attack. Our proof validates SFWQ is secure under the security model. Finally, we implement SFWQ and compare it with state-of-the-art schemes. The experimental results demonstrate that our scheme is feasible and efficient.

Federated learning (FL) is a promising paradigm for collaboratively training networks on distributed clients while retaining data locally. Recent work has shown that personal data can be recovered even though clients only send gradients to the server. To against the gradient leakage issue, differential privacy (DP)-based solutions are proposed to protect data privacy by adding noise to the gradient before sending it to the server. However, the introduced noise affects the training efficiency of local clients, resulting in low model accuracy. Moreover, the identity privacy of clients has not been seriously considered in FL. In this article, we propose FL2DP, a privacy-preserving scheme focusing on protecting the data privacy as well as the identity privacy of clients. Different from the current schemes that add noise sampled from the Gaussian or Laplace distribution, in our scheme the noise is added to the gradient based on the exponential mechanism to achieve high training efficiency. Then, clients upload the perturbed gradients to a shuffler, which reassigns these gradients with different identities. We give a formal privacy definition called gradient indistinguishability to provide strict unlinkability for gradients shuffle. We propose a new gradient shuffling mechanism by adapting the DP-based exponential mechanism to satisfy gradient indistinguishability using the designed utility function. In this case, an attacker cannot infer the real identity of the client via the shuffled gradient. We conduct extensive experiments on two real-world datasets, and the results demonstrate the effectiveness of the proposed scheme.

The emergence of vehicular applications such as collision warning enhances traffic efficiency and the driving experience of users. Due to the features of decentralization, complexity, and heterogeneity in intelligent transportation systems, the key issue is ensuring the legality and trustworthiness of all participating parties. Traditional methodologies such as public key infrastructure provide services for verifying the legitimacy of entities with certificates. However, even if a vehicle is legally registered, trust in its behavior is not always assured. Recently decentralized trust management systems (DTMS) are proposed to solve the trust issue. However, achieving both dependability and efficiency remains a challenge. In this paper, we propose a novel DTMS based on the consortium blockchain. We in particular focus on the dependability of trust evaluation, where trust computation is performed on both vehicles and RSUs. To achieve a sustainable trust environment, we propose an incentive model whereby the raters can earn rewards for providing honest ratings. Instead of utilizing the consensus algorithm based on the Proof of Work (PoW) or Proof of Stake (PoS), we implement a verifiable delay function (VDF)-based consensus model within the trusted execution environment (TEE) to ensure efficiency and security for the consortium blockchain. In addition, we design a smart contract on top of the blockchain to assist the system in detecting a specific attack against the trust system, known as the on-off attack. Through extensive experiments, the results of trust assessment and blockchain performance demonstrate the dependability and efficiency of our proposed DTMS.

As the origin of blockchains, the Nakamoto Consensus protocol is the primary protocol for many public blockchains (e.g., Bitcoin) used in cryptocurrencies. Blockchains need to be decentralized as a core feature, yet it is difficult to strike a balance between scalability and security. Many approaches to improving blockchain scalability often result in diminished security or compromise the decentralized nature of the system. Inspired by network science, especially the epidemic model, we try to solve this problem by mapping the propagation of transactions and blocks as two interacting epidemics, called the CoSIS model. We extend the transaction propagation process to increase the efficiency of block propagation, which reduces the number of unknown transactions. The reduction of the block propagation latency ultimately increases the blockchain throughput. The theory of complex networks is employed to offer an optimal boundary condition. Finally, the node scores are stored in the chain, so that it also provides a new incentive approach. Our experiments show that CoSIS accelerates blocks’ propagation and TPS is raised by 20%$\sim ~33$% on average. At the same time, the system security can be significantly improved, as an orphaned block rate is close to zero in better cases. CoSIS enhances the scalability and security of the blockchain while ensuring that all changes do not compromise the decentralized nature of the blockchain.

Image steganography is the technology of concealing secret messages within an image. Recently, generative image steganography has been developed, which conceals secret messages during image generation. However, existing generative image steganography schemes are often criticized for their poor steganographic capacity and extraction accuracy. To ensure secure and dependable communication, we propose a novel generative image steganography based on the denoising diffusion probabilistic model, called StegaDDPM. StegaDDPM utilizes the probability distribution between the intermediate state and generated image in the reverse process of the diffusion model. The secret message is hidden in the generated image through message sampling, which follows the same probability distribution as normal generation. The receiver uses two shared random seeds to reproduce the reverse process and accurately extract secret data. Experimental results show that StegaDDPM outperforms state-of-the-art methods in terms of steganographic capacity, extraction accuracy, and security. In addition, it can securely conceal and accurately extract secret messages up to 9 bits per pixel.

Vehicular Digital Forensics (VDF) is essential to enable liability cognizance of accidents and fight against crimes. Ensuring the authority to timely gather, analyze, and trace data promotes vehicular investigations. However, adversaries crave the identity of the data provider/user, damage the evidence, violate evidence jurisdiction, and leak evidence. Therefore, protecting privacy and evidence accountability while guaranteeing access control and traceability in VDF is no easy task. To address the above-mentioned issues, we propose Eunomia: an anonymous and secure VDF scheme based on blockchain. It preserves privacy with decentralized anonymous credentials without trusted third parties. Vehicular data and evidence are uploaded by data providers to the blockchain and stored in distributed data storage. Each investigation is modeled as a finite state machine with state transitions being executed by smart contracts. Eunomia achieves fine-grained evidence access control via ciphertext-policy attribute-based encryption and Bulletproofs. A user must hold specific attributes and a temporary-and-unexpired token/warrant to retrieve data from the blockchain. Finally, a secret key is embedded into data to trace the traitor if any evidence breach happens. We use a formal analysis to demonstrate the strong privacy and security properties of Eunomia. Moreover, we build a prototype in a WiFi-based Ethereum test network to evaluate its performance.

In the era of big data, data sharing not only boosts the economy of the world but also brings about problems of privacy disclosure and copyright infringement. The collected data may contain users’ sensitive information; thus, privacy protection should be applied to the data prior to them being shared. Moreover, the shared data may be re-shared to third parties without the consent or awareness of the original data providers. Therefore, there is an urgent need for copyright tracking. There are few works satisfying the requirements of both privacy protection and copyright tracking. The main challenge is how to protect the shared data and realize copyright tracking while not undermining the utility of the data. In this article, we propose a novel solution of a reversible database watermarking scheme based on order-preserving encryption. First, we encrypt the data using order-preserving encryption and adjust an encryption parameter within an appropriate interval to generate a ciphertext with redundant space. Then, we leverage the redundant space to embed robust reversible watermarking. We adopt grouping and K-means to improve the embedding capacity and the robustness of the watermark. Formal theoretical analysis proves that the proposed scheme guarantees correctness and security. Results of extensive experiments show that OPEW has 100% data utility, and the robustness and efficiency of OPEW are better than existing works.

Traffic-sensitive navigation systems in vehicular networks help drivers avoid traffic jams by providing several realtime navigation routes. However, drivers still encounter privacy concerns because their sensitive locations, i.e., their start point and endpoint, are submitted to an honest-but-curious navigation service provider (NSP). Previous privacy-preserving studies exhibit serious deficiencies under similar queries: if a driver makes several similar queries, i.e., periodically makes requests for the same start point and endpoint to the NSP, these requests will eventually reveal the areas of the two points as well as the route. In this paper, we present a novel privacy-preserving navigation scheme PiSim, which supports similar queries in navigation services. Intuitively, we transform the typical navigation approach into a traffic congestion querying approach. Instead of sending two locations to the NSP and awaiting a navigation route, drivers query the traffic congestion along the navigation route. Specifically, PiSim is characterized by extending anonymous authentication, facilitating privacy-preserving multi-keyword fuzzy search, and constructing weighted proximity graphs. Our scheme protects location privacy and route privacy, and defends against multiple requesting, spurious reporting, and collusion attacks from malicious drivers. Finally, a detailed analysis confirms the privacy and security properties of PiSim. Extensive experiments are conducted to demonstrate the feasibility, performance, and privacy protection level.

Internet users receive various online social networks (OSNs) services, however, providers of OSNs do not always provide users fine-grained privacy protection mechanisms with sufficient privacy protection for shared resources. In this paper, we propose a formal Group-Based Access Control (oGBAC) framework for preventing privacy disclosure when sharing information within or among groups in OSNs. Our framework extends the group-centric Secure Information Sharing (g-SIS) models by adapting the concept of the group to OSNs. We impose some restrictions to the group and information flow among groups to ensure that operations cannot incur privacy disclosure when sharing information among friends in OSNs. In view of characteristics of OSNs and the requirements of secure information flow, the oGBAC model also incorporates some ideas from the Attribute-Based Access Control (ABAC) to develop information flow based rules using relationship among attributes (such as tags, time and security levels) of objects and subjects in OSNs. Administration related rules and access related rules are designed for each access operation of group based OSNs' information sharing. The security of oGBAC model is analyzed using formal methods. To demonstrate the usability of the oGBAC model, we implement the model with the Comparative Attribute-Based Encryption (CCP-CABE), and analyze the security and efficiency of the implemented system to prove the effectiveness of the implemented system.

With the development of cloud server, reversible data hiding in encrypted domain has received widespread attention for management of encrypted media. Most of existing reversible data hiding methods are based on stream ciphers, which are mainly concerned with data storage security. While homomorphic ciphers focus on data processing security, which is popular in cloud and other third-party platforms. Reversible data hiding with homomorphic cryptography has been an active topic in the research filed. In this paper, we propose a new lossless data hiding method based on homomorphic cryptosystem. After a content owner generates encrypted media with homomorphic ciphers, a data hider embeds secret data by establishing a mapping between secret bits and losslessly modifying encrypted media. At the receiver side, the embedded data is extracted from encrypted domain with a little auxiliary message. And original media is recovered by directly decrypting marked encrypted media. During the embedding process, the modification of encrypted media does not change the corresponding original media owing to homomorphic and probabilistic properties. Thus, no distortion is introduced in the whole procedures of data embedding. And directly decrypted media containing embedded data is the same as the original media. Meanwhile, the proposed method achieves a high embedding rate through efficient mapping and skillful use of expanded pixel values. The experimental results show that compared with state-of-the-art methods, the proposed method has higher embedding rate without distortion.

Energy trading in Industrial Internet of Things (IIoT), a fundamental approach to realize Industry 4.0, plays a vital role in satisfying energy demands and optimizing system efficiency. Existing research works utilize a utility company to distribute energy to energy nodes with the help of energy brokers. Afterwards, they apply blockchain to provide transparency, immutability, and auditability of peer-to-peer (P2P) energy trading. However, their schemes are constructed on a weak security model and do not consider the cheating attack initiated by energy sellers. Such an attack refers to an energy seller refusing to transfer the negotiated energy to an energy purchaser who already paid money. In this article, we propose FeneChain, a blockchain-based energy trading scheme to supervise and manage the energy trading process toward building a secure energy trading system and improving energy quality for Industry 4.0. Specifically, we leverage anonymous authentication to protect user privacy, and we design a timed-commitments-based mechanism to guarantee the verifiable fairness during energy trading. Moreover, we utilize fine-grained access control for energy trading services. We also build a consortium blockchain among energy brokers to verify and record energy trading transactions. Finally, we formally analyze the security and privacy of FeneChain and evaluate its performance (i.e., computational costs and communication overhead) by implementing a prototype via a local Ethereum test network and Raspberry Pi.

In the shadow and water areas of InSAR data, the coherence is low and the interferometric phase is noisy. In these areas, phase unwrapping is a difficult problem as the phase error is prone to spread to other areas while integrating path crosses the noisy area. In order to avoid the unwrapping error, interferometric phase characteristics of these areas are analysed in theory according to their terrain in this paper. Based on the analysis, other than the usual strategy of setting integral path to restrict discontinuities spreading, phase compensation methods are proposed to avoid wrong unwrapping phase. The experiment using airborne InSAR data verifies the effectiveness of this method.

Nowadays information is crucial in many fields such as medicine, science and business, where databases are used effectively for information sharing. However, the databases face the risk of being pirated, stolen or misused, which may result in a lot of security threats concerning ownership rights, data tampering and privacy protection. Watermarking is utilized to enforce ownership rights on shared relational databases. Many reversible watermarking methods are proposed recently to protect rights of owners along with recovering original data. Most state-of-the-art methods modify the original data to a large extent, result in data quality degradation, and cannot achieve good balance between robustness against malicious attacks and data recovery. In this paper, we propose a robust and reversible database watermarking technique, Genetic Algorithm and Histogram Shifting Watermarking (GAHSW), for numerical relational database. The genetic algorithm is used to select the best secret key for grouping database, where the watermarking can be embedded with balanced distortion and capacity. The histogram of the prediction error is shifted to embed the watermark with good robustness. Experimental results demonstrate the effectiveness of GAHSW and show that it outperforms state-of-the-art approaches in terms of robustness against malicious attacks and preservation of data quality.

Research on adaptive steganography mainly focuses on how to design a reasonable cost function and how to utilize that cost function to achieve embedding in a stego image with the minimal distortion based on syndrome-trellis codes. Because previous adaptive steganographic methods use convolution with filters to obtain the residuals, these methods do not make good use of the textures of the image itself in the design of the cost function. In this letter, we define a new cost function that uses nonnegative matrix factorization to predict the image pixels and utilizes the mutual dependencies among the pixels to calculate the costs. We present a novel cost function in which the residuals are not calculated via convolution with constant filters. Experimental results show that our method outperforms the state-of-the-art MiPOD, spatial universal wavelet relative distortion, wavelet obtained weights, and HUGO-BD methods in resisting steganalysis based on the spatial rich model and is slightly superior to the high-pass, low-pass, low-pass method.

A new image formation algorithm is proposed for processing very high resolution spaceborne sliding-spotlight synthetic aperture radar (SAR) data. Because of along-track antenna steering, the Doppler bandwidth of the received SAR data is expanded significantly beyond one pulse repetition frequency interval. Furthermore, the range histories become spatially dependent in both dimensions and cannot be expressed exactly by a hyperbolic model. In our approach, we first reduce the Doppler bandwidth by a novel azimuth dechirp processing method in the range frequency domain. The data are then processed by the standard ω-κ algorithm with a fixed effective velocity. Thereafter, the chirp modulation concept is imported to rebuild new data with much shorter apertures. Finally, a standard back-projection algorithm is employed to accumulate the signal pixel by pixel along the newly built aperture. Thus, the balance between processing efficiency and precision can be controlled by adjusting the length of the new apertures. In addition, a more accurate 2-D spectrum derivation is employed to enhance the processing precision, and a novel range-splitting method is presented to accommodate the range dependence of effective velocities. Furthermore, when implementing the back projection, the image grid-the region and granularity level of which are user defined-is placed on the earth's surface instead of on the slant-range plane, and the routine geometry projection processing thus becomes dispensable.

This paper proposed a new approach to focus the very high resolution spaceborne sliding spotlight synthetic aperture radar (SAR) data. The main singularity of the approach is accommodating the space dependent range histories by a back projection method on reduced apertures achieved by chirp modulation method. Besides, in the range spectrum domain, a dechirp method is employed to reduce the Doppler bandwidth to be within one PRF interval. Additionally, range splitting method is employed to mitigate the range dependence of effective velocities. Furthermore, the image grid is placed on the earth surface instead of the slant range plane, and the routine geometry projection processing becomes dispensable. The proposed approach was validated by simulation results of nine point targets.

Currently, the Android platform is the most popular mobile platform in the world and holds a dominant share in the mobile device market. With the popularization of the Android platform, large numbers of Android malware programs have begun to emerge on the Internet, and the sophistication of these programs is developing rapidly. While many studies have already investigated Android malware detection through machine learning and have achieved good results, most of these are based on static data sources and fail to consider the concept drift problem resulting from the rapid growth in the number of Android malware programs and normal Android applications, as well as rapid technological advancement in the Android environment. To address this problem, this work proposes a solution based on an ensemble classifier. This ensemble classifier is based on a streaming data-based Naive Bayes classifier. Android malware has identifiable feature utilization tendencies. On this basis, feature selection algorithm is introduced into the ensemble classifier, and a sliding window is maintained inside the ensemble classifier. Based on the performance of the subclassifiers inside the sliding window, the ensemble classifier makes dynamic adjustments to address the concept drift problem in Android malware detection. The experimental results from the proposed method demonstrate that it can effectively address the concept drift problem in Android malware detection in a streaming data environment.

Digital image steganalysis is the art of detecting the presence of information hiding in carrier images. When detecting recently developed adaptive image steganography methods, state-of-art steganalysis methods cannot achieve satisfactory detection accuracy, because the adaptive steganography methods can adaptively embed information into regions with rich textures via the guidance of distortion function and thus make the effective steganalysis features hard to be extracted. Inspired by the promising success which convolutional neural network (CNN) has achieved in the fields of digital image analysis, increasing researchers are devoted to designing CNN based steganalysis methods. But as for detecting adaptive steganography methods, the results achieved by CNN based methods are still far from expected. In this paper, we propose a hybrid approach by designing a region selection method and a new CNN framework. In order to make the CNN focus on the regions with complex textures, we design a region selection method by finding a region with the maximal sum of the embedding probabilities. To evolve more diverse and effective steganalysis features, we design a new CNN framework consisting of three separate subnets with independent structure and configuration parameters and then merge and split the three subnets repeatedly. Experimental results indicate that our approach can lead to performance improvement in detecting adaptive steganography.

Doppler walk is introduced by high speed motion of targets in passive radar, which decreases the signal-to-noise ratio. In this paper, a Doppler walk rectification method based on keystone-Wigner-Ville transform (KWT) is proposed, and the interference, weights and noise impact are analyzed. No prior information and parameters searching are unnecessary.

Multichannel synthetic aperture radar promises high-resolution and wide-swath imaging simultaneously. Channel error estimation is a critical step in signal processing before imaging. This letter mainly derives the Cramér-Rao lower bounds (CRLBs) for phase error estimates of three commonly used error estimators. Furthermore, to comprehensively evaluate the estimators, this letter compares both their accuracy and effectiveness. The accuracy is assessed by the maximum estimation deviation among channels, and the effectiveness is assessed by the proximity of the mean square errors (MSEs) to CRLB for phase error estimates. Finally, simulation is conducted to compare the maximum deviation as well as the MSE versus CRLB among the three estimators, under different clutter distributions and signal-to-noise ratios. Combined with the estimation accuracy and effectiveness, this letter aims to provide justifications for the proposed algorithms and gives recommendations for method selection in engineering applications.

The precision of the mutilsquint methods effects by several factors. This paper deduces the effects of these factors on the multisquint estimation accuracy. Expression of the estimation accuracy is deduced, which provides theoretical bases for the parameter choice and system design of the airborne interferometric SAR.

In passive radar, the waveform is not controlled by the system, so strong echoes usually produce high sidelobes in the correlation function. Since the sidelobes can mask the weak targets, strong echo cancellation methods are required. The generalized adaptive notch filter (GANF) is an efficient method compared with the extensive cancellation algorithm. However, the GANF estimates different frequencies separately (in parallel or series), and a point-by-point iterative operation is adopted, which leads to heavy computational burden. This letter presents a multifrequency estimation notch filter which is a simplified GANF based on the signal model. Furthermore, an adaptive block notch filter is proposed to reduce the processing time. The efficiency of the block notch filter is verified by simulations.

Existing low-Earth-orbit synthetic aperture radar (SAR) algorithms generally assume that the data are azimuth invariant. However, this assumption does not hold for the medium-Earth-orbit (MEO) SAR systems due to the significantly longer azimuth integration time and complex imaging geometries. As a result, the MEO SAR data cannot be processed accurately and efficiently using the existing algorithms. To solve this problem, this letter proposes a two-step azimuth perturbation (AP) method that uses the first-step AP to remove the bulk azimuth variance at the range processing stage and the second-step AP to remove the residual variance at the azimuth processing stage. As an example, an improved range Doppler algorithm with the integrated two-step AP is discussed in this letter. Simulations of an L-band MEO SAR with 5-m resolution at 10 000-km orbit height are used to demonstrate the validity and accuracy of this algorithm.

Interferometric phase filtering is an indispensable step to obtain accurate measurement of digital elevation model and surface displacement. In the case of low-correlation or complicated topography, traditional phase filtering methods fail in balancing noise elimination and phase preservation, which leads to inaccurate interferometric phase. A new nonlocal interferometric phase filtering method taking advantage of higher order singular value decomposition (HOSVD) is proposed in this letter. For each pixel of the interferometric phase, a 3-D data array is established, and shrinkage is applied after HOSVD. A Wiener filter is used to improve the denoising performance in the end. Simulated and real data are employed to validate that the proposed method outperforms other traditional methods and some of the state-of-the-art nonlocal methods.

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, some approximations, which are valid for SAR systems of moderate aperture length, are usually adopted in commonly used MOCO approaches. However, a much more accurate SAR data processing approach is appealing to process the low-frequency SAR systems with large aperture length, such as P-band. In this paper, a new MOCO approach with high precision and high efficiency is proposed. After the ω - κ processing and the range-dependent MOCO, the analytical expression of a 2-D spectrum of a partially focused SAR image is given. Afterward, aperture reduction is achieved by a chirp modulation technique. Finally, with high precision and less computation cost, back projection along the new built short apertures (affected by the residual motion errors) is employed to yield a fairly well-focused SAR image. Experimental results on simulated and actual P-band SAR data are presented to verify the performance of the proposed approach.

The geometry distortion phenomenon of SAR imaging arises in the mountainous scenarios due to the presence of strong terrain slopes. Because of phase discontinuities or the absence of valid phase, for single pass interferometric SAR (InSAR), it is difficult to recover accurate digital elevation model (DEM) in such areas. Fusion of two or more different aspects of InSAR data is practicable to deal with this problem. In this paper, the processing procedures of airborne InSAR data are presented. In order to decrease the processing error of every single aspect data, an iterative motion compensation (MOCO) method is used. Besides, the interferometric phase of shadow area is linearly complemented before phase unwrapping to avoid error spreading. Experimental results using two anti-parallel aspects of airborne InSAR data validate the feasibility of fusion.

In the area of airborne synthetic aperture radar (SAR), motion compensation (MOCO) is a crucial technique employed to correct the SAR data affected by nonlinear platform trajectory during data acquisition. Due to range-azimuth coupling and computational burden consideration, MOCO is usually implemented only with respect to range dependent motion errors, which is adequate for SAR systems of moderate aperture length. However, a significantly more accurate SAR processing approach is desired for processing low frequency SAR systems with large aperture lengths, such as P-band. In this paper, we propose a new airborne SAR processing algorithm that considers both the high efficiency of the ω-κ algorithm and the high precision of the back projection algorithm. Experimental results on simulated and actual P-band SAR data are presented to verify the effectiveness of the proposed approach.

In passive radars, coherent integration is an essential method to achieve processing gain for target detection. The cross ambiguity function (CAF) and the method based on matched filtering are the most common approaches. The method based on matched filtering is an approximation to CAF and the procedure is: (1) divide the signal into snapshots; (2) perform matched filtering on each snapshot; (3) perform fast Fourier transform (FFT) across the snapshots. The matched filtering method is computationally affordable and can offer savings of an order of 1000 times in execution speed over that of CAF. However, matched filtering suffers from severe energy loss for high speed targets. In this paper we concentrate mainly on the matched filtering method and we use keystone transform to rectify range migration. Several factors affecting the performance of coherent integration are discussed based on the matched filtering method and keystone transform. Modified methods are introduced to improve the performance by analyzing the impacts of mismatching, precision of the keystone transform, and discretization. The modified discrete chirp Fourier transform (MDCFT) is adopted to rectify the Doppler expansion in a multi-target scenario. A novel velocity estimation method is proposed, and an extended processing scheme presented. Simulations show that the proposed algorithms improve the performance of matched filtering for high speed targets.

A novel method of interferometric synthetic aperture radar phase filtering that combines empirical mode decomposition (EMD) with Hölder exponent adjustment is presented in this letter. First, intrinsic mode functions (IMFs) of different levels are obtained by decomposing the real and imaginary parts of the noisy interferometric phase in complex formulation respectively employing EMD, which is a totally data-driven method without parameters to be selected. Then, we increase the Hölder exponents of every IMF to appropriate extent according to the features of the signal and noise contained in them to realize different filtering effects. Thus, noise can be efficiently filtered without the loss of detailed information of the interferogram. Finally, the filtered IMFs are reconstructed to form the denoised interferogram. The experiments of simulated data with various correlation coefficients and real data verify the effectiveness and adaptability of the method.

Due to the significant azimuth variance property in medium-Earth-orbit (MEO) synthetic aperture radar (SAR) echo, it is difficult for the conventional SAR algorithms to achieve a good compromise between accuracy and efficiency. A novel algorithm based on Keystone transform (KT) and azimuth perturbation (AP) is introduced in this paper to handle this problem. The function of KT is to correct the range walk and thus to mitigate the azimuth variance effect on range processing. The function of AP is to equalize the Doppler histories in each range gate and thus to mitigate the azimuth variance effect on azimuth compressing. Simulation results of an L-band MEO SAR with 5 m resolution at 10,000 km altitude demonstrate the capability of our algorithm.

The layover phenomenon of SAR imaging arises when different height contributions collapse in the same range-azimuth resolution cell, due to the presence of strong terrain slopes or discontinuities in the scenarios. Because of the phase discontinuities, for single baseline interferometric SAR, it is difficult to recover the accurate unwrapped phase in layover areas by traditional phase unwrapping methods. In this paper, according to the phase characteristic of layover areas, we propose a new method to retrieve unwrapped phase based on local frequency estimate. It can avoid the unwrapping error resulted from the phase jump at the edge of layover areas. As a result, relative accurate DEM of layover areas can be reconstructed, which is beneficial to afterward geocoding in InSAR topographic mapping.

A novel parameter estimation algorithm of fast moving targets using single-antenna airborne synthetic aperture radar (SAR) databased on desampling and Radon transform (RT) is introduced in this letter. First, the dual-channel data are constructed by desampling the single-antenna airborne SAR data in the azimuth direction. Then, the clutter and the spectrum aliasing of the moving target can be cancelled by coherent subtracting. As a result, the moving target trajectory exhibits a single curve in both the range-compressed and the range-Doppler domains. Second, range cell migration correction is adopted to eliminate the range curve and parts of the range walk. Owing to the Doppler ambiguity, the moving target trajectory becomes a straight line. Third, the desampled Doppler ambiguity number and the Doppler rate of the moving target can be calculated by the slope of the line, which is measured by RT. Finally, along- and across-track velocities of the moving target are further obtained. The effectiveness of the proposed scheme is validated by the simulated and real data.

The signal processing of the medium-Earth-orbit synthetic aperture radar (SAR) is more challenging than that of the current low-Earth-orbit SAR because the imaging geometry is more complicated, and the range and azimuth variances are more severe. This paper deals with these imaging problems in three aspects. First, an advanced hyperbolic range equation (AHRE) is proposed for the first time, which is more precise for a spaceborne SAR than the conventional hyperbolic range equation (CHRE). Second, the point target spectrum based on the AHRE is analytically derived, which is useful for developing efficient SAR processing algorithms. Third, the well-known nonlinear chirp scaling (NLCS) algorithm is modified according to this new spectrum, and the so-called AHRE-based advanced NLCS (A-NLCS) algorithm is established. The simulation results validate the correctness of our method for L-band SAR systems at altitudes from 1000 to 10 000 km with an azimuth resolution around 3 m. It is also shown that the A-NLCS algorithm has better performance than the CHRE-based algorithms in longer integration time cases. Therefore, we recommend the A-NLCS algorithm for a spaceborne SAR with a lower frequency, finer resolution, and higher satellite altitude.

A synthetic aperture radar (SAR) raw data simulator is an important tool for testing the system parameters and the imaging algorithms. In this paper, a scene raw data simulator based on an inverse ω-kalgorithm for bistatic SAR of a translational invariant case is proposed. The differences between simulations of monostatic and bistatic SAR are also described. The algorithm proposed has high precision and can be used in long-baseline configuration and for single-pass interferometry. Implementation details are described, and plenty of simulation results are provided to validate the algorithm.

Processing bistatic SAR image all through numerical calculation is the concept of NuSAR. In this paper, the block scheme of NuSAR is modified to handle the translational variant case. Then a new calculation method of NuSAR is provided, which saves half of the memory compared with the existing calculation method. The provided new NuSAR is practical and can handle the spaceborne-airborne configuration.

As the Doppler history depends both on the transmit range and the receive range in bistatic SAR, those targets, who have the same bistatic range but different locations, will have different doppler history. So the unknown terrain undulation will cause defocusing in bistatic SAR imaging. This paper firstly analyzes which parameters in bistatic configuration affect the defocusing severely, and then shows the simulation results to testify the analysis. Besides, it points out the 3D imaging ability (though very weak) of bistatic SAR based on the defocusing phenomena. And Finally the 3D imaging results are also exhibited.

Forward-looking imaging has many potential applications, but it is impossible with the usual monostatic synthetic aperture radar (SAR) principle. Through the bistatic SAR configuration, forward-looking imaging can be realized for one of the bistatic platforms. This letter designs a bistatic configuration with a stationary transmitter and a forward-looking airborne receiver. It then analyzes the 2-D resolution and finds out which geometric parameter affects the imaging ability mostly. Besides, it gives out the signal formulation in the frequency domain and shows its imaging characteristics. Then, an imaging method is chosen for this special configuration, and the simulation results are exhibited, which validate the correctness of the analysis and prove the 2-D imaging ability of forward-looking bistatic SAR.

This paper first shows the 3D property of bistatic synthetic aperture radar (BiSAR) geometry, which clarifies that the algorithms for bistatic SAR should be deduced in 3D space. It then models the bistatic echo according to the 3D geometry and obtains the signal spectrum in the wavenumber domain. Based on the spectrum, the formula for the wavenumber-domain interpolation of the omega-K algorithm is deduced, and the residual phase is obtained. Then, the impacts of the residual phase, including position displacement, range, and azimuth defocusing, and a constant phase for each pixel, are explicated. Finally, the simulating results exhibited at the end of this paper validate the correctness of the analysis and the feasibility of the algorithm.

This paper deals with the imaging problem of one-stationary bistatic SAR (BiSAR) with large bistatic angle. An improved nonlinear chirp scaling (NLCS) algorithm is proposed for this BiSAR. The main work here includes three aspects. First, a range chirp scaling function for correcting the differential range cell migration correction is derived. Then, the azimuth perturbation is generated by local fit method, which makes the NLCS algorithm suitable for the large bistatic angle case. Furthermore, the negative effects introduced by the perturbation (including phase error and locality error) are discussed, and some compensation methods are proposed to enhance the capability of the algorithm. The simulating results exhibited at the end of this paper validate the correctness of the analysis and the feasibility of the algorithm.