With the rapid development of the low-altitude economy, skyline queries play a crucial role in identifying relevant data based on specific query requirements. To reduce storage overhead and improve query efficiency, data owners increasingly outsource their data to cloud servers. However, cloud servers may be untrusted and can potentially return incorrect or incomplete query results. Furthermore, most existing skyline query schemes do not support dynamic data updates and fail to satisfy the privacy and verifiability requirements essential for real-world low-altitude economic scenarios. In this paper, we propose a Secure Dynamic and Verifiable Skyline Query (SDVSQ) scheme, which supports dynamic and verifiable searchable encryption for skyline queries. We first devise a novel index structure, SDVR-tree, designed for efficient skyline query processing, where each data object is represented as a linked list in the leaf nodes. Each node in the linked list corresponds to a raw data object encrypted using a modified Paillier cryptosystem, ensuring that users accessing a list node cannot infer its sub-nodes. To support secure skyline computation, we design privacy-preserving protocols for squared Euclidean distance, comparison, and minimum operations. Additionally, SDVSQ ensures public verifiability of query result correctness and completeness by leveraging blockchain to store verification objects, while avoiding heavy on-chain computation. Formal security analysis shows that SDVSQ ensures forward privacy, data privacy, and query privacy while supporting result verification. Extensive experimental results demonstrate that SDVSQ significantly reduces computational overhead for both updates and skyline queries.

Face-swapping technologies have rapidly emerged as a mainstream AI service across entertainment, social media, and virtual platforms. While current face-swapping methods offer highly realistic results, they also introduce significant privacy risks, as most current approaches require clear target faces, exposing users’ identities. Moreover, the absence of built-in authorization and forensic mechanisms renders these systems incapable of tracing or verifying manipulated content, raising critical issues over accountability and potential misuse. To address these challenges, we propose a privacy-preserving and forensics-enabled face-swapping framework that simultaneously safeguards user identity and enables robust post-hoc face recovery. Instead of relying on visible target faces, our method operates on non-facial target images, fundamentally preventing identity exposure at the source. To ensure provenance traceability, we embed the target face’s features into non-facial regions of the generated image via an imperceptible and reversible encoding scheme. To further enhance robustness, we introduce a mask-distortion simulation layer that bridges pre-/post-swap mask discrepancies and stabilizes face recovery under perturbations. Extensive experiments demonstrate that our method produces realistic face-swapped images without revealing the original identity, while enabling high-fidelity recovery under various adversarial conditions—validating its effectiveness in both privacy protection and forensic traceability.

In a (k, n)-threshold secret image sharing (SIS) scheme, a secret image is encoded intonshadow images and distributed to the corresponding participants, enabling lossless reconstruction with anykcorrect shadow images. This inherent fault tolerance allows up ton–kshadow images to be lost or corrupted. However, in real-world scenarios, all shadow images are susceptible to malicious tampering, cropping, or noise during transmission and storage, making it difficult to guarantee the availability ofkintact shadows. Robust secret image sharing (RSIS) schemes have been proposed to address this issue, yet existing methods often suffer significant degradation in reconstruction quality as the attack strength increases, revealing limitations in their robustness. To address these issues, we propose an RSIS scheme against malicious shadow images by Reusing Polynomial Coefficients with hash function (RSIS-RPC), which provides both malicious shadow detection and error correction capabilities. The correction capability improves with the degree of coefficient reuse, where greater reuse provides stronger resilience to pixel corruption. However, as more coefficients are reused, the size of the generated shadow images increases correspondingly, resulting in higher storage requirements. This trade-off between robustness and efficiency makes the proposed scheme adaptable to diverse application scenarios requiring secure and resilient image sharing. Experimental results and analyses demonstrate that the proposed scheme achieves superior robustness compared to existing schemes.

Image steganalysis detects hidden information within images. However, existing methods are primarily designed for low-resolution images and they struggle to address the challenges posed by the widespread use of high-resolution images in real-world scenarios such as communications and social media. Under the secure embedding constrained by the square root law, the steganographic noise of high-resolution images is significantly diluted, thereby making “strong decision regions” critical to detection increasingly scarce, whereas the interference effect of “weak decision regions” is relatively prominent. Existing methods treat all areas equally, making it difficult to fully utilize strong decision regions and suppress the negative impact of weak decision regions. To address these issues, we propose the HRIS, a two-phase collaborative optimization framework for high-resolution steganalysis from “discovery” to “utilization”. In the “discovery” phase, we propose a dynamically contribution-guided decision region recognition mechanism. This mechanism employs a difference amplification module to amplify the steganographic noise differences between regions and then leverages a cooperative game-driven dynamic optimization strategy to compute each sub-region's contribution to the prediction. It accurately identifies and reinforces strong decision regions while suppressing interference from weak decision regions, resulting in significantly improved local detection accuracy. In the “utilization” phase, we propose a decision regions-global steganographic features bidirectional collaborative optimization framework that leverages the identified strong and weak decision regions to direct the extraction of global steganographic noise features. These global features are then fed back to refine the local feature representations, enabling collaborative enhancement between local and global analyses. Extensive experimental results demonstrate that our method achieves state-of-the-art performance on high-resolution images.

In recent years, video steganography technique based on H.265/HEVC has received widespread attention. Typically, video steganography selects various syntax elements during the encoding process as carriers, and utilizing Prediction Unit (PU) as carrier is currently one of the most significant research directions. However, due to the limited number of PU types, such algorithms often suffer from insufficient capacity and visual quality. To alleviate the aforementioned issues, this paper proposes an H.265/HEVC video steganography algorithm that utilizes polygon encoding and Improved Deep Learnable Similarity Network Filter (IDLSNF). Firstly, we design a new polygon encoding rule, which maps different integers into several polygons. Secondly, we propose a novel steganography method based on polygon encoding and PU partition mode. This method selects the PUs of$8\times 8$and$16\times 16$coding unit in P-frames as carriers and hides the secret message by modifying the partition mode of two adjacent PUs. Due to the ability of polygon encoding to represent more information within a small range, it increases capacity with low steganographic distortion. Thirdly, we further propose a filter by improving DLSN, which enhances the visual quality of the entire stego video by processing I-frames. Extensive experimental results show that the video steganography algorithm proposed in this paper achieves higher capacity and superior visual quality compared to current State-of-the-Art methods. Meanwhile, our algorithm can also obtain good BRI and anti-steganalysis performance. This method has promising application prospects in the field of video covert communication.

Face forgery detection suffers from cross-dataset generalization challenges, where performance degradation occurs due to distribution shifts between training and testing data. Recently, pseudo-fake face generation strategy has mitigated models overfitting to specific forgery traces. However, detectors based on this strategy exhibit an overreliance on blending boundary artifacts for their classification decisions. This overreliance significantly limits their ability to generalize to more advanced face manipulation algorithms, such as FaceDancer and InSwap, which are designed to produce smooth and natural transitions in the blending boundary region. To address this, we propose MAP-Mamba, a novel Multi-Artifacts Perception Mamba framework for modeling generalizable artifact representations from “Generation” to “Enrichment” to “Strengthening”. First, we design an attribute-level face blending method that generate pseudo-fake faces containing fine-grained artifacts via three attribute generators. These pseudo-fakes mimic subtle local inconsistencies in advanced forgery algorithms, guiding the MAP-Mamba to learn diverse forgery features beyond the blending boundary artifacts. Second, considering the variability of face artifacts distribution caused by different forgery algorithms, an artifact style mixing strategy is designed to enrich the artifact style distribution in the training phase by mixing and reorganizing the artifact style features, and to enhance the model’s ability to handle unknown forgery methods. Finally, an adaptive artifact guidance mechanism is proposed to dynamically amplify the artifact-related feature to further strengthen the model’s sensitivity to key artifacts. Extensive experiments on several benchmarks show that MAP-Mamba achieves superior robustness and generalization performance.

The advent of deep learning has accelerated the development of facial manipulation techniques, particularly face-swapping and face attribute editing, raising serious concerns about privacy and identity-related misuse. Existing proactive defense methods predominantly target attribute editing and often generalize poorly to face-swapping models, making it difficult to provide effective protection across both tasks within a unified framework. To bridge this gap, we propose a generalized defense framework, Epoch-Adaptive Adversarial Perturbation Optimization (EA-APO). Specifically, EA-APO introduces a proactive defense mechanism that establishes optimal adversarial paths by optimizing perturbations on a white-box surrogate model to enhance adversarial transferability, and applies the resulting perturbations to source face images to disrupt both face swapping and face attribute editing, even against previously unseen target models in black-box settings. This approach mitigates identity feature tampering while adapting to changes in visual attributes and preserving high-quality adversarial examples. Experimental results show the generalization of our method across multiple face-swapping and attribute-editing models, including commercial ones, while also maintaining strong defense under various common post-processing operations and real-world social media transmission conditions, underscoring its potential for real-world deployment.

The rapid advancement of Generative Adversarial Networks (GANs) and diffusion models has enabled the creation of highly realistic synthetic images, presenting significant societal risks, such as misinformation and deception. As a result, detecting AI-generated images has emerged as a critical challenge. Existing research emphasizes extracting fine-grained features to enhance detector generalization, yet they often lack consideration for the importance and interdependencies of internal elements within local regions and are limited to a single frequency domain, hindering the capture of general forgery traces. To overcome the aforementioned limitations, we first utilize a sliding window to restrict the attention mechanism to a local window, and reconstruct the features within the window to model the relationships between neighboring internal elements within the local region. Then, we design a dual frequency domain branch framework consisting of four frequency domain subbands of DWT and the phase part of FFT to enrich the extraction of local forgery features from different perspectives. Through feature enrichment of dual frequency domain branches and fine-grained feature extraction of reconstruction sliding window attention, our method achieves superior generalization detection capabilities on both GAN and diffusion model-based generative images. Evaluated on diverse datasets comprising images from 65 distinct generative models, our approach achieves a 2.13% improvement in detection accuracy over state-of-the-art methods.

Voice-enabledmobile applications(apps) are exploding in popularity as they could be manipulated with voice commands to achieve convenient man-machine interaction. These voice-enabled apps also raise security and privacy concerns about whether they would maliciously invoke microphones to realize voice eavesdropping. To explore this issue, in this work, we design baleful apps to access the microphone covertly, the results of test studies demonstrate that covert eavesdropping attacks can bypass existing device detection schemes as well as are unnoticeable to human users. To prevent the covert voice eavesdropping attack, we propose a versatilemicrophone icon detection(MicID) scheme inspired by the groundtruth that authorization of the voice function requires the user to touch the specific microphone icon in most of voice-based apps. Specifically, we devise a deep learning model,lightweight YOLO(L-YOLO), to locate the microphone icon on the screen quickly and accurately. By determining whether the located microphone icon is touched by the user, we can judge whether the current microphone access belongs to the app's normal operation or illegal eavesdropping. Finally, we conduct extensive experiments by deploying the scheme on real devices and collecting dataset. The evaluation results show that the proposed MicID scheme achieves more than 99% accuracy with low computation cost.

As image data outsourcing to mobile cloud grows, data privacy has become a major concern. Privacy-preserving image retrieval aims to search over encrypted data without requiring decryption. However, existing schemes face three critical challenges: struggle to balance accuracy, efficiency, and security; limited scalability for large-scale image retrieval in multi-user settings; and vulnerability to security threats arising from user permission changes or key compromises. In this paper, we propose ELSEIR, a novel framework for accurate, efficient, and privacy-preserving image retrieval. ELSEIR leverages a deep hashing model to extract image feature vectors and designs an irreversible random hash code generation module that combines secure permutation keys with two differential privacy methods for privacy protection. To enable accurate searches in multi-user settings, ELSEIR introduces a key conversion protocol that allows the cloud to unify ciphertexts encrypted under different user keys via corresponding switch keys. Furthermore, we extend the framework to ABE-ELSEIR, which supports immediate and efficient user revocation. We further provide formal security proofs demonstrating that the proposed frameworks are resilient against known-plaintext and key collusion attacks. Extensive experiments on real-world datasets show that our scheme achieves accuracy comparable to the unprotected baseline, while surpassing existing approaches in both retrieval accuracy and efficiency.

To ensure the authenticity and validity of Secret Image Sharing (SIS) schemes, Verifiable Secret Image Sharing (VSIS) methods have been proposed. However, existing VSIS schemes are vulnerable to attacks from Dishonest Participant (DP). The dishonest participants may still accessk-1 valid shares when they are identified, enabling them to recover the secret image. To address this issue, a VSIS based on polynomial interpolation for resisting Dishonest Participant Attacks (VSIS-RDPA) is proposed. Unlike traditional SIS schemes, where secret pixels are used as polynomial coefficients, our scheme treats secret pixel values as function values of the polynomial. On the contrary, the secret key and secret pixel values serve as inputs to reconstruct ak-2-degree polynomial using Lagrange interpolation, enhancing security against malicious participants. Authentication parameters generated by a hash function are combined with thek-2-degree polynomial to form a completek-1-degree polynomial, which subsequently is utilized to generate shares. On the receiver end, thek-1-degree polynomial is first reconstructed, and the authentication parameters generated by the hash function are compared with those obtained from the reconstructed polynomial. If the two sets of values match, the authentication is successful, allowing for the recovery of the secret image. In addition, a modified authentication phase with ECC is also proposed to enhance the robustness of authentication. Experimental results and analysis demonstrate that the proposed schemes can resist DP attacks and ensure efficiency, verifiability, and security.

Crowdsourcing has emerged as a vital paradigm for task execution and data collection, with task matching as a core application. crowdsourcing platforms can leverage the spatial keyword similarity to identify whether a worker’s interests and location align with a task requester’s requirements. However, untrusted crowdsourcing platforms pose significant privacy risks to both task requesters and workers. To mitigate these risks, participants typically encrypt their data before outsourcing. In this paper, we propose a privacy-preserving Spatial Keyword Similarity-based Task Matching (SKSTM) scheme that enables secure task matching. In SKSTM, we encode both locations and keywords using Geohash and bitmap representations, respectively, transforming secure task matching into inner product operations in the ciphertext domain via Enhanced Asymmetric Scalar Product-Preserving Encryption (EASPE). Security analysis and experimental results demonstrate that SKSTM preserves participants’ privacy while outperforming state-of-the-art schemes in task matching efficiency.

Image steganalysis is a detection task to distinguish whether a secret message is embedded in a digital image. Due to the domain inconsistency caused by Cover Source Mismatch(CSM) and Steganographic Algorithm Mismatch (SAM), most of them suffer from significant performance degradation. Recent mismatched steganalysis focused on extracting domain invariant features by domain adversarial training or feature alignment. However these schemes are limited to unstable performance in diverse domain mismatch scenarios, and are even ineffective in some cases. In this paper, we propose a Universal Mismatched Steganalysis PCD-UMS via pair-wise confidence difference-based pseudo-label selection from the perspective of optimizing target training data. Specifically, we reveal a strong positive correlation commonality between pair-wise confidence difference and the detection performance of steganalysis among various mismatch scenarios. Based on this, a novel pseudo-label selection strategy consisting of maximum confidence difference first (MCDF) rule and pair-wise label differential storage (PLDS) rule is designed to select and filter the reliable target pseudo-labels. Furthermore, a multi-perspective pair-wise feature alignment loss is designed to initially transfer the classification ability of source steganalysis, thus solving the problem that source steganalysis fails completely under some domain mismatch scenarios. Comprehensive experiments show that our PCD-UMS outperforms the existing mismatched steganalysis by 12.07% and 3.40% in terms of detection performance under CSM and SAM scenarios.

Proactive defense against face forgery seeks to disrupt the output of forgery models by embedding imperceptible adversarial perturbations into face images to be protected. However, existing methods predominantly focus on deepfakes, often neglecting traditional image manipulations. It limits their practical applicability, as attackers may resort to traditional manipulations when deepfake attempts fail. To bridge this gap, a Dual-Forgery Proactive Defense (DFPD) method is proposed for combating both deepfakes and traditional image manipulations. For deepfake resistance, the DFPD designs a gradient-based ensemble adversarial attack that effectively disrupts outputs from multiple deepfake models. To defeat traditional manipulations, it also designs a fragile watermarking algorithm based on Invertible Neural Network (INN), enabling accurate localization of tampered regions. Furthermore, to mitigate the mutual interference between perturbation injection and watermark embedding, on the one hand, the DFPD adopts a serial pipeline starting with watermark embedding and then perturbation injection, which ensures that the injected perturbations are not displaced into residual image during INN-based embedding. On the other hand, a morphological post-processing module is introduced to eliminate adversarial noise in the tampering localization results. Extensive experiments validate the effectiveness of DFPD, demonstrating a 20.25% improvement in deepfake disruption over the best baseline in terms of PSNR and a 9.67% increase in traditional tampering localization in terms of ACC, while preserving high perceptual quality (32.75 dB PSNR).

In recent years, adversarial attacks on video recognition models have attracted increasing attention. However, most existing strategies are extensions of image-based methods, where adversarial perturbations are computed independently and embedded into individual frames. This independent per-frame perturbation process wastes computational resources and leads to excessive query consumption. To address this problem, we introduce Frequency Domain Distributed Perturbations (FDP), a straightforward yet effective black-box video attack method using temporal correlations between video frames. Specifically, FDP first converts the input video into the frequency domain and calculates globally coordinated adversarial perturbations in the spectral space. By conducting global optimization in the frequency domain, FDP improves the effectiveness of each query, significantly decreasing the total number of queries needed. The resulting perturbations are temporally distributed across frames to preserve the spatiotemporal structure. Furthermore, we introduce a frequency-sensitive mask to identify the spectral regions most critical to the model's predictions. By applying perturbations only to these key frequency bands, FDP further reduces the perturbation search space and improves query efficiency. Extensive experiments demonstrate that our method significantly reduces query consumption while achieving higher attack success rates than state-of-the-art approaches.

The rapid advancement of generative models, such as GANs and Diffusion models, has enabled the creation of highly realistic synthetic images, raising serious concerns about misinformation, deepfakes, and copyright infringement. Although numerous Artificial Intelligence Generated Image (AIGI) detectors have been proposed, often reporting high accuracy, their effectiveness in real-world scenarios remains questionable. To bridge this gap, we introduce AIGIBench, a comprehensive benchmark designed to rigorously evaluate the robustness and generalization capabilities of state-of-the-art AIGI detectors. AIGIBench simulates real-world challenges through four core tasks: multi-source generalization, robustness to image degradation, sensitivity to data augmentation, and impact of test-time pre-processing. It includes 23 diverse fake image subsets that span both advanced and widely adopted image generation techniques, along with real-world samples collected from social media and AI art platforms. Extensive experiments on 11 advanced detectors demonstrate that, despite their high reported accuracy in controlled settings, these detectors suffer significant performance drops on real-world data, limited benefits from common augmentations, and nuanced effects of pre-processing, highlighting the need for more robust detection strategies. By providing a unified and realistic evaluation framework, AIGIBench offers valuable insights to guide future research toward dependable and generalizable AIGI detection.

Dynamic searchable symmetric encryption (DSSE) schemes support keyword search queries on encrypted dynamic datasets with add-or-delete operations stored on an untrusted remote server. Multi-user DSSE (MUDSSE) further considers multiple users to access the encrypted dataset. Most works of MUDSSE focus on how to promise forward and backward privacy of queries. However, the problem in which the data owner can not revocate the deleted encrypted data on the encrypted dataset adaptively and efficiently is not sufficiently considered. To solve this problem, we propose a new multi-user DSSE scheme named X-MUSE, extending cryptographic primitives Symmetric Revocable Encryption (SRE) to design a new searchable encryption with optimal search time for the deletion operation. Furthermore, to minimize communication size and counter new integrity threats raised by malicious clients, we extend X-MUSE to design a new MUDSSE named B-MUSE by integrating blockchain-based technology. Our evaluation confirms that our schemes have practical search performance and lower storage costs on both the server and the client side compared to the state-of-the-art.

Cost-based image steganography can significantly enhance its performance through a Reinforcement Learning (RL) framework. However, existing methods still face limitations in the generation of reward signals and the capture of image texture details. To address these challenges, this paper proposes a Dual-Branch Texture Enhancement Reinforcement Learning framework (DBT-RL) for symmetric embedding cost learning. This framework incorporates a Texture Information Enhancement Module (TIEM), enabling the policy network to more effectively focus on complex textured regions. Additionally, DBT-RL integrates multiple steganalysis to construct an ensemble environment network and introduces a novel adaptive update strategy. This strategy dynamically selects the best-performing steganalyzer to provide rewards to the policy network while self-updating weaker steganalyzers, ensuring that the policy network receives precise and dynamically balanced feedback. A large number of experimental results show that DBT-RL achieves high performance in the security of symmetric-cost embedding steganography.

Recent advances in steganography leverage generative adversarial networks (GANs) as a robust framework for securing covert communications through adversarial training between stego-generators and steganalytic discriminators. This paradigm facilitates the synthesis of secure steganographic images by harnessing the competition between network components. However, existing GAN-based approaches suffer from asymmetric capacity between generators and discriminators: suboptimally trained discriminators provide inadequate gradient guidance for generator optimization, causing premature convergence and security degradation. To overcome this critical limitation, we propose an enhanced multi-steganalyzer adversarial architecture incorporating maximum mean discrepancy (MMD) regularization. Our framework introduces two key innovations: 1) an MMD-based regularization mechanism mitigating distributional discrepancies among multiple steganalyzers through kernel embedding optimization, and 2) a reward function with fusing gradients derived from multiple steganalyzers to boost reinforcement learning-based adversarial training. This dual strategy enables the discriminator to learn generalized forensic features while maintaining equilibrium in adversarial training dynamics, ultimately allowing the generator to produce stego images resistant to multiple steganalyzers simultaneously. Comprehensive experiments validate our method’s superiority: When evaluated across five steganalysis networks, including YedNet, CovNet, LWENet, SRNet, and SwT-SN, at 0.1-0.4 bpp payloads, the proposed framework achieves improvements in average detection error rates over state-of-the-art techniques such as SPAR-RL and GMAN. Ablation studies further confirm that MMD regularization contributes significantly to security enhancement.

Artificial Intelligence Generated Content (AIGC) has created a fertile ground for image steganography. Existing Coverless Image Steganography (CIS) methods rely on image semantics to encode secrets, transmitting stego images without embedding, inherently resisting steganalysis. However, constructing CIS Datasets (CISDs) for these methods demands excessive resources, making them impractical for communication. Moreover, achieving low cost and high security is unattainable under these conditions. Therefore, we propose a CIS method based on semantic-controlled text-to-image generation. Our method disguises users as typical AIGC community members utilizing mainstream black-box text-to-image generation with Stable Diffusion (SD). During pre-processing, plain prompts, derived from dialogues with a large language model, are divided into coded and uncoded prompts through our encryption process, where a secret key determines coded prompts. In communication, confusion prompts are selected from uncoded and coded prompts, excluding those determined by secrets. Subsequently, our stego shuffling process combines topic, secret, and confusion prompts to produce stego prompt sets. Diverse stego images maintaining visual topic consistency are generated from these sets using SD with generation seeds indicating transmission order. By introducing confusion prompts, our method is secure from recognition when revealing stego prompts. Experimental results demonstrate our method achieves low communication costs and enhances communication security.

Few-shot image generation aims to train generative models using a small number of training images. When there are few images available for training (e.g. 10 images), Learning From Scratch (LFS) methods often generate images that closely resemble the training data while Transfer Learning (TL) methods try to improve performance by leveraging prior knowledge from GANs pre-trained on large-scale datasets. However, current TL methods may not allow for sufficient control over the degree of knowledge preservation from the source model, making them unsuitable for setups where the source and target domains are not closely related. To address this, we propose a novel pipeline called Peer is your Pillar (PIP), which combines a target few-shot dataset with a peer dataset to create a data-unbalanced conditional generation. Our approach includes a class embedding method that separates the class space from the latent space, and we use a direction loss based on pre-trained CLIP to improve image diversity. Experiments on various few-shot datasets demonstrate the advancement of the proposed PIP, especially reduces the training requirements of few-shot image generation.

The (k,n)-threshold Secret Image Sharing (SIS) is a naturally fault-tolerant technique for image privacy protection. A secret image is processed through secret sharing to generatenshadow images, which are then distributed tondifferent recipients. During the recovery phase, the complete secret image can be reconstructed by anykout ofnshadow images. Although (k,n)-threshold SIS itself allows for the loss of up to$n-k$shadow images, if there are pixel errors in the remainingkshadow images, the recovery of the secret image will be declared a failure. Therefore, Robust Secret Image Sharing (RSIS) has been proposed to address the issue. However, the current proposed RSIS schemes only demonstrated limited robustness against noise attacks. This paper presents a novelk-consistency-based RSIS scheme to resist malicious attacks, including noise, JPEG compression, tampering, and cropping. In the sharing phase, a dual-SIS mechanism is first designed to perform two rounds of secret sharing on the secret image. In the recovery phase, high-quality secret image can be reconstructed based onk-consistency after attacking. The experimental results demonstrated that our scheme not only provides comprehensive robustness but also allows for flexible adjustment of shadow images’ sizes, ensuring both security and efficiency during image sharing.

The purpose of deep image hiding is to embed the secret image imperceptibly in an equally sized cover image, and then recover the secret image almost perfectly at the receiver end. How to improve the quality of recovered secret images while ensuring the visual quality and security of stego images is an important challenge. In order to address this issue, a novel deep image hiding framework called EFCA-DIH (Edge Features and Coordinate Attention-based Invertible Network for Deep Image Hiding) is proposed. Firstly, an important feature extraction module is proposed to extract wavelet sub-band features coupled with edge features, thereby hiding the secret image better in the cover image. Secondly, a coordinate attention mechanism is introduced into the invertible hidden module to embed the secret information in the complex texture regions. Finally, an edge feature loss function is designed to constrain the edge differences between the stego image and the cover image, and between the secret image and the recovered secret image, thereby improving the quality of both the stego image and the recovered secret image. Experimental results have demonstrated that our EFCA-DIH significantly improves the quality of recovered secret images compared with other state-of-the-art methods, while maintaining the visual quality and security of stego images.

Image steganalysis has always been an important topic in the field of information security, and researchers have designed many excellent steganalysis models. However, the existing steganalysis models tend to construct a single path and increase the convolution kernels to reduce the size of feature maps, which is not comprehensive enough to extract the features and may boost the number of parameters. In addition, the single residual block stacking may pay attention to protecting stego signals and neglect the mining of hidden features. To address these issues, we propose a steganalysis model based on dual-path enhancement and fractal downsampling, which is suitable for both spatial and JPEG domains. The model reuses and strengthens noise residuals through two dual-path enhancement blocks, and designs a fractal downsampling block for downsampling at multiple levels, angles, and composition structures. The experimental results demonstrate that the proposed model achieves the best detection performance in both spatial and JPEG domains compared with other start-of-the-art methods. Besides, we design a series of ablation experiments to verify the rationality of each component.

Voice assistants have been widely adopted for their ability to provide non-touch human-computer interaction. However, while they offer convenience, their continuous listening for specific wake-up words raises privacy concerns, as it may lead to eavesdropping on user conversations. To investigate this issue, we devised covert eavesdropping attacks by perturbing and replaying events generated during the user’s normal activation of the voice assistant. The results demonstrate the feasibility and harmfulness of such eavesdropping attacks. To counter these covert voice eavesdropping attacks, we propose an effective defense scheme called CrossUnwind. This scheme leverages the groundtruth that voice assistant wake-up requires hardware to generate and send wake-up events. Specifically, we designed a novel tombstone file parsing process and an accurate event discrimination algorithm to obtain detailed call station information of the wake-up event without compromising the system. This allows us to determine whether the current wake-up event was generated by hardware. We deployed CrossUnwind on real devices and compared it to well-known machine learning and deep learning methods. The results demonstrate that CrossUnwind can achieve high accuracy in eavesdropping detection with faster speeds and lower resource utilization.

Computer vision (CV) applications empower various Internet of Things (IoT) scenarios. However, their advancements in image generation and manipulation tools make it increasingly easy to produce highly deceptive forged images, escalating the risk of image forgery. Cryptography-based methods can secure images but cannot support direct CV applications with compromised visual legibility. Existing generative adversarial network (GAN)-based steganography methods can effectively facilitate CV applications and image forgery prevention with high indistinguishability between stego and cover images. However, they are inefficient in resource-constrained IoT scenarios. Therefore, we propose a lightweight image forgery prevention scheme for IoT using GAN-based steganography. Our scheme embeds identity data within images. If forged, it fails to recover, triggering alerts. Our scheme can significantly improve efficiency with a lightweight generator designed by incorporating blueprint separable convolutions, sum connections and discrete wavelet transform while ensuring high effectiveness. Real-world IoT experimental results demonstrate this.

Non-convertible tokens (NFTs) have become a fundamental part of the metaverse ecosystem due to its uniqueness and immutability. However, existing copyright protection schemes of NFT image art relied on the NFTs itself minted by third-party platforms. A minted NFT image art only tracks and verifies the entire transaction process, but the legitimacy of the source and ownership of its mapped digital image art cannot be determined. The original author or authorized publisher lack an active defense mechanism to prove ownership of the digital image art mapped by the unauthorized NFT. Therefore, we propose a self-defense copyright protection scheme for NFT image art based on information embedding in this article, called SDCP-IE. The original author or authorized publisher can embed the copyright information into the published digital image art without damaging its visual effect in advance. Different from the existing information embedding works, the proposed SDCP-IE can generally enhance the invisibility of copyright information with different embedding capacity. Furthermore, considering the scenario of copyright information being discovered or even destroyed by unauthorized parties, the designed SDCP-IE can efficiently generate enhanced digital image art to improve the security performance of embedded image, thus resisting the detection of multiple known and unknown detection models simultaneously. The experimental results have also shown that the PSNR values of enhanced embedded image are all over 57db on three datasets BOSSBase, BOWS2, and ALASKA#2. Moreover, compared with existing information embedding works, the enhanced embedded images generated by SDCP-IE reaches the best transferability performance on the advanced CNN-based detection models. When the target detector is the pre-trained SRNet at 0.4 bpp, the test error rate of SDCP-IE at 0.4 bpp on the evaluated detection model YeNet reaches 53.38%, which is 4.92%, 28.62%, and 7.05% higher than that of the UTGAN, SPS-ENH, and Xie-Model, respectively.

Presentation Attack Detection Model (PADM) plays a crucial role in biometric authentication, particularly in fingerprint Liveness Detection Model (FLDM). However, FLDM is susceptible to various unauthorized usage and dissemination threats, highlighting the urgent necessity to protect its Intellectual Property (IP). While previous backdoor watermarking techniques have been effective in authenticating the IP of FLDM, they unfortunately pose a risk to its performance. Thus, this paper presents a novel model watermarking strategy named CLGuard, leveraging robust fingerprint attributes to create authentic label watermarks, facilitating the verification of ownership for FLDM. Notably, these watermarks seamlessly integrate into both the input and output layers, minimizing any irreversible modifications to the feature space of FLDM, ensuring optimal performance. Initially, a selection of High Complexity Inputs (HCI) is chosen based on robust fingerprint attributes, and watermark data is embedded into the HCI set using imperceptible backdoor. Simultaneously, a coding network is deployed to conceal the watermark data within the background layer of the samples, mitigating any adverse impacts on the model's deep feature space. Subsequently, a mapping function is established between triggers and original labels, enabling the fine-tuning of the model with embedded watermarks on a diverse mixed dataset. Ultimately, IP is authenticated by leveraging trigger sets and original misidentification sets. Comprehensive experiments on multiple benchmark datasets demonstrate that CLGuard effectively authenticates suspicious PADM IP without introducing anomalous input-output pairs, thus preserving the original task performance. Furthermore, it withstands attacks such as fine-tuning, neuronal cleanse, and watermark removal, showcasing reliability, stealthiness, fidelity, and robustness.

Blockchain technology has garnered increasing attention from researchers. Because blockchain systems may contain malicious or spatially limited nodes that may delay block verification and reduce block transmission rate, this work proposes a block transmission model by designing and using special clusters. This work proposes the cluster formation and selection mechanisms. Nodes are grouped into clusters in a blockchain, and clusters with high fitness values are chosen to transmit blocks by calculating their trust values and block transmission rates. The proposed method is compared with the peers: Layer-Chain, BlockP2P-EP and RNS. According to experimental findings, the proposed method is superior to its peers regarding the time needed for block synchronization and transmission, block occupation storage ratio, transaction throughput, and block transmission success ratio.

Adversarial embedding for image steganography is a novel technology to effectively enhance the steganographic security of the traditional steganographic algorithms. However, the existing schemes still have room for further improvement in the design of optimization strategy and the steganographic post-processing of optimization failure. In this paper, we design the progressive probability optimizing strategy (PPO). It dynamically selects more efficient gradients to guide the optimization of the probability optimization in a progressive manner. Moreover, we propose a discarded stego recycling mechanism (DSR) to re-select the stego from the discarded stego set that have failed to deceive the target steganalyzer after the optimzation fails. In such way, the statistical distribution of the stego can still further approximate the cover, thus further improving the steganographic security on re-trained steganalyzers in adversary-aware scenario. Comprehensive experiments show that compared with the existing advanced schemes, the proposed method boosts the security improvement against both the re-trained hand-crafted feature-based and deep leanring-based steganalysis models.

Linguistic steganography based on text generation is a hot topic in the field of text information hiding. Previous studies have managed to improve the syntactic quality of steganography texts using natural language processing techniques based on deep learning, but their steganography models still lack the ability to control the semantic and contextual characteristics in texts, which is caused by the shortage of relevant information they can obtain. This results in a great decline in the imperceptibility of steganographic texts. To address the problem, we propose a context-aware linguistic steganography method based on neural machine translation called NMT-Stega. The model generates translation containing secret messages based on the neural machine translation model with semantic fusion and language model reference units. In this way, the semantics and contexts of translation are controlled by the additional semantic and contextual features acquired from the text to be translated. Also, a new encoding that combines arithmetic coding with a waiting mechanism is proposed in our model. This method solves the low embedding capacity problem of waiting mechanism while ensuring the semantic and contextual characteristics of steganographic text are less modified. Experimental results show that our model outperforms the previous models and encoding methods in semantic correlation, embedding capacity and imperceptibility.

Recently, the asymmetric cost-based steganographic method using generative adversarial networks has achieved significant success. This highlights the substantial potential of deep learning-based asymmetric cost generation methods over traditional methods reliant on cost enhancement. However, the current frameworks for asymmetric cost learning ignore the correlation between positive and negative embedding costs, resulting in an imbalance asymmetric embedding costs. This can cause scattered modified pixels or even anomalous modified pixels in the stego image, thereby reducing steganographic security. In this paper, we propose a novel asymmetric steganographic cost learning framework, termed Steganographic embedding Cost Generation and Modulation (SCGM), to ensure a balance between asymmetric embedding costs by maintaining the correlation and therefore improve steganographic security. In our framework, we initially train a policy network to produce symmetric costs and subsequently use an adaptive modulation module we designed to achieve asymmetry. The modulation module facilitates the adaptive transformation of learned symmetric costs into asymmetric costs by autonomously learning modulation proportions during adversarial training with steganalysis. Moreover, we develop distinct adversarial loss functions for both the symmetric cost generation and the asymmetric cost modulation phases to further enhance steganographic security. Extensive experimental results have demonstrated that SCGM attains state-of-the-art performance in steganographic security, with an average error rate across steganalyzers that exceeds the existing best asymmetric cost-based steganography method by 2.77%.

Deep image hiding is a challenging image processing task that aims to hide a secret image into a cover image of equal size perfectly. How to improve the imperceptibility of deep image hiding while ensuring high computational efficiency is a primary challenge. Where imperceptibility means not being visually perceived while not being perceived by the steganalysis model. In this paper, we propose a novel deep image hiding framework called DIH-OAIN (Deep Image Hiding based on One-way Adversarial Invertible Networks) to address it. Firstly, an image cascade framework is introduced to extract image semantics and details with dual-resolution branches, and reduces computation complexity by balancing image resolution and model complexity. Secondly, a hidden probability guided module is designed to constrain the secret image to be hidden in the texture region, utilizing the image texture complexity as prior knowledge. The above two points can effectively improve visual imperceptibility. Finally, a one-way adversarial training strategy is proposed to enhance the model imperceptibility. A series of experimental results show that the proposed method is significantly improved in imperceptibility comparing to state-of-the-art deep image hiding algorithms, while maintaining a low computation complexity.

Adversarial example-based steganographic methods that utilize the gradients of target steganalyzer to update symmetric costs are emerging. The existing adversarial adjustment strategies for costs still have limited improvements in steganographic security. The existing gradient selection scheme, which sets a fixed gradient selection ratio for all images, is not delicate enough. To address the above problems, this paper proposes an iterative two-stage probability adjustment strategy with a progressive incremental searching mechanism (ITPA-PIS) to further improve the security of updated asymmetric distortions. Unlike previous works that adopted the cost as the adjustment object, we explore a new adjustment object, i.e., probability, and then design an iterative two-stage probability adjustment strategy (ITPA) to obtain a more secure asymmetric distortion, thereby improving the anti-detection performance of the traditional symmetric distortion algorithms against deep learning-based steganalyzers. In addition, we specifically design a progressive incremental searching mechanism (PIS) to select partially efficient gradients to guide the probability adjustment. Unlike existing gradient selection schemes that manually set a fixed selection ratio, PIS adopts a progressive searching method to dynamically determine the gradient selection ratio suitable for each image, thereby enhancing the overall performance of the proposed ITPA again. The experimental results show that our proposed ITPA-PIS achieves outstanding security performance on the CNN-based steganalysis models XuNet, YedroujNet, SRNet, and EfficientNet and hand-crafted feature-based steganalysis models SRM and MaxSRMd2 under the adversary unawareness and adversary awareness scenarios.

Invertible secret image sharing with authentication (ISISA) distributes comprehensible stego images generated from secret images and cover images to involved participants. The secret image and cover image can be correctly recovered after authentication. However, existing ISISA schemes suffer from issues such as a single kind of image, limited embedding capacity, poor visual quality and a lack of authentication capability. To address these issues, this paper provides a novel invertible secret image sharing scheme with authentication for embedding color palette images into true color images. In this scheme, the pixels of the palette secret image and the bits of the cover pixel are used as coefficients of the polynomial. Share is embedded into a true color cover image to generate an intermediate stego image. Authentication information is then derived from the intermediate stego image and hidden in the cover image. The final stego images that resemble the cover image are obtained and sent to authorized participants. At the receiver end, once k stego images are verified, the secret image and cover image can be losslessly recovered for a (k, n)-threshold scheme. The experimental results and theoretical analysis demonstrated the superiority and practicality of the scheme.

In spatial crowdsourcing, location-based task recommendation schemes are widely used to match appropriate workers in desired geographic areas with relevant tasks from data requesters. To ensure data confidentiality, various privacy-preserving location-based task recommendation schemes have been proposed, as cloud servers behave semi-honestly. However, existing schemes reveal access patterns, and the dimension of the geographic query increases significantly when additional information beyond locations is used to filter appropriate workers. To address the above challenges, this paper proposes two efficient and privacy-preserving location-based task recommendation (EPTR) schemes that support high-dimensional queries and access pattern privacy protection. First, we propose a basic EPTR scheme (EPTR-I) that utilizes randomizable matrix multiplication and public position intersection test (PPIT) to achieve linear search complexity and full access pattern privacy protection. Then, we explore the trade-off between efficiency and security and develop a tree-based EPTR scheme (EPTR-II) to achieve sub-linear search complexity. Security analysis demonstrates that both schemes protect the confidentiality of worker locations, requester queries, and query results and achieve different security properties on access pattern assurance. Extensive performance evaluation shows that both EPTR schemes are efficient in terms of computational cost, with EPTR-II being$10^{3}\times$faster than the state-of-the-art scheme in task recommendation.

Poisoning efficiency is crucial in poisoning-based backdoor attacks, as attackers aim to minimize the number of poisoning samples while maximizing attack efficacy. Recent studies have sought to enhance poisoning efficiency by selecting effective samples. However, these studies typically rely on a proxy backdoor injection task to identify an efficient set of poisoning samples. This proxy attack-based approach can lead to performance degradation if the proxy attack settings differ from those of the actual victims, due to the shortcut nature of backdoor learning. Furthermore, proxy attack-based methods are extremely time-consuming, as they require numerous complete backdoor injection processes for sample selection. To address these concerns, we present a Proxy attack-Free Strategy (PFS) designed to identify efficient poisoning samples based on the similarity between clean samples and their corresponding poisoning samples, as well as the diversity of the poisoning set. The proposed PFS is motivated by the observation that selecting samples with high similarity between clean and corresponding poisoning samples results in significantly higher attack success rates compared to using samples with low similarity. Additionally, we provide theoretical foundations to explain the proposed PFS. We comprehensively evaluate the proposed strategy across various datasets, triggers, poisoning rates, architectures, and training hyperparameters. Our experimental results demonstrate that PFS enhances backdoor attack efficiency while also offering a remarkable speed advantage over previous proxy attack-based selection methodologies.

Numerous mobile devices are equipped with voice assistants to facilitate contactless user-device interaction. However, the widespread availability of voice assistants also raises security and privacy concerns, as they can be maliciously triggered to perform voice eavesdropping. Although diverse attacks have been taken to manipulate voice assistants for eavesdropping, they exhibit deficiencies of limited attack scopes and conspicuous attack behaviors because they target specific voice assistants or require extra voice commands to activate them. To manipulate arbitrary voice assistants for covert eavesdropping attack, we conduct a comprehensive analysis of voice assistant implementation in the Android system and refine a universal workflow. Through meticulous analysis and experimental verification, we uncover an inherent vulnerability that in voice assistants across device types that can be awakened by an artificial faking Intent. Building on this significant discovery, we propose an attack termed VoiceEar. It leverages a malicious event generation file and a first-in-first-out Intent generation algorithm to trigger voice assistants within the normal workflow for eavesdropping, without voice commands. Finally, we deploy the VoiceEar attacks on 25 mainstream mobile devices, and invite 95 volunteers for eavesdropping activity perception testing. The results unequivocally demonstrate the seamless execution of VoiceEar attacks, with neither users nor devices awareness.

In addition to relying on super computing power and professional domain knowledge, training a high-precision deepfake fingerprint detection model (DFDM) to authenticate the fingerprints also requires the support of massive private fingerprint data. To sum up, the DFDM should be deemed as intellectual property (IP) of the trainers, so it is crucial to protect IP. Currently, most watermarking-based IP protection schemes are implemented by introducing additional tasks, such as constructing trigger sets, fine-tuning model weights, etc., which severely impair the performance of the original task and increase the training cost. Inspired by the feature knowledge learned by the model, this letter proposes an IP protection (IPP) scheme for DFDM by verifying whether the suspect model contains the fingerprint ridge features learned by the victim model from another perspective based on the verifier. Firstly, the local binary pattern (LBP) is used to enhance the ridge texture on the fingerprint samples, so that DFDM can better learn the ridge features. Then, a DFDM lacking texture augmentation is employed as the adversarial model for training the meta-verifier without any alteration to the model parameters. Finally, the trained meta-verifier is used to determine whether the suspected model contains the ridge features in the victim model. The public fingerprint dataset (LivDet2017) was leveraged in the DFDM training process to validate our approach. Experimental results show that the proposed scheme can verify the IP of DFDM and is robust to some common attacks.

Recently, generative steganography that transforms secret information to a generated image has been a promising technique to resist steganalysis detection. However, due to the inefficiency and irreversibility of the secret-to-image transformation, it is hard to find a good trade-off between the information hiding capacity and extraction accuracy. To address this issue, we propose a secret-to-image reversible transformation (S2IRT) scheme for generative steganography. The proposed S2IRT scheme is based on a generative model, i.e., Glow model, which enables a bijective-mapping between latent space with multivariate Gaussian distribution and image space with a complex distribution. In the process of S2I transformation, guided by a given secret message, we construct a latent vector and then map it to a generated image by the Glow model, so that the secret message is finally transformed to the generated image. Owing to good efficiency and reversibility of S2IRT scheme, the proposed steganographic approach achieves both high hiding capacity and accurate extraction of secret message from generated image. Furthermore, a separate encoding-based S2IRT (SE-S2IRT) scheme is also proposed to improve the robustness to common image attacks. The experiments demonstrate the proposed steganographic approaches can achieve high hiding capacity (up to 4bpp) and accurate information extraction (almost 100% accuracy rate) simultaneously, while maintaining desirable anti-detectability and imperceptibility.

Radio frequency identification technology has been widely used to verify the presence of items in many applications such as warehouse management and supply chain logistics. In these applications, the challenge of how to timely identify the missing tags (namely tag searching or missing tag identification) is a key focus. Existing missing tag identification solutions have not achieved their full potentials because collision slots have not been well explored. In this paper, we propose an approach named collision resolving based missing tag identification (CR-MTI) to break through the performance bottleneck of existing missing tag identification protocols. In CR-MTI, multiple tags are allowed to respond with different binary strings in a collision slot. Then, the reader can verify them together by using the bit tracking technology and particularly designed string, thereby significantly improve the time efficiency. CR-MTI also reduces the number of messages transmitted by the reader using customized coding. We further explore the optimal parameter settings to maximize the performance of our proposed CR-MTI. Extensive simulation results show that our proposed CR-MTI outperforms prior art in terms of time efficiency, total executive time and communication complexity.

How to obtain the information from massive tags is a key focus of RFID applications. The occurrence of collisions leads to problems such as reduced identification efficiency in RFID networks. To tackle such challenges, most tag collision arbitration protocols focus on scheduling tag identification with collision avoidance. However, how to effectively identify tags in collisions to improve identification efficiency has not been well explored. In this paper, we propose a group query allocation method to divide the string space into mutually disjoint subsets which contains several strings. Each string can be viewed as a full ID or partial ID of a tag. When multiple string from a subset are sent simultaneously, the reader can identify all of them in a time slot. Based on the group query allocation method, a segment detection based characteristic group query tree (SD-CGQT) protocol is presented for fast tag identification by significantly reducing the collision slots and transmitted bits. Numerous experimental results verify the superiority of the proposed SD-CGQT, compared to prior arts in system efficiency, total identification time, communication complexity and energy consumption.

Linguistic steganalysis is an important topic in the field of information security and signal processing. In recent years, linguistic steganalysis have mainly utilized deep learning techniques and make great success. But suffer from the following major disadvantages. From the perspective of model structure, current methods only extract coarse features of the text, without focusing on the fine-grained representations. In terms of application, most of the studies only focus on single hidden scene and ignore the more realistic mixed hidden scenes which are more complex and realistic. These weaknesses limit the performance and the application of linguistic steganalysis in reality. In this paper, we propose a novel linguistic steganalysis method to overcome these weaknesses. This proposed method can extract distinguished text representation which fuses hierarchical features and perform excellently in sophisticated conditions. Firstly, we adapt gated graph neural networks as the coarse graph updater to update node representations on the graph level. Then we design a fine graph updater composed of the graph attention mechanism to focus on the highlighted nodes on the node-level. Moreover, we extract the most notable feature on the dimension-level of node by the graph channel attention module. Finally, the readout function is designed to fuse the hierarchical features and make the classification. The experimental results show that our method achieves the best results compared with the previous methods in both single hidden scene and mixed hidden scenes, which prove the effectiveness of the proposed method.

Vision systems applied to drones, automatic vehicles, and robots have become an increasingly popular sensing method. However, vision sensors that make up these systems are vulnerable to malicious input attacks, which can lead to serious consequences. Privious work on attacking cameras of automatic vehicles shows that lasers can cause failure of camera-based functionalities, but it lacks analysis of the results and does not conduct experiments in the actual scenarios. In this article, a laser-based attack on cameras and binocular vision sensors of drones is presented. First, we propose a threat model that describes how an adversary attacks the drone then perform feasibility analysis of the attack from theory and practice. Next, we design multi-variable experiments in the lab to systematically study the effectiveness of the attack, and further analyze how each variable affects the results. To get intuitive and fine-grained results, multidimensional image similarity is used to measure the effects. In particular, experiments in the actual scenarios are carried out, and results show that the attack can make obstacle avoidance, target recognition and tracking completely failed. Finally, lightweight countermeasures based on hardware and software are proposed to improve sensor resilience against the attack.

Summary Graph data processing has been widely applied in a variety of domains such as industry, science, social network, and so on. It therefore has stimulated many efforts devoted to this area. To embrace the fast development trend of big graph data, graph data processing based on Pregel‐like systems has been regarded as one of the most promising ways and has widely attracted the attention of researchers. However, it still remains in its early stage and there still exist many challenges. In Pregel, the superstep synchronization is time consuming as the graph data iteration operation requires multiple synchronizations. Furthermore, the graph data partition strategy adopted by Pregel fails to support load balancing, therefore causing the increase of network I/O overhead as the scale of graph data grows. To address these issues, this paper presents an efficient computational framework for graph data processing based on the bulk synchronous parallel model. The global synchronization control mechanism is improved by determining the start time of the next round of superstep through counting the number of global message files. Furthermore, an improved graph data partition mechanism based on a balanced hash method is proposed to reduce the communication overhead between different partitions of sub‐graph computational tasks. We also re‐design the PageRank algorithm to verify the effectiveness of the proposed framework. Experimental results on different real‐world datasets verify the efficiency of our proposed framework as it outperforms Giraph (an open source Pregel‐like system) by 58%−69%, and achieves 10×−17× performance improvement over Hadoop.

As cloud computing becomes prevalent, more and more data owners are likely to outsource their data to a cloud server. However, to ensure privacy, the data should be encrypted before outsourcing. Symmetric searchable encryption allows users to retrieve keyword over encrypted data without decrypting the data. Many existing schemes that are based on symmetric searchable encryption only support single keyword search, conjunctive keywords search, multiple keywords search, or single phrase search. However, some schemes, i.e., static schemes, only search one phrase in a query request. In this paper, we propose a multi-phrase ranked search over encrypted cloud data, which also supports dynamic update operations, such as adding or deleting files. We used an inverted index to record the locations of keywords and to judge whether the phrase appears. This index can search for keywords efficiently. In order to rank the results and protect the privacy of relevance score, the relevance score evaluation model is used in searching process on client-side. Also, the special construction of the index makes the scheme dynamic. The data owner can update the cloud data at very little cost. Security analyses and extensive experiments were conducted to demonstrate the safety and efficiency of the proposed scheme.

Radio frequency identification (RFID) is widely applied in massive items tagged domains. Existing medium access control (MAC) solutions primarily focus on improving slot efficiency or reducing the total number of slots. However, with pervasive applications of RFID, the time and energy consumption are increasingly important and should be considered in the new design. In this paper, we re-exam the problem of tag identification in UHF RFID system from the perspective of time and energy consumption. The presented work comprehensively reviews and analyzes the prior tag reading protocols. Based on prior art, we further discuss a novel design of tag reading algorithm to improve both time and energy efficiency of EPC C1 Gen2 UHF RFID standard. By exploring the effectiveness of embedding slot-by-slot mechanism in a sub-frame observation phase and combine the sub-frame and slot-by-slot observation in the proposed algorithm, which can achieve more fine-grained frame size adjustment with time and energy-efficiency. Moreover, the cardinality estimation function of the algorithm is implemented by the look-up tables, which allows dramatically reduction in computational complexity and energy consumption. Both simulation results and experiments show clear performance improvement over the commercial solutions.

Recently, handwriting recognition approaches has been widely applied to Human-Computer Interface (HCI) applications. The emergence of the novel mobile terminals urges a more man-machine friendly interface mode. The previous air-writing recognition approaches have been accomplished by virtue of cameras and sensors. However, the vision based approaches are susceptible to the light condition and sensor based methods have disadvantages in deployment and highcost. The latest researches have demonstrated that the pervasive wireless signals can be used to identify different gestures. In this paper, we attempt to utilize channel state information (CSI) derived from wireless signals to realize the device-free air-write recognition called Wi-Fi. Compared to the gesture recognition, the increased diversity and complexity of characters of the alphabet make it challenging. The Principle Component Analysis (PCA) is used for denoising effectively and the energy indicator derived from the Fast Fourier Transform (FFT) is to detect action continuously. The unique CSI waveform caused by unique writing patterns of 26 letters serve as feature space. Finally, the Hidden Markov model (HMM) is used for character modeling and classification. We conduct experiments in our laboratory and get the average accuracy of the Wi-Fi are 86.75 and 88.74 percent in two writing areas, respectively.

Currently, searchable encryption is a hot topic in the field of cloud computing. The existing achievements are mainly focused on keyword-based search schemes, and almost all of them depend on predefined keywords extracted in the phases of index construction and query. However, keyword-based search schemes ignore the semantic representation information of users' retrieval and cannot completely match users' search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, for the first time, we define and solve the problems of semantic search based on conceptual graphs (CGs) over encrypted outsourced data in clouding computing (SSCG). We first employ the efficient measure of “sentence scoring” in text summarization and Tregex to extract the most important and simplified topic sentences from documents. We then convert these simplified sentences into CGs. To perform quantitative calculation of CGs, we design a new method that can map CGs to vectors. Next, we rank the returned results based on “text summarization score”. Furthermore, we propose a basic idea for SSCG and give a significantly improved scheme to satisfy the security guarantee of searchable symmetric encryption (SSE). Finally, we choose a real-world dataset, i.e., the CNN dataset to test our scheme. The results obtained from the experiment show the effectiveness of our proposed scheme.

Currently, searchable encryption becomes the focus topic with the emerging cloud computing paradigm. The existing research schemes are mainly semantic extensions of multiple keywords. However, the semantic information carried by the keywords is limited and does not respond well to the content of the document. And when the original scheme constructs the conceptual graph, it ignores the context information of the topic sentence, which leads to errors in the semantic extension. In this paper, we define and construct semantic search encryption scheme for context-based conceptual graph (ESSEC). We make contextual contact with the central key attributes in the topic sentence and extend its semantic information, so as to improve the accuracy of the retrieval and semantic relevance. Finally, experiments based on real data show that the scheme is effective and feasible.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets to cloud. To preserve privacy, the datasets are usually encrypted before outsourcing. However, the common practice of encryption makes the effective utilization of the data difficult. For example, it is difficult to search the given keywords in encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users' retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we propose ECSED, a novel semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. ECSED uses two cloud servers. One is used to store the outsourced datasets and return the ranked results to data users. The other one is used to compute the similarity scores between the documents and the query and send the scores to the first server. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. We employ the multi-keyword ranked search over encrypted cloud data as our basic frame to propose two secure schemes. The experiment results based on the real world datasets show that the scheme is more efficient than previous schemes. We also prove that our schemes are secure under the known ciphertext model and the known background model.

With the rapid development of the network and the informatization of society, how to improve the accuracy of information is an urgent problem to be solved. The existing method is to use an intelligent robot to carry sensors to collect data and transmit the data to the server in real time. Many intelligent robots have emerged in life; the UAV (unmanned aerial vehicle) is one of them. With the popularization of UAV applications, the security of UAV has also been exposed. In addition to some human factors, there is a major factor in the UAV’s endurance. UAVs will face a problem of short battery life when performing flying missions. In order to solve this problem, the existing method is to plan the path of UAV flight. In order to find the optimal path for a UAV flight, we propose three cost functions: path security cost, length cost, and smoothness cost. The path security cost is used to determine whether the path is feasible; the length cost and smoothness cost of the path directly affect the cost of the energy consumption of the UAV flight. We proposed a heuristic evolutionary algorithm that designed several evolutionary operations: substitution operations, crossover operations, mutation operations, length operations, and smoothness operations. Through these operations to enhance our build path effect. Under the analysis of experimental results, we proved that our solution is feasible.

Summary Both mobile computing and cloud computing have experienced rapid development in recent years. Although centralized cloud computing exhibits abundant resources for computation‐intensive tasks, the unpredictable and unstable communication latency between the mobile users and the cloud makes it challenging to handle latency‐sensitive mobile computing tasks. To address this issue, fog computing recently was proposed by pushing the cloud computing to the network edge closer to the users. To realize such vision, we can augment existing access points in wireless networks with cloudlet servers for hosting various mobile computing tasks. In this paper, we investigate how to deploy the servers in a cost‐effective manner without violating the predetermined quality of service. In particular, we practically consider that the available cloudlet servers are heterogeneous, ie, with different cost and resource capacities. The problem is formulated into an integer linear programming form, and a low‐complexity heuristic algorithm is invented to address it. Extensive simulation studies validate the efficiency of our algorithm by it performs much close to the optimal solution.

Cloud download service, as a new application which downloads the requested content offline and reserves it in cloud storage until users retrieve it, has recently become a trend attracting millions of users in China. In the face of the dilemma between the growth of download requests and the limitation of storage resource, the cloud servers have to design an efficient resource allocation scheme to enhance the utilization of storage as well as to satisfy users' needs like a short download time. When a user's churn behavior is considered as a Markov chain process, it is found that a proper allocation of download speed can optimize the storage resource utilization. Accordingly, two dynamic resource allocation schemes including a speed switching (SS) scheme and a speed increasing (SI) scheme are proposed. Both theoretical analysis and simulation results prove that our schemes can effectively reduce the consumption of storage resource and keep the download time short enough for a good user experience.

Searchable encryption is an important research area in cloud computing. However, most existing efficient and reliable ciphertext search schemes are based on keywords or shallow semantic parsing, which are not smart enough to meet with users' search intention. Therefore, in this paper, we propose a content-aware search scheme, which can make semantic search more smart. First, we introduce conceptual graphs (CGs) as a knowledge representation tool. Then, we present our two schemes (PRSCG and PRSCG-TF) based on CGs according to different scenarios. In order to conduct numerical calculation, we transfer original CGs into their linear form with some modification and map them to numerical vectors. Second, we employ the technology of multi-keyword ranked search over encrypted cloud data as the basis against two threat models and raise PRSCG and PRSCG-TF to resolve the problem of privacy-preserving smart semantic search based on CGs. Finally, we choose a real-world data set: CNN data set to test our scheme. We also analyze the privacy and efficiency of proposed schemes in detail. The experiment results show that our proposed schemes are efficient.

In practice, search keywords have quite different importance when users take search operations. In addition, such keywords may have a certain grammatical relationship among them, which reflect the importance of keywords from the user's perspective intuitively. However, the existing search techniques regard the search keywords as independent and unrelated. In this paper, for the first time, we take the relation among query keywords into consideration and design a keyword weighting algorithm to show the importance of the distinction among them. By introducing the keyword weight to the search protocol design, the search results will be more in line with the user's demand. On top of this, we further design a novel central keyword semantic extension ranked scheme. By extending the central query keyword instead of all keywords, our scheme makes a good tradeoff between the search functionality and efficiency. To better express the relevance between queries and files, we further introduce the TF-IDF rule when building trapdoors and the index. In particular, our scheme supports both data set and keywords updates by using the sub-matrix technique. Our work first gives a basic idea for the design of the central keyword semantic extension ranked scheme, and then presents two secure searchable encryption schemes to meet different privacy requirements under two different threat models. Experiments on the real-world data set show that our proposed schemes are efficient, effective, and secure.

With the increasing adoption of cloud computing, a growing number of users outsource their datasets into cloud. The datasets usually are encrypted before outsourcing to preserve the privacy. However, the common practice of encryption makes the effective utilization difficult, for example, search the given keywords in the encrypted datasets. Many schemes are proposed to make encrypted data searchable based on keywords. However, keyword-based search schemes ignore the semantic representation information of users retrieval, and cannot completely meet with users search intention. Therefore, how to design a content-based search scheme and make semantic search more effective and context-aware is a difficult challenge. In this paper, we proposed an innovative semantic search scheme based on the concept hierarchy and the semantic relationship between concepts in the encrypted datasets. More specifically, our scheme first indexes the documents and builds trapdoor based on the concept hierarchy. To further improve the search efficiency, we utilize a tree-based index structure to organize all the document index vectors. Our experiment results based on the real world datasets show the scheme is more efficient than previous scheme. We also study the threat model of our approach and prove it does not introduce any security risk.

Abstract Human genome project is a grand scale scientific work, which aims at measuring three billion base pairs in human chromosomes (haploid). It brings a great challenging task to store and utilize these gene sequences (GS) securely and effectively. With the development of the cloud computing, the storage of gene information can be out of consideration. However, their secure utilization still puzzles data owners and data users. One popular way is to encrypt these GS and construct searchable indexes for secure retrieval. In this paper, we first define and solve the problem of privacy‐preserving outsourced gene data search in encryption domain. We transfer GS into numerical vectors by reasonable mapping for ease of similarity calculation. We employ secure KNN algorithm to encrypt the query, index, and gene data and compute relevance scores securely. We test our scheme through a real‐world dataset: plant GS from National Center of Biotechnology Information. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract A heterogeneous cloud system, for example, a Hadoop 2.6.0 platform, provides distributed but cohesive services with rich features on large‐scale management, reliability, and error tolerance. As big data processing is concerned, newly built cloud clusters meet the challenges of performance optimization focusing on faster task execution and more efficient usage of computing resources. Presently proposed approaches concentrate on temporal improvement, that is, shortening MapReduce time, but seldom focus on storage occupation; however, unbalanced cloud storage strategies could exhaust those nodes with heavy MapReduce cycles and further challenge the security and stability of the entire cluster. In this paper, an adaptive method is presented aiming at spatial–temporal efficiency in a heterogeneous cloud environment. A prediction model based on an optimized Kernel‐based Extreme Learning Machine algorithm is proposed for faster forecast of job execution duration and space occupation, which consequently facilitates the process of task scheduling through a multi‐objective algorithm called time and space optimized NSGA‐II (TS‐NSGA‐II). Experiment results have shown that compared with the original load‐balancing scheme, our approach can save approximate 47–55 s averagely on each task execution. Simultaneously, 1.254‰ of differences on hard disk occupation were made among all scheduled reducers, which achieves 26.6%improvement over the original scheme. Copyright © 2016 John Wiley & Sons, Ltd.

Keyword-based search over encrypted outsourced data has become an important tool in the current cloud computing scenario. The majority of the existing techniques are focusing on multi-keyword exact match or single keyword fuzzy search. However, those existing techniques find less practical significance in real-world applications compared with the multi-keyword fuzzy search technique over encrypted data. The first attempt to construct such a multi-keyword fuzzy search scheme was reported by Wang et al., who used locality-sensitive hashing functions and Bloom filtering to meet the goal of multi-keyword fuzzy search. Nevertheless, Wang's scheme was only effective for a one letter mistake in keyword but was not effective for other common spelling mistakes. Moreover, Wang's scheme was vulnerable to server out-of-order problems during the ranking process and did not consider the keyword weight. In this paper, based on Wang et al.'s scheme, we propose an efficient multi-keyword fuzzy ranked search scheme based on Wang et al.'s scheme that is able to address the aforementioned problems. First, we develop a new method of keyword transformation based on the uni-gram, which will simultaneously improve the accuracy and creates the ability to handle other spelling mistakes. In addition, keywords with the same root can be queried using the stemming algorithm. Furthermore, we consider the keyword weight when selecting an adequate matching file set. Experiments using real-world data show that our scheme is practically efficient and achieve high accuracy.

In cloud computing, searchable encryption scheme over outsourced data is a hot research field. However, most existing works on encrypted search over outsourced cloud data follow the model of “one size fits all” and ignore personalized search intention. Moreover, most of them support only exact keyword search, which greatly affects data usability and user experience. So how to design a searchable encryption scheme that supports personalized search and improves user search experience remains a very challenging task. In this paper, for the first time, we study and solve the problem of personalized multi-keyword ranked search over encrypted data (PRSE) while preserving privacy in cloud computing. With the help of semantic ontology WordNet, we build a user interest model for individual user by analyzing the user's search history, and adopt a scoring mechanism to express user interest smartly. To address the limitations of the model of “one size fit all” and keyword exact search, we propose two PRSE schemes for different search intentions. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective.

ABSTRACT To prevent digital image from unauthorized use, image copy detection is an important technique in the field of copyright protection. The conventional methods of image copy detection concentrate on extracting global or local signatures to resist various kinds of copy attacks. However, the global signatures are sensitive to some geometric transformations, such as rotation and cropping, while the local signatures are not discriminative enough to identify copies from similar images. Considering both the robustness and discriminability, a novel image signature based on the combination of global and local signatures is proposed for image copy detection. Firstly, the interest points are detected from a given image by using the Hessian–Affine detector. Secondly, the image is divided into some circle tracks, and thus the interest points are distributed into these tracks. Finally, to combine the advantages of the circle‐track‐based global signature and the interest points, the global distribution characteristics of interest points based on circle tracks are used to generate our image signature. Experimental results demonstrate the effectiveness of our proposed method in the aspects of both robustness and discriminability. Copyright © 2013 John Wiley & Sons, Ltd.

Cloud computing becomes increasingly popular. To protect data privacy, sensitive data should be encrypted by the data owner before outsourcing, which makes the traditional and efficient plaintext keyword search technique useless. The existing searchable encryption schemes support only exact or fuzzy keyword search, not support semantics-based multi-keyword ranked search. In the real search scenario, it is quite common that cloud customers' searching input might be the synonyms of the predefined keywords, not the exact or fuzzy matching keywords due to the possible synonym substitution (reproduction of information content) and/or her lack of exact knowledge about the data. Therefore, synonym-based multi-keyword ranked search over encrypted cloud data remains a very challenging problem. In this paper, for the first time, we propose an effective approach to solve the problem of synonym-based multi-keyword ranked search over encrypted cloud data. We make contributions mainly in two aspects: synonym-based search for supporting synonym query and multi-keyword ranked search for achieving more accurate search result. Two secure schemes are proposed to meet privacy requirements in two threat models of known ciphertext model and known background model. In enhanced scheme, the sensitive frequency information can be well protected by introducing some dummy keywords, which is not adopted in basic scheme. We give security analysis to justify the correctness and privacy-preserving guarantee of the proposed schemes. Extensive experiments on real-world dataset validate our analysis and show that our proposed solution is very efficient and effective in supporting synonym-based searching.

ABSTRACT A new steganographic method for data hiding in Microsoft Word 2007–2010 (Microsoft Corp., Redmond, WA, USA) files that use Office Open XML (OOXML) format is proposed. Secret information can be imperceptibly embedded into OOXML documents by splitting up the printable text, which is defined by the main document body of the OOXML format document. The number of printable words contained in each segment represents the secret message. Theoretical analysis demonstrates that embedding bit rate of the proposed method can take the maximum value (0.8) when 2 bits of secret message are embedded into each segment. Experiments show that 0.44 bit is embedded into each word and 1/151 bit is embedded into each bit of the document on average, which is higher than contemporary linguistic steganography approaches. The method can resist “Format”, “Impersonation”, “Save As”, “Copy”, and other active attacks, and all these changes will not be shown on the MS Office screen display. Therefore, the proposed method can apply to the fields of covert communication and security protection for OOXML format documents. Copyright © 2011 John Wiley & Sons, Ltd.