Reviewerly/exHarmony huggingface dataset:
A Decentralized Quality Management Scheme for Content Moderation

Content moderation is a critical issue for media providers to protect viewers from harmful content and also to build a good reputation with the public. Due to the lack of a trustworthy and effective quality management mechanism for content moderation, the heavy workload of content moderation remains difficult for media providers. In this paper, we propose a decentralized management mechanism for content moderation, in which the permissioned blockchain provides credible, transparent and verifiable infrastructure and the smart contracts ensure the automatic execution of pre-set management rules. The proposed mechanism combines a quality evaluation and monetary incentive method based on authenticity historical data, aiming to achieve long-term sustainable quality improvement. Experimental results show that our mechanism can successfully execute the preset management rules automatically, which can simplify the management complexity and avoid human intervention.
Radiofrequency Fingerprint Feature Extraction and Recognition Using a Coordinate Attention-Guided Deep Residual Shrinkage Network

The current radiofrequency fingerprint (RFF) feature extraction and recognition methods using deep learning (DL) lack multiple dimensions for the extracted feature vectors. At the same time, due to the black box property of deep learning, problems such as overfitting often occur in experiments, leading to a decrease in classification effect. In this paper, we proposed a DL method for RFF feature extraction and identification using the coordinate attention-guided (CA) deep residual shrinkage network (DRSN-CA). DRSN-CA integrates the advantages of CA mechanism and DRSN, optimizes the internal attention mechanism for residual shrinkage building unit (RSBU) into a better CA mechanism, and generates a new RSBU-CA module. The module combines the two dimensions of space and channel, which not only reduces the loss of information because of dimensionality reduction, but also enhances the impact of feature extraction for the model under low signal-to-noise ratio. The quantitative results indicated that the effect of DRSN-CA is promising. Compared with the traditional RFF feature extraction and recognition methods, the classification recognition accuracy of 92.5% can be achieved at the maximum distance of 62ft at the lower signal-to-noise ratio of 0dB.
Efficient Fine-Grained Forward Secure Encryption Scheme Based on Attributes in Mobile Healthcare

In mobile health (mHealth), people's personal health records (PHRs) will be captured in real time. It will be managed by the cloud server and shared to authorized users. This will enable people to enjoy more timely medical services. The confidentiality and unauthorized access of PHRs is guaranteed by the attribute set encryption (CP-ABE) with ciphertext policy. However, existing CP-ABE schemes not only face heavy computation cost and storage overhead, but also once the data user's key is leaked, all the data that can be decrypted by that user will also be leaked. It is a huge challenge to ensure the forward security of data. Most of the existing solutions only support coarse-grained data protection and cannot solve the problem of excessive computation cost and storage overhead. In this paper, based on the enhanced Policy-based Puncturable Encryption (P- PUN-ENC) primitive and and-gate access structure,we propose an efficient fine-grained forward secure encryption scheme based on attributes, named EFFS-CP-ABE. In EFFS-CP-ABE, data users are able to protect the corresponding data on the cloud in a policy-based manner accurately and permanently. In addition, to solve the problem of decryption cost in our solution, we combine the decryption outsourcing technique with our scheme. This allows the decryption process of our solution to be performed only 7 times with bilinear mapping. Also the length of the ciphertext of our scheme is constant. Extensive simulation results and performance evaluations demonstrate the safety, effectiveness and practicality of the EFFS-CP-ABE scheme.
GAN-Based Covert Communications Against an Adversary with Uncertain Detection Threshold in Federated Learning Networks

Federated learning (FL) is a promising distributed machine learning paradigm that can address privacy and security issues in the Internet of Things, Beyond 5G and 6G, and so on. Typically, numerous mobile devices in the FL network enable the server to complete model aggregation by uploading trained models obtained from local data. However, due to the broadcast nature of wireless channels, this upload process is vulnerable to adversary's monitoring or potential attacks, and it becomes one of the important risks of model leakage. Physical layer covert communications can protect the existence of covert signals or links, which is one of the effective methods to protect this model uplink. Furthermore, when there is an adversary with an uncertain detection threshold in the network, there is a competitive game between the adversary and mobile devices, which poses a challenge to the transmit power design of covert schemes. Considering the dynamic competition process in covert communications is similar to the zero-sum game process between discriminators and generators in Generative Adversarial Networks (GANs). Therefore, we propose a GAN-based physical layer covert communications against the adversary and protect the uplink in the FL networks. Moreover, the generator imitates the mobile device and the discriminator imitates the adversary. Both are constructed using neural networks, and the learning optimization process is achieved through a zero-sum game between them. Experiment results show that the designed algorithm is convergent and can obtain the optimal covert transmit power and the maximum average communication rate.
A Local Differential Privacy Based Method to Preserve Link Privacy in Mobile Social Network

With MSNs(mobile social networks) playing an increasingly important role in our daily life, individual privacy issues in MSNs are becoming more and more important. To address these issues, differential privacy is widely adopted in MSNs. However, it is a great challenge how to preserve the individual link privacy while ensuring data sharing in a distributed environment. Therefore, a LDP based method is proposed, which is composed of three steps. Firstly, the 2-hop subgraphs of nodes are used to achieve node encoding. Then, the random response completes the edge modification of each node. Finally, after the approximate value of each node edges is obtained, the edge modification technology is employed to generate a synthetic graph that achieves privacy preserving for link privacy of each node. Specially, the properties of random response and the similarity of nodes are utilized to improve data utility. Theoretical analysis proves that the proposed method satisfies differential privacy. The experimental results demonstrate the effectiveness of this method.
B-Pet: The PET Model with Parameter-Efficient Learning

In recent years, under the trend of training models in big data, Few-shot learning (FSL) which aims to learn models to solve problems with a few samples has also achieved good results on many data sets. In fact, acquiring high-quality training samples is expensive in many aspects, but FSL can save the overhead costs. Among FSL models, the PET model combines semi-supervised learning, prompt learning and knowledge distillation based on the pre-training language model. However, in fine-turning the PET model has the disadvantages that consumes a lot of resources and time and requires heavy costs of storage for model preservation. Therefore, this paper proposes the B-pet model, which freezes most of the training parameters and only trains bias parameters during fine-turning process, significantly reducing the storage consumption of the model for downstream tasks. We used six data sets with <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\vert \tau \vert=\mathbf{10},\ \mathbf{50},\ \mathbf{100}$</tex> and three different data training models respectively. The results show that four data sets on the B-pet model performed better than original PET model training. It is obvious that in the memory-constrained environment deployment, multitasking fine-tunes models have practical value. It also proved that most semi-supervised models with fixed parameters are realizable.
Distributed Storage Method for Data Security Sharing and Exchange

the traditional distributed storage method realizes the decentralized storage of data to a certain extent, but there are still some problems, such as excessive waste of storage space, low security, low storage efficiency and so on. In order to solve these problems, this paper designs a high security storage scheme with adaptive selection of storage nodes, anti-loss and theft, and anti-repudiation, among which, on the one hand, it realizes the efficient storage of data and reduces the space occupation rate. On the other hand, it maintains the security, integrity and availability of the data throughout the life cycle of storage.
Real-Time Semantic Segmentation Algorithm Based on Tversky Loss Function and Mixed Pooling

In recent years, semantic segmentation methods based on deep learning have made remarkable developments. Despite achieving high segmentation accuracy, the performance of real-time segmentation methods cannot satisfy real-world applications. In order to achieve a balance between segmentation accuracy and speed, a real-time semantic segmentation algorithm based on Tversky loss function and mixed pooling is proposed in this paper. A Short-Term Dense Concatenate network (STDC network) is used to construct the encoder, and a mixed pooling module is used for the final part of the encoder, which uses strip pooling and average pooling to enhance the feature representation while expanding the receptive field. Additionally, a Tversky-based loss function is used for the detail guidance module of the backbone network in the encoder, and a joint loss function is used to supervise the whole network's training. We achieved 76.9% mIoU at 110.7 FPS on the Cityscapes dataset, a 2.4% improvement in accuracy over the benchmark algorithm STDCSeg, and 72.2% mIoU at 177.6 FPS on the Camvid dataset, satisfied the requirements of the real-time segmentation task.
New Traceable and Revocable Attribute Based Encryption on Lattices

This scheme proposes a new traceable and revocable attribute-based encryption scheme on the lattice (LTR-ABE). First, the attribute-based encryption scheme is more suitable for today's distributed networks and can provide flexible access control. The access control policy used in this paper is the access tree structure, which is applied to the ciphertext policy attribute-based encryption. Second, when a user leaves the system or the key is compromised, the user needs to be revoked from the system. Therefore, the user is tracked to determine whether it is a malicious user and then revoked. More importantly, our proposed revocation mechanism does not need to update the key to achieve revocation. The algorithm is run by an honest and trusted data service manager (DSM) in a cloud server. With the development of quantum computers, the traditional cryptographic mechanism based on bilinear pairs cannot resist quantum attacks. Therefore, our cryptographic scheme is proposed based on lattice theory, specifically the Learning with Error (LWE) problem in lattice, which is resistant to quantum computer attacks. Thus, a new revocation mechanism is implemented in the study of postquantum cryptography.
A Deep Model Intellectual Property Protection Method Supporting Public Verification

Recently, the intellectual property protection methods based on deep learning have achieved great success, but there are still serious infringement issues that the network topology or hyper parameters of the trained model are stolen by third parties. In this paper, we construct a deep learning model based on the autoencoder to remove the bone from the medical images containing chest x-ray, and the specific trigger set is trained and predicted to get the effect of the backdoor watermark. The scheme of zero-knowledge proof is applied to transform the backdoor watermark of the model into the fixed-length string, which is published in the block chain to verify the ownership of the model. Through the non-interactive verification between the model owner and the third party, the ownership of the model can be confirmed by the third party and the verification process will not disclose any information of the model itself. The method proposed in this paper can support infinite times of verification and does not reveal any information about the model, so as to achieve the protection of intellectual property rights of the model.
EDF: Enhanced Deep Fingerprinting attacks on Websites

Website Fingerprinting (WF) attacks enable network surveillants to monitor traffic data and learn features from encrypted packet sequences, thus identifying which website user is visiting, which seriously threatens user privacy on Tor. Recently, zero-delay lightweight defense methods (i.e., FRONT and GLUE) were designed for Tor to efficiently defend many known WF attacks. But, can they defend more unknown intelligent WF attacks? Due to the efficiency of deep learning model, we explore whether there is a more intelligent deep learning model that could defeat FRONT and GLUE. In this work, we propose two deep learning based WF attacks, EDF-S and EDF-P. EDP-S incorporates a global average pooling layer and series model into Deep Fingerprinting (DF), while EDF-P introduces a global average pooling layer and parallel model into DF. We conduct extensive WF attack experiments on several datasets to prove our improvement on effectiveness. EDF-P can achieve over 97% TPR and 96% F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score on a non-defended dataset. We also find that EDF-S and EDF-P perform better than previous attacks against GLUE. Compared with DF, EDF-S can achieve almost 10% enhancement for the attack precision and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score while EDF-P can achieve more than 10% enhancement for the attack precision and F <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> score. Our enhanced WF attacks bring new challenges to the above mentioned WF defenses.
ML-Based Detection Approaches for Covert Communication with Multi-D Signal Features

This work explores the efficacy of multidimensional (Multi-D) signal features in detecting covert communication, specifically considering time-domain features, frequency-time features, frequency offset, and SNR. To this end, we propose two novel machine learning (ML)-based detection schemes utilizing these signal features: a supervised learning approach employing k-nearest neighbors (KNN) and an unsupervised learning approach leveraging density-based spatial clustering of applications with noise (DBSCAN). To assess the performance of these schemes, we construct a covert communication testbed that enables the extraction of signal features in diverse electro-magnetic environments. Our experimental results show that both the KNN-based and DBSCAN-based schemes outperform traditional energy-based detection methods, and that our proposed schemes are more effective in identifying covert communication in complex electromagnetic environments.
A Dimensional Perspective Analysis on the Cybersecurity Risks and Opportunities of ChatGPT-Like Information Systems

As a recent breakthrough in generative artificial intelligence, ChatGPT is capable of creating new data, images, audio, or text content based on user context. In the field of cybersecurity, it provides generative automated AI services such as network detection, malware protection, and privacy compliance monitoring. However, it also faces significant security risks during its design, training, and operation phases, including privacy breaches, content abuse, prompt word attacks, model stealing attacks, abnormal structure attacks, data poisoning attacks, model hijacking attacks, and sponge attacks. This paper starts from the risks and events that ChatGPT has recently faced, proposes a framework for analyzing cybersecurity in cyberspace, and envisions adversarial models and systems. It puts forward a new evolutionary relationship between attackers and defenders using ChatGPT to enhance their own capabilities in a changing environment and predicts the future development of ChatGPT from a security perspective.
A Trusted Privacy-Preserving Model for Cross-Chain Transactions Based on zk_SNARKs

Currently, the privacy protection technology of blockchain is not mature enough, such as user data leakage and lack of anonymity exist, and these problems are especially serious when conducting cross-chain transactions. In this paper, we propose a method of cross-chain transactions based on zk_SNARKs in a trusted environment for cross-chain transactions for privacy protection. Cross-chain transactions are performed through the features of zk_SNARKs such as efficiency, privacy, and verifiability. zk_SNARKs needs to generate trusted settings with the help of a third party during the transaction, and the process has serious privacy issues, and the private parameters generated during the transaction may be maliciously attacked and thus obtained by attackers. To address this problem, we use a hardware technology SGX for trusted computing to encrypt and store some private parameters generated by zk_SNARKs, thus ensuring the security of cross-chain transactions. The experimental results show that the privacy protection scheme using SGX for cross-chain transactions takes less time than the traditional transaction privacy protection, with a verification time of 1.2s for a number of 10 sidechains, and is more efficient than the traditional cross-chain transaction privacy protection.
Cloud Data Deduplication Scheme Based on Blockchain

To address the problem of untrustworthiness among entities faced in the process of data deduplication in cloud storage environment, this paper proposes a cloud data deduplication scheme based on blockchain. Firstly, to ensure the trustworthiness of data ownership proof in the data deduplication process, a data ownership verification algorithm based on random location sampling is designed with the help of blockchain technology and Merkle hash tree; Secondly, an oblivious pseudo-random protocol is used to obtain convergent encryption key to facilitate deduplication by cloud servers provides. Finally, the scheme security is proved by conducting security analysis, while simulation experiments are conducted to verify the effectiveness of the scheme.
Research on Lightweight Human Pose Estimation Model Based on Knowledge Distillation

Most of the existing state-of-the-art human pose estimation methods mainly pursue improving the precision of human pose estimation, ignoring the model's complexity and making it impossible to deploy it on resource-constrained devices. In addition, some other methods overly pursue the model's lightweight, which significantly decreases the model's precision and affects the experience of practical applications. However, in reality, fast and accurate applications are often more popular, so this area of research is of some practical significance. To achieve lightweight deployment and better practical application, A lightweight human pose estimation model based on knowledge distillation is proposed, which firstly uses group convolution and channel shuffle operation to lighten the HRNet model to obtain the S-HRNet model. Secondly, a response-based knowledge distillation algorithm is proposed to use the pre-trained HRNet as the teacher model and S-HRNet as the student model with the help of an offline knowledge distillation strategy, and the prediction results of the teacher model are used to supervise the learning of the student model together with the real labels. Finally, this paper conducts a large number of experiments on the MS COCO dataset proposed by Microsoft, and the experimental results show that the model in this paper has a higher average precision and lower number of parameters and computations compared to the other models mentioned.
Physical-Sensing Inconsistency Vulnerability Mitigation for Multi-UAV Crowdsensing

Multiagent deep reinforcement learning (MADRL)-based methods have been widely used to control the flight of multiple UAVs in high dynamic mobile crowdsensing (MCS). The existing MADRL-based methods assume that the state used for decision-making, such as GPS position, is accurate. However, the integrity of state is fragility to the GPS attacks. We formulate this vulnerability as the Physical-Sensing Inconsistency (PSI). Different from the existing methods that use additional hardware and software to judge the authenticity of GPS signals, we use the technology of robust deep reinforcement learning to overcome the impact of GPS attacks on UAV flight. Specifically, based on the existing MADRL algorithm, i.e., Multi-Agent Deep Deterministic Policy Gradient (MADDPG), we add a regularizer to its policy updating to mitigate the impact of the PSI. Finally, we use theoretical analysis and experiments to verify the security and effectiveness of the proposed method.
Privacy-preserving Federated Learning Against Byzantine Attack via Top-k Indexes

Federated learning as an emerging machine learning paradigm has received much attention due to its security feature of only uploading trained model gradients instead of raw data. However, it has been demonstrated that shared gradients may expose sensitive client data, and malicious clients could upload poisoned model gradients, disrupting the global model's convergence. Although existing privacy protection schemes and malicious client detection schemes for federated learning have been proposed, these detection schemes under privacy protection still have high communication and computational overhead. To address the above issues, we propose TGS-SM, a privacy-preserving, byzantine-robust, and computationally-efficient federated learning framework. Specifically, we identify the malicious client based on the similarity of the index mask generated by the Top-k mechanism. Additionally, we combine the shuffle mechanism and the dual-server framework to ensure privacy protection. Extensive experiments have shown that our scheme can achieve efficient defense while ensuring the accuracy of the global model.
Product Retrieval Based on Subtractive Angular Margin Loss

This paper proposes a new loss function called Subtractive Angular Margin Loss (SAML) for product retrieval in the electronic retail industry. Product retrieval aims to find specific goods from a large classification of items at the Stock-Keeping Unit (SKU) level. SAML improves upon ArcFace loss by changing the decision boundary from an additive angle to a subtractive angle, which prevents over-compressing sample features of each class in angular space and better distinguishes complex patterns of different products. The performance of SAML is compared with other conventional loss functions using ResNet as standard backbone network on the Products-10k dataset, which contains almost 10,000 SKU-level products. Experimental results show that SAML outperforms other loss functions and achieves 1.58% improvement on top-1 accuracy compared to ArcFace.