ABSTRACT This paper presents an efficient GAN‐based adversarial attack method to spoof deepfake detectors. While such detectors, built on deep neural networks, show high accuracy in identifying forgeries, they are vulnerable to adversarial perturbations. Our proposed model employs a two‐branch architecture: one branch generates general, image‐independent perturbations, while the other enhances the adversarial efficacy of the reconstructed images. Through joint training, we generate visually realistic outputs that severely degrade detector performance. Experiments on FaceForensics++ demonstrate the effectiveness of the proposed method. It achieves competitive performance by reducing detection accuracy below 16% while maintaining high visual fidelity. The resulting highly imperceptible adversarial samples highlight a significant vulnerability in existing detectors.

This article is concerned with the co-design of privacy-preserving and resilient consensus protocol for a class of multiagent networks (MANs), where the information exchanges over communication networks among the agents suffer from eavesdropping and Sybil attacks. First, we introduce a new attack model in which an adversarial agent could launch a Sybil attack, generating a large number of spurious entities in the network, thereby gaining disproportionate influence. In this communication framework, a whole-process privacy-preserving mechanism is designed that is capable of protecting both initial and current states of agents. Then, instead of existing methods requiring identifying and mitigating Sybil nodes, a degree-based mean-subsequence-reduced (D-MSR) resilient strategy is implemented, showcasing its significant properties: 1) ensuring the effectiveness of aforementioned designed privacy protection strategy; 2) allowing the network to contain Sybil nodes without elimination; and 3) reaching consensus among the normal agents. Finally, several numerical simulations are provided to validate the effectiveness of the proposed results.

The proliferation of facial manipulation has been propelled by generative adversarial networks (GAN), severely threatening to the personal privacy and reputation. Accordingly, one such countermeasure is adversarial watermark, which is embedded into the protected image prior to GAN synthesization attack, resulting into the distorted fake content obtained by malicious attackers. However, in practice, JPEG compression usually causes a remarkable degradation on the performance of adversarial watermark. To address this challengeable issue, this letter presents a novel robust adversarial watermark, which can effectively defend against GAN synthesization attack, even though suffering from JPEG compression. Extensive experiments verify the superiority of our proposed method in the benchmark dataset; more importantly, the robustness of the proposed adversarial watermark is comprehensively evaluated on the both simulated transmission channel and the realism social network platform.

This article investigates the secure and privacy-preserving consensus problem of multiagent systems (MASs) with directed interaction topologies under multiple cyberattacks, which contain deception attacks and DoS attacks. First, a unified attack model is introduced to characterize such a multiple attack phenomenon. Besides, considering the existence of eavesdroppers who can intercept the data transmitted on the links, a fully distributed agent value reconstruction method based on the idea of state decomposition is designed to prevent the leakage of the agent's initial information. Then, a novel privacy-preserving adaptive resilient consensus algorithm (PPARCA) with certain graph robustness condition for MASs under the multiple cyberattacks is proposed. The algorithm adaptively takes different countermeasures in the face of different cyberattacks. PPARCA uses the reconstructed agents' states and combines with the modified secure acceptance and broadcast algorithm (SABA). Theoretical analysis shows that the proposed algorithm can effectively protect the privacy of the initial state of the agents, and reach resilient consensus in the face of cyberattacks. Finally, numerical simulations and Raspberry Pi MASs practical application experiments demonstrate the effectiveness of the proposed results.

Regulating illegal activities in cyberspace to balance user privacy and cyberspace governance has been a non-trivial challenge when designing anonymous authentication solutions. For example, while several existing anonymous authentication protocols support accountability, they either risk leaking users' private keys or incur significant overhead for accountability in each ongoing authentication, including in cloud service-based authentication schemes. Seeking to address these limitations, this paper proposes an auditable anonymous user authentication (A2UA) protocol based on blockchain for cloud services. The A2UA protocol mainly employs bilinear pairing, partial authentication factors, dynamic credits and fake-public keys (FPKs) to achieve anonymous mutual authentication between users and cloud service providers, and applies ring signature and blockchain to accomplish two-level accountability while maintaining user privacy. Our analysis results show that the A2UA protocol outperforms several other existing schemes in terms of security, computation and communication costs as well as security and privacy features. Additionally, it has good feasibility in terms of the Ethereum Gas cost as demonstrated in our evaluation.

With the development of facial recognition technology, face anti-spoofing as the most important security module of face recognition system becomes more and more important. As a matter of fact, face anti-spoofing is still a challenging task, especially facing various attacks simultaneously. Moreover, most of current detectors mainly focus on binary classification while usually fail to complete the task of fine-grained multiple classification, referring to as replay, print, partial mask, and full mask attacks. To fill the gap, in this context, it is proposed to design the fine-grained detection network for classifying various face spoofing attack modes. First, we propose to establish a Transformer style network structure for feature extraction, where the convolution mapping operation is adopted instead of traditional linear mapping. Specifically, we adopt the self-attention module for extracting long distance feature, and convolution mapping is used to maintain the model's ability to extract local features. Finally, the simple yet effective linear classifier is introduced for fine-grained classification. Moreover, with the help of the VGG based style-transfer network, the well-designed scheme of data augmentation module is proposed for solving the problem of insufficient training samples. In the large-scale experiments, compared with the baseline detectors, our proposed fine-grained classifier with low computation cost performs its superiority for multiple classification.

With the continuous development of computer hardware equipment and deep learning technology, it is easier for people to swap faces in videos by currently-emerging multimedia tampering tools, such as the most popular deepfake. It would bring a series of new threats of security. Although many forensic researches have focused on this new type of manipulation and achieved high detection accuracy, most of which are based on supervised learning mechanism with requiring a large number of labeled samples for training. In this paper, we first develop a novel unsupervised detection manner for identifying deepfake videos. The main fundamental behind our proposed method is that the face region in the real video is taken by the camera while its counterpart in the deepfake video is usually generated by the computer; the provenance of two videos is totally different. Specifically, our method includes two clustering stages based on Photo-Response Non-Uniformity (PRNU) and noiseprint feature. Firstly, the PRNU fingerprint of each video frame is extracted, which is used to cluster the full-size identical source video (regardless of its real or fake). Secondly, we extract the noiseprint from the face region of the video, which is used to identify (re-cluster for the task of binary classification) the deepfake sample in each cluster. Numerical experiments verify our proposed unsupervised method performs very well on our own dataset and the benchmark FF++ dataset. More importantly, its performance rivals that of the supervised-based state-of-the-art detectors.

Most existing face authentication systems have limitations when facing the challenge raised by presentation attacks, which probably leads to some dangerous activities when using facial unlocking for smart device, facial access to control system, and face scan payment. Accordingly, as a security guarantee to prevent the face authentication from being attacked, the study of face presentation attack detection is developed in this community. In this work, a face presentation attack detector is designed based on residual color texture representation (RCTR). Existing methods lack of effective data preprocessing, and we propose to adopt DW-filter for obtaining residual image, which can effectively improve the detection efficiency. Subsequently, powerful CM texture descriptor is introduced, which performs better than widely used descriptors such as LBP or LPQ. Additionally, representative texture features are extracted from not only RGB space but also more discriminative color spaces such as HSV, YCbCr, and CIE 1976 L∗a∗b (LAB). Meanwhile, the RCTR is fed into the well-designed classifier. Specifically, we compare and analyze the performance of advanced classifiers, among which an ensemble classifier based on a probabilistic voting decision is our optimal choice. Extensive experimental results empirically verify the proposed face presentation attack detector’s superior performance both in the cases of intradataset and interdataset (mismatched training-testing samples) evaluation.

Security of consensus control is of key significance in multi-agent systems. In this paper we investigate the resilient consensus problem for multi-agent systems under the specific attack scenarios where the attacker can eavesdrop on initial information of agents among the system, and modifies the values in the communication links to interfere in the consensus process. To protect the privacy of node' value, we employ the cryptography of homomorphic encryption to encrypt initial integer state of each agent, without revealing to other agents the real value in the network. When the communication network meets the necessary connectivity, we develop a variation of so-called ratio consensus algorithm that deal with malicious attacks. The numerical example of directed network is conducted to illustrate the effectiveness of our proposed algorithm.

Point of interest (POI) recommendation has become an important means to help people discover attractive locations. Previous studies show that modeling the context information of users' check-ins is necessary for POI recommendation. However, most previous hybrid models ignore the fact that a user's interested region is likely to drive him to visit other POIs in it, which means that his region-level interest is indispensable in the recommendation process. In this paper, we take this challenge and propose a hybrid model called POI-Region POI Recommendation (PRPOIR). Specifically, PRPOIR contains two modules: Interest Module and Context Module. In Interest Module, inspired by the success of the Logistic Matrix Factorization (LMF) to model implicit feedback, we apply it to model users' POI-level interest and region-level interest respectively. To capture the difference in the users' emphasis on region-level interests, we innovatively introduce a strategy to establish the dynamic weight of users' region-level interest. Then in Context Module, by further integrating geographical influence and social correlations into PRPOIR, it can alleviate the sparsity of check-ins and bring further performance improvement. To verify the effectiveness of PRPOIR, we conduct experiments on two real-world datasets, experimental results show that PRPOIR outperforms the state-of-the-art models.

Passenger-searching strategies, as the crowd intelligence of massive taxi drivers, are hidden in their historical GPS traces. Mining traces to understand the efficient passenger searching strategies can benefit drivers themselves. Traditionally, the research on passenger search strategies from taxi GPS traces is mainly focused on statistical techniques. Although this can improve the ability of drivers to find potential passengers through hotspots recommendation, most of these research still directly use raw GPS data and failed to take drivers' experience into account. Moreover, because driver's experience is behind of raw data, can't obtain directly, so the traditional model is unable to make good use of it during hot spots mining. In this paper, we proposed an inference model based on HITS (Hypertext Induced Topic Search), which perfectly describes the relationship between hot spots and drivers' experience and thus effectively handle the above problem. We first extract hotspots by an innovative PDBSCAN algorithm based on fuzzy grid partition and match them with corresponding landmarks by a landmark matching algorithm which based on kernel density estimation. Then the HITS-based inference model is used to mine popular hotspots and the most experienced drivers. Finally, we plan an optimal path for drivers. Experimental results demonstrate the efficiency and the ability of this method to provide drivers with better hotspots and hunting sequences recommendation.

The widespread of location-based social networks has generated massive check-in sequences in chronological order. Forecasting check-in sequences is significant while challenging due to the check-ins' sparsity problem. Existing methods have followed closely to incorporate spatial and temporal context to alleviate the data sparsity problem, but neglect the semantic sequential correlation between check-ins. Howbeit, incorporating the semantic sequential correlation between check-ins for next POI recommendation encounters the challenges of semantic sequential correlation measurement and sequential behavior modeling. To measure the semantic sequential correlation, we apply a semantic sequential correlation calculation model based on a semantic correlational graph that incorporates the time intervals' influence to calculate the semantic sequential correlation. Then, we apply a novel Long Short-Term Memory (LSTM) framework equipped with two additional semantic gates that takes the additional semantic sequential correlation as the extra input to capture users' sequential behaviors and model their long short-term interest with the restrictions in the semantic level. Finally, we cluster users into different groups as an improvement of our model to achieve a more accurate recommendation. Our proposed model is evaluated on a real-world and large-scale dataset and the experimental results demonstrate that our method outperforms the state-of-the-art methods for next POI recommendation.

The problem of authenticating a re-sampled image has been investigated over many years. Currently, however, little research proposes a statistical model-based test, resulting in that statistical performance of the resampling detector could not be completely analyzed. To fill the gap, we utilize a parametric model to expose the traces of resampling forgery, which is described with the distribution of residual noise. Afterward, we propose a statistical model describing the residual noise from a resampled image. Then, the detection problem is cast into the framework of hypothesis testing theory. By considering the image content with designing a texture weight map, two types of statistical detectors are established. In an ideal context in which all distribution parameters are perfectly known, the likelihood ratio test (LRT) is presented and its performance is theoretically established. An upper bound of the detection power can be successfully obtained from the statistical performance of an LRT. For practical use, when the distribution parameters are not known, a generalized LRT with three different maps based on estimation of parameters is established. Numerical results on simulated data and real natural images highlight the relevance of our proposed approach.

Business districts serve as basic structures for understanding the organization of real-world economic network. Discovering these business districts in cities establish new types of valuable applications that can benefit end users: Business investors can better identify the proximity of existing business districts and hence, can contribute a better future planning for investing. In this paper, we propose improved affinity propagation clustering for business districts mining. Given check-in data, whose geography information represents business venues' location, we introduce a affinity propagation clustering algorithm(AP), a basic solution, to cluster venues. This strategy requires that real-valued messages are exchanged among business venues until a set of centers and corresponding business districts gradually emerges. However, the computational complexity of AP is affected by the scale of input. And it's not adaptive for random distribution of venues when mining business districts. To conduct business districts mining efficiently, we introduce a pruning method, termed as PAP. And then present merging based mine approach, termed as MAP. We conduct experiments from Yelp data, and experimental results show that our proposed method outperforms the basic solutions and resolves the problem well.

User Identification problem is concerned with identifying the same person with multiple virtual identities across social network sites(SNSs). Most of the existing approaches pays close attention to the similarity of profile attributes, generate-contents and linkages of friends or simply combination of these features. Only one method analyzes the feasibility of user tags in User Identification problems, but does not analyze the particularity and the inconsistency of tags belong to users among different social networks. In this paper, an improved user identification method across social networks via tagging behaviors is proposed that a new symmetric variant of BM25 (BM25 is a bag-of-words retrieval function that ranks a set of documents based on the query terms appearing in each document, regardless of the inter-relationship between the query terms within a document) using the semantic relationships between inconsistent tags among different social networks. By using extracted features from the inconsistent tagging behaviors, profile attributes and SVM supervised learning techniques, a classifier is developed for performing user identity matching between two social network sites. Evaluation on Douban and Weibo real world data-set showed that the accuracy of the proposed method is 30% higher than that of the common tag-based approach.

Most bike-sharing service providers offer a free ride for a short time period. In this paper, we study how to find the optimal route that is free of rental cost and minimizes the trip distance from one location to another within a bike-sharing system, in which the utilization of bike stations dynamically changes over time. We use a time-dependent dynamic graph to model the network of bike stations. In the graph, each vertex represents a bike station and is associated with a vertex-usage function. The difficulty of this problem is mainly attributed to the fluctuation of the usage function because a fully utilized station cannot accept returned bikes. Efficiency is another challenge, as we must explore all possible paths between the source and the destination. To address these challenges, we propose techniques to find a solution optimized for efficiency. First, to reduce the search space, we construct a station network graph on top of a road network. Next, we employ a pathstree to identify all the paths with lengths less than the userpreferred maximum detour distance and we select an optimal path toward the destination. We present the design details of our algorithms and we analyze the algorithms' correctness and complexity. To demonstrate the feasibility of our methods, we also report the results from the extensive experiments we conducted.

The network traffic classification is one of the most fundamental work in the network measurement and management, and this problem is more and more impact as the network scale grows. Many methods are proposed by researchers, but methods based on flow statistics seem more popular than the others. In this paper, we proposed a novel method based on refined flow statistical features. The new statistics, skewness and kurtosis, and new flow statistical features, payload length, were introduced into raw feature set firstly. Then, with the consideration of efficiency in the classification stage, the feature selection was used on the raw feature set to get an optimal feature set and the feature selection are mainly based on the K-means clustering algorithm. The comparison experiment results show that the proposed optimal feature set reaches the same precision level with half time consuming and internal cluster distance when compared with the raw set.