Fake Messages Research Articles

A Deniable Encryption Method for Modulation-Based DNA Storage.

Recent advancements in synthesis and sequencing techniques have made deoxyribonucleic acid (DNA) a promising alternative for next-generation digital storage. As it approaches practical application, ensuring the security of DNA-stored information has become a critical problem. Deniable encryption allows the decryption of different information from the same ciphertext, ensuring that the "plausible" fake information can be provided when users are coerced to reveal the real information. In this paper, we propose a deniable encryption method that uniquely leverages DNA noise channels. Specifically, true and fake messages are encrypted by two similar modulation carriers and subsequently obfuscated by inherent errors. Experiment results demonstrate that our method not only can conceal true information among fake ones indistinguishably, but also allow both the coercive adversary and the legitimate receiver to decrypt the intended information accurately. Further security analysis validates the resistance of our method against various typical attacks. Compared with conventional DNA cryptography methods based on complex biological operations, our method offers superior practicality and reliability, positioning it as an ideal solution for data encryption in future large-scale DNA storage applications.

An Intelligent Attack Detection Framework for the Internet of Autonomous Vehicles with Imbalanced Car Hacking Data

The modern Internet of Autonomous Vehicles (IoVs) has enabled the development of autonomous vehicles that can interact with each other and their surroundings, facilitating real-time data exchange and communication between vehicles, infrastructure, and the external environment. The lack of security procedures in vehicular networks and Controller Area Network (CAN) protocol leaves vehicles exposed to intrusions. One common attack type is the message injection attack, which inserts fake messages into original Electronic Control Units (ECUs) to trick them or create failures. Therefore, this paper tackles the pressing issue of cyber-attack detection in modern IoV systems, where the increasing connectivity of vehicles to the external world and each other creates a vast attack surface. The vulnerability of in-vehicle networks, particularly the CAN protocol, makes them susceptible to attacks such as message injection, which can have severe consequences. To address this, we propose an intelligent Intrusion detection system (IDS) to detect a wide range of threats utilizing machine learning techniques. However, a significant challenge lies in the inherent imbalance of car-hacking datasets, which can lead to misclassification of attack types. To overcome this, we employ various imbalanced pre-processing techniques, including NearMiss, Random over-sampling (ROS), and TomLinks, to pre-process and handle imbalanced data. Then, various Machine Learning (ML) techniques, including Logistic Regression (LR), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and K-Nearest Neighbors (k-NN), are employed in detecting and predicting attack types on balanced data. We evaluate the performance and efficacy of these techniques using a comprehensive set of evaluation metrics, including accuracy, precision, F1_Score, and recall. This demonstrates how well the suggested IDS detects cyberattacks in external and intra-vehicle vehicular networks using unbalanced data on vehicle hacking. Using k-NN with various resampling techniques, the results show that the proposed system achieves 100% detection rates in testing on the Car-Hacking dataset in comparison with existing work, demonstrating the effectiveness of our approach in protecting modern vehicle systems from advanced threats.

Resource efficient stabilization for local tasks despite unknown capacity links

Self-stabilizing protocols enable distributed systems to recover correct behavior starting from any arbitrary configuration. In particular, when processors communicate by message passing and the communication links are unbounded, fake messages may be placed in communication links by an adversary. When the number of such fake messages is unknown, self-stabilization may require huge resources:•generic solutions (a.k.a. data link protocols) require unbounded resources, which makes them unrealistic to deploy,•specific solutions (e.g., census or tree construction) require O(nlog⁡n) or O(Δlog⁡n) bits of memory per node, where n denotes the network size and Δ its maximum degree, which may prevent scalability.We investigate the possibility of resource-efficient self-stabilizing protocols in this context.Specifically, we present self-stabilizing protocols for (Δ+1)-coloring and maximal independent set construction in any n-node graph, under the asynchronous message-passing model. The problems of (Δ+1)-coloring and maximal independent set construction are widely regarded as benchmark problems for evaluating local algorithms. Our protocols offer many desirable features. They are deterministic, converge in O(kΔn2log⁡n) message exchanges, where k is the (unknown) initial number of (possibly corrupted) messages in a communication link. They use messages of O(log⁡log⁡n+log⁡Δ) bits with a memory of O(Δlog⁡Δ+log⁡log⁡n) bits at each node. The resource consumption of our protocols is thus almost oblivious to the number of nodes, enabling scalability. Moreover, a striking property of our protocols is that the nodes do not need to know the number, or any bound on the number of messages initially present in each communication link of the initial (potentially corrupted) network configuration. This permits our protocols to handle any future network with unknown message capacity communication links.A key building block of our coloring and maximal independent set schemes is an algorithm to obtain an acyclic orientation of graph edges, that is of independent interest, and can serve as a useful tool for solving other tasks in this challenging setting.

Identification of Fake Messages in The Media Based on Their Emotional Coloring

The purpose of this research is to determine the feasibility of identifying fakes based on their emotional tone. The research objective is to develop a methodical approach that allows for the automatic identification of fakes without complex verification procedures using software tools. The method we propose does not involve revolutionary mathematical models but rather compiles existing developments in a specific sequence of procedures, which constitutes its scientific novelty. The advantage of this approach lies in its simplicity and relatively easy application, even for users with basic computer skills. To illustrate and facilitate replication, we provide a research flowchart and a link to the "Orange" program with open-source code. Specifically, the methodology involves calculating emotional probabilities according to Plutchik and classifying messages based on a Random Forest (RF) model, implemented within the mentioned program through widgets.

HP-LSTM: Hawkes Process–LSTM-Based Detection of DDoS Attack for In-Vehicle Network

Connected and autonomous vehicles (CAVs) are advancing at a fast speed with the improvement of the automotive industry, which opens up new possibilities for different attacks. A Distributed Denial-of-Service (DDoS) attacker floods the in-vehicle network with fake messages, resulting in the failure of driving assistance systems and impairment of vehicle control functionalities, seriously disrupting the normal operation of the vehicle. In this paper, we propose a novel DDoS attack detection method for in-vehicle Ethernet Scalable service-Oriented Middleware over IP (SOME/IP), which integrates the Hawkes process with Long Short-Term Memory networks (LSTMs) to capture the dynamic behavioral features of the attacker. Specifically, we employ the Hawkes process to capture features of the DDoS attack, with its parameters reflecting the dynamism and self-exciting properties of the attack events. Subsequently, we propose a novel deep learning network structure, an HP-LSTM block, inspired by the Hawkes process, while employing a residual attention block to enhance the model’s detection efficiency and accuracy. Additionally, due to the scarcity of publicly available datasets for SOME/IP, we employed a mature SOME/IP generator to create a dataset for evaluating the validity of the proposed detection model. Finally, extensive experiments were conducted to demonstrate the effectiveness of the proposed DDoS attack detection method.

Fake News Detection using Machine Learning

Abstract: In recent years, the proliferation of fake news has precipitated numerous social and political quandaries. As the predominant source of information shifts towards digital platforms, discerning accountability for disseminated opinions becomes increasingly challenging, hindering authentication of received information. Given the pervasive nature of ecological and societal dilemmas, the role of machine learning in combating fake messages on social media is paramount. The virality of messages, whether genuine or fabricated, underscores the necessity for an automated, resilient, dependable, and efficient detection mechanism amidst multifarious challenges. This review delves into the contemporary landscape of fake news detection mechanisms within social media. By examining the contextual backdrop of fake news and its ramifications on users, we explore various methodological approaches categorized as content-based, social context-based, and hybrid-based methods. Culminating with an elucidation of four pivotal research challenges, this paper aims to steer future endeavors towards advancing the field.

A Survey on Security Mechanisms in D2D IoT Networks

Due to the availability of handheld, mobile devices and the advent of cloud and bog data applications, the data traffic in the licensed bands has increased. This has led to the exploration of alternative avenues to de-load the conventional wireless networks. A new paradigm called device to device networks has evolved which is a category of mobile ad-hoc networks. Device-to- device (D2D) communication is expected to play a significant role in upcoming networks as it will reduce the burden from the cellular systems. This may make big data applications easier. However the D2D networks don’t use the security provided by cellular networks. Hence there is a chance of attacks. The major attack in D2D devices is the eavesdropping attack in which mobile hosts share the same wireless medium and broadcast signals over airwaves, which can be easily intercepted by receivers tuned to the proper frequency. Thus, the attacker can read exchanged messages and is able to inject fake messages to manipulate other users. This paper investigates the salient approaches of D2D networks in the context of security and access control and existing research methods. Keywords: Internet of Things (IoT), Network Security, D2D Security, Throughput, Error Rate.

Arithmetic optimization based secure intelligent clustering algorithm for Vehicular Adhoc Network.

Vehicular Adhoc Network (VANET) suffers from the loss of perilous data packets and disruption of links due to the fast movement of vehicles and dynamic network topology. Moreover, the reliability of the vehicular network is also threatened by malicious vehicles and messages. The malicious vehicle can promulgate fake messages to the node to misguide it, which may result in the loss of precious lives. In this situation, maintaining efficient, reliable, and secure communication among automobiles is of extreme importance, especially for a densely populated network. One of the remedies is vehicular clustering, which can effectively perform in a high-density network. However, secure cluster formation and cluster optimization are important factors to consider during the clustering process because non-optimal clusters may incur high end-to-end communication delays and produce overhead on the network. In addition, malicious nodes and packets reduce passenger and driver safety, increase road accidents, and waste passenger and driver time. To this end, we employ Arithmetic Optimization Algorithm (AOA) to design a secure intelligent clustering named AOACNET. AOA is used to achieve optimality of vehicular clusters. During cluster formation, the algorithm prevents unauthentic nodes from becoming cluster members by taking into consideration the performance value of each automobile. The vehicle's performance value is based on the record of data transmission. If a vehicle transmits a fake message, it will receive a penalty of (-1), and in the case of transmitting a legitimate message, a reward of (+1) will be assigned to the vehicle. Initially, all the vehicles have equal performance value which either increase or decrease based on communication with their peers. The vehicles will become cluster members only if their performance value is greater than the threshold value (0). AOACNET is tested in MATLAB using various evaluation metrics (i.e., number of clusters, load balancing, computational time, network overhead and delay). The simulation results show that the proposed algorithm performs up to 25% better than the similar contenders in terms of designated optimization objectives.

Fake in the Context of Worldview Determinants of Modern Society

Introduction. The increasing intensification of false meanings in the communicative space, which permanently generates the processes of alienation in society, actualizes the reflection of the phenomenon of fake messages, requiring their socio-philosophical understanding. The purpose of the article is to identify the specifics of the positioning of a fake, on the one hand, and to diagnose the risks associated with the power of the impact of false meanings that stimulate latent tensions and challenges in society, on the other. Materials and Methods. The theoretical and methodological basis was the concept of the post-non-classical social and humanitarian paradigm, which includes the principles of complementarity and pluralism. The study is based on the synergetic method, the theory of communicative action (J. Habermas), as well as the value approach. The theory of simulacra (J. Baudrillard, J. Derrida, M. Foucault, etc.) is methodologically significant for revealing the essence of a fake in the context of virtual space, through the prism of which the fake claims to replace the genuine. Results. Civilizational shifts that have taken place in society are identified and interpreted, which give the impression of a high degree of socio-cultural disorganization, which is reflected in the ideological foundations of social practice. It has been established that fake is a special culture of some new reality, which is actively imposed on the target audience. The formation of a social quality based on a fake involves the use of the latter as an ideological tool for influencing the mass consciousness and controlling people’s behavior. Discussion and Conclusions. The purpose of a fake is to launch an idea into the mass consciousness, inspire the need for certain political events, tune in to certain assessments, arousing strong emotions, and even reasonably push for action. It is concluded that the communicative space is oversaturated with competing ideologies, value systems, functioning under the guise of pluralism of opinions. The multiplicity of fakes not only does not contribute to mutual understanding between different social groups, but also carries the threat of disorganization of society itself. From this point of view, the disorganization introduced into the mass consciousness gives rise to failures in the thinking of the collective subject.

Secure Message Dissemination with QoS Guaranteed Routing in Internet of Vehicles

Internet of Vehicles (IoV) is a variant of vehicular adhoc network (VANET) where vehicles can communicate with other vehicles, infrastructure devices, parking lots and even pedestrians. Communication to other entities is facilitates through various services like DSRC, C2C-CC. Fake messages can be propagated by attackers for various selfish needs. Complex authentication procedures can affect the propagation of emergency messages. Thus a light weight mechanism to ensure the trust of messages without affecting the delivery deadlines for emergency messages. Addressing this problem, this work proposes a clustering based network topology for IoV where routing is optimized for message dissemination of various classes using hybrid meta-heuristics. In addition, two stage message authentication technique combining collaborative authentication with Bayesian filtering is proposed to verify the authenticity of message. Through simulation analysis, the proposed solution is found to detect fake messages with an accuracy of 96% with 10% lower processing delay compared to existing works.

The Effect of Media Literacy on Misinformation and Deep Fake Video Detection

Misinformation has become a major problem in virtually all societies. The emergence of deepfake AI technology has only exacerbated issues around misinformation and disinformation. One way to try to limit the negative effects of fake news and false information in a society is media literacy. This study focused on the effect of media literacy on detecting fake news in both video and text messages. Based on Inoculation Theory, three hypotheses were suggested and a quasi-experimental research design was conducted. The participants included 204 highly educated Egyptians who were divided into three groups. One group was given a powerpoint lecture on the meaning of misinformation, its consequences, how to verify information received via social media, and examples of fake news. The second group was given a similar presentation but instead of talking about misinformation in general the presentation focused on deepfake AI technology. The third group was the control and were not given any media literacy information. Two types of fake messages were evaluated by each group; a Facebook post with text and an image and a deepfake video. The results of the study suggest the effectiveness of media literacy lectures in raising the rate of detection of fake news whether in the form of text or video. Results also indicated a decreased probability of sharing the fake messages. The study recommends designing media literacy lectures including both information on misinformation in general and specific information on the new technology creating fake news like deepfake.

Impact of endorsement policy on the performance of blockchain‐based VANET

AbstractWith the revolution in technology, VANET has emerged as a new generation of technology and used globally for intelligent traffic management systems. VANET uses a PKI‐based protocol to maintain privacy and security. Still, some crucial issues remain unresolved, like blind belief in communicated messages, lack of vehicle's interest in participation, revealing the sender vehicle's identity, and maintaining integrity. The solution to these issues will prevent vehicles from propagating fake messages while concurrently protecting the confidentiality of each involved vehicle against tracking attacks. Blockchain, a peer‐to‐peer, immutable, distributed ledger‐based technology, can be adapted as a robust solution to overcome critical challenges and make VANET more secure and reliable. This paper proposes a novel blockchain‐based VANET architecture, blockchain‐based vehicular ad‐hoc network (B‐VANET), implemented on the Hyperledger Fabric platform. B‐VANET aims to secure the network by validating road events before actually updating it on the network. For validation, the trust factor of each vehicle and their past involvement in the network is maintained. Trust factor value decides the trustworthiness of a vehicle. Based on this trust factor, a dynamic endorsement policy is designed. Endorsement policy, part of the smart contract, plays a vibrant role in Hyperledger Fabric. It decides how many and which particular peers can endorse the transaction to make it valid and add it to the ledger. Multiple possible approaches to endorsement policies are identified for B‐VANET and are analyzed using the Hyperledger Caliper tool. These approaches are evaluated for data integrity, confidentiality, and real‐time system issues. An appropriate approach for the B‐VANET system is recognized, giving the best throughput and latency in all possible scenarios. It is analyzed that compared to the worst approach, the best approach achieves an almost 15% increase in throughput and reduces latency by nearly half. We also compared the performance of B‐VANET and other approaches to show that our proposed system better meets VANET's security needs.

TRUCON: Blockchain-Based Trusted Data Sharing With Congestion Control in Internet of Vehicles

The Internet of vehicles (IoV) has a substantial impact on traffic efficiency improvement and accidents avoidance. Due to restricted resources, vehicles must share observed data with RSUs and other vehicles to execute some time-tolerant computing tasks. However, data provided by vehicles cannot always be trusted due to the presence of attackers. Fake messages could have catastrophic ramifications, such as vehicle collisions. Furthermore, extensive data sharing might cause channel congestion, resulting in the loss of vital messages during delivery. To overcome the aforementioned issues, we propose TRUCON, a blockchain-based trusted data sharing mechanism with congestion control in IoV. Firstly, we propose a Kademlia algorithm-based traffic data forwarding method to control channel congestion state. By adjusting the bucket size and distance threshold, source vehicles can limit the number of reference vehicles forwarded. Secondly, we present a cuckoo filter-based traffic data deduplication and discrimination approach. To avoid repetitive sharing, vehicles and RSUs can check their local filters to verify if the current data report has been shared. Based on the foregoing, we propose a blockchain-based trust management mechanism with congestion control. RSUs serve as full nodes while vehicles are light nodes in the blockchain. Finally, we develop a trust management prototype system with congestion control that incorporates both on-chain and off-chain parts. It signifies that our scheme is both feasible and effective.

PACM: Privacy-Preserving Authentication Scheme With on-Chain Certificate Management for VANETs

Privacy-preserving authentication is designed to protect vehicular ad-hoc networks (VANETs) from illegitimate users and fake messages while maintaining the privacy of legitimate users’ identities. However, existing authentication schemes have disadvantages such as non-transparent certificate issuance and revocation, high identity authentication and certificate revocation overhead. In this paper, we propose an efficient privacy-preserving authentication scheme with on-chain certificate management (PACM) in VANETs, where the service manager (SM) of each domain serves as a node of the blockchain to build a distributed system. Specifically, based on elliptic curve cryptography (ECC) and exclusive-OR operations, we achieve secure and lightweight mutual authentication between vehicles and roadside units (RSUs) by regularly updated pseudonyms. Then, we adopt the blockchain to record the issuance and revocation of all certificates, which makes SM’s activities transparent. Moreover, we introduce the counting garbled bloom filter (CGBF) to enable fast query and revocation of certificates. Besides, we design a non-forgeable and non-repudiable billing mechanism based on the hash chain technology. Security analysis and experimental results show that PACM achieves stronger security with less overhead.

Proactive Anti-Eavesdropping With Trap Deployment in Wireless Networks

Due to the open nature of the wireless medium, wireless communications are especially vulnerable to eavesdropping attacks. This article designs a new wireless communication system to deal with eavesdropping attacks. The proposed system can enable a legitimate receiver to get desired messages and meanwhile an eavesdropper to hear “fake” but meaningful messages by combining confidentiality and deception, thereby confusing the eavesdropper and achieving additional concealment that further protects exchanged messages. Towards this goal, we propose techniques that can conceal exchanged messages by utilizing wireless channel characteristics between the transmitter and the receiver, as well as techniques that can attract an eavesdropper to gradually approach a trap region, where the eavesdropper can get fake messages. We also provide both theoretical and empirical analysis of the established secure channel between the transmitter and the receiver. We develop a prototype system using Universal Software Defined Radio Peripherals (USRPs). Experimental results show that an eavesdropper at a trap location can receive fake information with a bit error rate (BER) close to 0, and the transmitter with multiple antennas can successfully deploy a trap area.

A Survey of Intelligent Detection Designs of HTML URL Phishing Attacks

Phishing attacks are a type of cybercrime that has grown in recent years. It is part of social engineering attacks where an attacker deceives users by sending fake messages using social media platforms or emails. Phishing attacks steal users’ information or download and install malicious software. They are hard to detect because attackers can design a phishing message that looks legitimate to a user. This message may contain a phishing URL so that even an expert can be a victim. This URL leads the victim to a fake website that steals information, such as login information, payment information, etc. Researchers and engineers work to develop methods to detect phishing attacks without the need for the eyes of experts. Even though many papers discuss HTML and URL-based phishing detection methods, there is no comprehensive survey to discuss these methods. Therefore, this paper comprehensively surveys HTML and URL phishing attacks and detection methods. We review the current state-of-art deep learning models to detect URL-based and hybrid-based phishing attacks in detail. We compare each model based on its data preprocessing, feature extraction, model design, and performance.

Relevance and Political Discourse: Cognitive Grammar Meets Relevance Theory

The paper seeks to account, in terms of the Cognitive Grammar (CG) approach, for what Relevance theorists call epistemic vigilance, a metarepresentational human ability, often adopted by the addressee in his/her attempts to detect and evaluate (potentially) fake messages. In this way, we wish to explore the possibility of rapprochement between Cognitive Linguistics and Relevance Theory. In our attempt to incorporate epistemic vigilance into the Cognitive Grammar format, we propose to set up a cognitive space called a Relevance Schema (RS), a special “module” associated with Ronald Langacker’s Current Discourse Space (CDS). We claim that epistemic vigilance is instrumental in deriving “an implicational character” of the “true”, intended message that emerges in the blended space as a result of the clash between the explicatures’ meaning and the addressee’s understanding of a “state of affairs”. In Section 2, we discuss Charles Forceville’s Relevance-theoretic account of a political poster featuring Barack Obama. This is followed by a presentation in Section 3 of Ronald Langacker’s theory of CDS. Finally, Section 4 develops an analysis of the front-page issue of the Russian independent newspaper Novaya Gazeta and the related article devoted to the Russian invasion of Ukraine.

ЭМОЦИОНАЛЬНЫЕ ОСОБЕННОСТИ ВОСПРИЯТИЯ ФЕЙКОВЫХ СООБЩЕНИЙ

This article considers and analyzes a new type of messages containing unreliable information, which in mass communication received the name "fake", as well as potential generatives of fake, the features of the operation of the mechanisms for its creation, translation and perception by the mass consciousness, which is largely carried out as a result of purposeful influence on the sphere of emotions of a modern person. Particular attention is paid to the demand and circulation of fakes in the mechanisms for broadcasting advertising and propaganda messages, as well as the functional role of fakes in the implementation of the intentions of the creators of advertising or propaganda containing false information. Fake is conceived, reproduced and replicated in the expectation of a purposeful impact on the sphere of emotions of its potential recipients. A fake must certainly have the property of "catching" emotions, and being held through them in perception in order to then, like a virus, unleash an epidemic of real "emotional infection". The mass distribution of fake messages became possible only against the background of a combination of technogenic globalization of the world information space and an artificially organized reference crisis within it. Based on the results of the study, the origins of the emotional appeal of fake messages are identified and analyzed, and the conditions under which this effect manifests itself most clearly are considered. In conclusion, such concepts as technogenic globalization of the information space, fake news, fact-checking and crisis of reference are explored.

A Novel Multifaceted Trust Management Framework for Vehicular Networks

Maintaining interconnectivity between dynamic vehicular nodes and ensuring trust in vehicular networking is still a challenging issue. It is crucial for the vehicular environments to maintain a stable interconnectivity between the vehicular nodes and further to prevent the vehicular nodes from broadcasting fake messages and simultaneously protect the vehicles against tracking attacks. An efficient Trust Management System (TMS) is proposed in this paper that will ensure trustworthiness and stable interconnectivity between vehicular entities to assure road safety and reliable communication. The proposed approach incorporates a novel timestamp mechanism and block chain concepts to ensure the steady inter-connectivity between the dynamic vehicular nodes. Further, the framework leverages block chain concepts to verify the correctness of the events stored in the Road Side Units (RSUs). It also proposes a versatile hybrid trust model that uses innovative direct and recommended trust evaluation techniques to compute trust between the vehicular entities. Moreover, the framework integrates a threading mechanism to schedule message execution in direct trust evaluation, and clustering techniques to group similar messages in indirect trust evaluation. Various experimental and comparative analyses with other related studies are carried out in a simulated environment to evaluate the performance of the proposed hybrid trust model. The findings show that the proposed trust model produces an accuracy of 92% in identifying malicious nodes.

Когнитивная интерпретация феномена «фейк» как жанра медиадискурса

The article is dedicated to the description of the cognitive mechanism of fake messages implementation in the media discourse. The controversial nature of this phenomenon is due to a number of linguistic and extralinguistic features, which determines a comprehensive cognitive-discursive analysis of the fake phenomenon in order to identify the sources of its destructiveness, the mechanisms that ensure the successful manifestation of the fake. The crucial violations of the basic laws of formal logic which function as a source of fake conflictogenity are examined in the paper, their productivity is revealed in this aspect, and several recommendations for verifying fake information are provided. The analyzed material is taken from the Internet pages of print and online media of various levels and formats, blogs functioning in the course of the media discourse. It is established that fake is a complex multi-stage cognitive-communicative action. The deliberate unreliability of a fake message determines the mandatory application of various kinds of violations of the norms of communication, first of all – the laws of formal logic. Violations of the laws of identity and sufficient reason are most often used, less often – the law of contradiction; the law of the excluded third, due to its specificity, is not used at all. Violation of the latter law inevitably leads to disavowal of falsification. Structural violations taking place in the cognitive model of a fake message contribute to the successful masking of fake information, as well as blocking the recipient’s critical thinking and, as a result, the intensification of speech effects on the addressee. In addition, the communicative perspective of a fake is associated with causing damage to the addressee. All these aspects make it possible to qualify fake as a destructive type of communicative actions involving the spreading of obviously unreliable socially significant information and aimed at the realization of speech influence on the addressee.