Deceptive Method Research Articles

Assessing synthetic milk through the detection of anionic detergents in milk: a concise review

Abstract The emergence of synthetic milk as a deceptive method for economic gains involves the incorporation of adulterants such as starch, urea, inexpensive vegetable oils, glucose detergent, water, and preservatives. This fraudulent mixture, seamlessly integrated with pure milk, deceives consumers by maintaining a natural taste and appearance. However, this unethical practice poses significant health risks, particularly to vulnerable demographics like children, pregnant women, and individuals with compromised immune systems. Despite being prevalent in both developed and developing countries, this deceptive practice could extend to other regions, given the global demand for milk and dairy products. Various methods exist for detecting synthetic milk, and one prominent approach involves identifying anionic detergent, which serves as a key component in the production of synthetic milk, acting as an emulsifier for vegetable oils and other ingredients. Therefore, this paper aims to provide an in-depth review of the role of detergent in synthetic milk production, highlighting the evolution of detection technologies. Additionally, a comparative analysis of the strengths and limitations of the method for identifying anionic detergent in synthetic milk is presented.

Navigating the Gray: Design Practitioners' Perceptions Toward the Implementation of Privacy Dark Patterns

Designers are sometimes accused of using deceptive methods to manipulate users' information privacy decisions through "privacy dark patterns." Through semi-structured interviews, we explore the perceptions of 23 design practitioners towards the implementation of "privacy dark patterns" created by other designers. This paper explores designers' perceived responsibilities toward users' privacy and their interpretations of the reasons behind the design implementation. We found a range of empathetic rationales among our participants toward other designers' intentions. An example theme is Designer Followed the Status Quo, where common and widely used privacy interfaces are normalized a practice that is typically viewed as reasonable. Our participants' interpretations of the design intent influenced their self-reported practices for navigating similar design requests. We propose a set of factors that influence privacy design practices, including following conventions and norms, ensuring legal compliance, reliance on established usability standards, perceived benefits to businesses and consumers, and degrees of privacy harm.

Algorithm of face anti-spoofing based on pseudo-negative features generation.

Despite advancements in face anti-spoofing technology, attackers continue to pose challenges with their evolving deceptive methods. This is primarily due to the increased complexity of their attacks, coupled with a diversity in presentation modes, acquisition devices, and prosthetic materials. Furthermore, the scarcity of negative sample data exacerbates the situation by causing domain shift issues and impeding robust generalization. Hence, there is a pressing need for more effective cross-domain approaches to bolster the model's capability to generalize across different scenarios. This method improves the effectiveness of face anti-spoofing systems by analyzing pseudo-negative sample features, expanding the training dataset, and boosting cross-domain generalization. By generating pseudo-negative features with a new algorithm and aligning these features with the use of KL divergence loss, we enrich the negative sample dataset, aiding the training of a more robust feature classifier and broadening the range of attacks that the system can defend against. Through experiments on four public datasets (MSU-MFSD, OULU-NPU, Replay-Attack, and CASIA-FASD), we assess the model's performance within and across datasets by controlling variables. Our method delivers positive results in multiple experiments, including those conducted on smaller datasets. Through controlled experiments, we demonstrate the effectiveness of our method. Furthermore, our approach consistently yields favorable results in both intra-dataset and cross-dataset evaluations, thereby highlighting its excellent generalization capabilities. The superior performance on small datasets further underscores our method's remarkable ability to handle unseen data beyond the training set.

The Silent Threat: Human Trafficking and Migrant Smuggling Procedures Through the Eyes of the Courts in Malaysia

Human trafficking and migrant smuggling are two severe international crimes. It is a worldwide problem, and it is particularly pernicious. There are many parts to human trafficking, including forced labour, commercial sex, and organ harvesting. Human traffickers are known to use violent, manipulative, and deceptive methods and tactics to lure vulnerable targets into situations of exploitation. In Malaysia, it is estimated that millions of men, women, and children worldwide are victims of these crimes. The international community has adopted the protocols and the United Nations Office on Drugs and Crime in assisting member states to combat these internationally organised crimes. However, there are inadequate laws, both substantive and procedural, in place to deal with those who perpetrate such crimes, to bring them to account for their misdeeds, and to protect the victims of such crimes where such crimes continue to flourish because of the high profits.

Research of the potential impact of social engineering on digital transformation processes

Digital transformation reflects the process of transition of society and business to new technologies in order to improve operations and provide new opportunities. This process includes the introduction of artificial intelligence, the Internet of Things, cloud services, and many other technologies to improve business results. Such a transition promises us a number of advantages, including: flexibility and efficiency of digital processes due to total computerization; increasing the efficiency of production processes due to the Internet of Things, cloud computing and automation; improving interaction with customers based on artificial intelligence. This is far from a complete list of Digital Transformation technologies. But no matter how many technologies appear in the future, all of them, along with their advantages, create new challenges for cyber security, including those based on social engineering methods. Social engineering is manipulative and deceptive methods used to obtain confidential information, access systems, or gain control over people through the use of psychological and social mechanisms. The article examines known cases of cyberattacks using social engineering. It is shown that neither small nor large companies are immune from social engineering. The basic model of social engineering, which is based on the tendency of people to transfer all information and perform illogical actions, was studied. Social engineering technologies were analyzed: Pretexting, Phishing, Road apple, Shoulder surfing, Service for service. Potential threats from social engineering methods for systems: SCADA, Cloud Computing, Internet of Things, Drones, Big Data, Blockchain, Artificial Intelligence and Social Networks are assessed. The key means of countering social engineering in the era of digital transformation have been identified. Conclusions are made regarding the impact of social engineering in the future.

Ethical Implications of Deceptive Earnings Management Practices

This study is devoted to the analysis of the consequences of using deceptive methods of income management and the justification of the importance of observing ethical standards in financial reporting to ensure the sustainable development of companies. The purpose of the study is to evaluate existing revenue management practices from the point of view of their compliance with ethical standards of business conduct. Based on a critical review of the literature on income management, it was concluded that the use of fraudulent methods, especially in the preparation of financial statements, reduces the integrity and reliability of information used by interested parties in making management decisions, distorts the distribution of resources, hinders the efficient functioning of capital markets and endangers the stable functioning of the economy. Based on the results of the study, it was concluded that transparency, honesty and accountability in financial activities play an important role in creating a business environment that encourages fair and honest behavior. This contributes to the preservation of the interests of interested parties and the sustainable growth of the economy due to the observance of ethical standards in the field of business. Based on the analysis of existing revenue management practices, it has been proven that the artificial increase in share prices, the use of shadow financial transactions, and the reduction of the workforce contribute to the growth of companies’ profits, due to the dismissal of experienced employees and, possibly, the reduction of its future competitiveness. The study theoretically proves the need for organizations to find a balance between financial activities and their compliance with ethical norms, taking into account that short-term profits achieved through questionable activities can ultimately lead to a decrease in trust in companies and serve as a threat to their long-term viability. The results of the study can be useful for managers of enterprises, shareholders and subjects of the financial system as a whole from the point of view of a deeper understanding of ethical problems related to income management and ways to increase the transparency and reliability of information displayed in financial statements of companies.

Spam Spoiler for Public and Private E-Mail Services

Abstract: In recent years, cyber security incidents have occurred frequently. In most of these incidents, attackers have used different types of spam email as a knock-on to successfully invade government systems, well-known companies, and websites of politicians and social organizations in many countries. The detection of spam mail from big email data has been paid public attention. However, the camouflage technology of spam mail is becoming more and more complex, and the existing detection methods are unable to confront the increasingly complex deception methods and the growing number of emails. In this project, we proposed to design a novel efficient approach named Spam Spoiler for big email data classification into four different classes: Normal, Fraudulent, Harassment, and Suspicious E-mails by using LSTM-based GRU. The new method includes two important stages, the sample expansion stage and the testing stage under sufficient samples. This project the LSTM-based GRU efficiently captures meaningful information from E-mails that can be used for forensic analysis as evidence. Experimental results revealed that Spam Spoiler performed better than existing ML algorithms and achieved a classification accuracy of 98% using the novel technique of LSTM with recurrent gradient units. As different types of topics are discussed in E-mail content analysis. Spam Spoiler effectively outperforms existing methods while keeping the classification process robust and reliable.

ANALYSIS OF THE INFLUENCE OF FLEXING IN SOCIAL MEDIA ON COMMUNITY LIFE

Flexing or showing off is a new habit that society loves today. Technological factors also influence flexing behavior. We Are Social 2023 states that 167 million Indonesians are active in using social media. In general, flexing is done to legitimize social status through various uploads on TikTok, Instagram Facebook, YouTube and others. This study aims to analyze of the influence of flexing in social media on community life. The results of the study show that luxury content is a favorite of netizens. Even though the luxury that is displayed is not necessarily true. Flexing has a positive influence because flexing can be used for employee branding through linkedIn. Flexing marketing to achieve success in popularity, business and endorsement. However, overall flexing has a more negative influence . Flexing creates consumptive behavior and hedonism. The use of goods is no longer interpreted as fulfilling needs, but rather as a brand or symbol to indicate lifestyle and social status. Sometimes flexing is used as a method of deception. POLRI and related institutions are expected to be more informative, actively monitoring and countering flexing content that has the potential to harm the public. Intelligence is needed in managing information, in the era of information openness, people must be more proficient in social media, filter flexing content so they don't get trapped in FOMO. Actually, flexing is the right of each individual, but it should not reduce the values of life. Minimize flexing behavior by prioritizing achievement.

Middle-shallow feature aggregation in multimodality for face anti-spoofing

At present, most advanced algorithms for face anti-spoofing use stacked convolutions and residual structure to obtain complex characteristics of deep networks, and then distinguish liveness and deception. These methods ignore the shallow features that contain more detailed information. As a result, the model lacks sufficient fine-grained information, which affects the accuracy and robustness of the algorithm. In this paper, we use the simple features of the shallow network to increase the fine-grained information of the model, so as to improve the performance of the algorithm. First of all, the shallow features are spliced to the middle layer by "shortcut" structure to reserve more details for the middle layer features and improve their detail representation ability. Secondly, the network is initialized with the best pre-trained model parameters under unbalanced samples, and then trained on the balanced samples to improve the classification ability of the model. Finally, RS Block based on depthwise separable convolution is used to replace res module, and model parameters and floating point operations are reduced from 18G and 61 M to 1.9 M and 347 M. The algorithm is simulated on CASIA-SURF dataset, and the results show that the average classification error rate (ACER) is only 0.0008, TPR@FPR = 10E−4 reaches 0.9990, which is better than the previous face anti deception methods.

The effect of deception in simulation-based education in healthcare: a systematic review and meta-analysis

Simulation in healthcare education enables learners to practice in a realistic and controlled environment without putting real patients at risk. Deception can be incorporated to generate a realistic learning experience. We aim to perform a systematic review of the literature to study the effect of deception in SBE in healthcare. Online database search was performed from conception up to the date of search (December 2023). Qualitative descriptive analysis included all published and unpublished works as for the quantitative analysis, only randomized clinical trials with an objective measurement tool relating to learner’s performance were included. Forward citation tracking using SCOPUS to identify further eligible studies or reports was also applied. Twelve out of 9840 articles met the predefined inclusion criteria. Two randomized controlled trials were identified using deception for the intervention group and ten articles provided current knowledge about the use of deception in simulation-based education in healthcare. The aspects discussed in the latter articles related to the possible forms of deception, its benefits and risks, why and how to use deception appropriately, and the ethics related to deception. Although this meta-analysis shows that using deception in SBE in healthcare by challenging authority negatively affects the trainees’ performance on the mAIS scale, this approach and other forms of deception in SBE, when used appropriately and with good intent, are generally accepted as a valuable approach to challenge learners and increase the level realism of SBE situations. Further randomized trials are needed to examine and confirm the effect of other deceptive methods and the true psychological effect of those interventions on validated scales.

The Influence of False Evidence Ploy Variants on Perceptions of Coercion and Deception

ABSTRACT During custodial interrogations, law enforcement officers are permitted to use various methods of deception to entice a suspect to give a confession. Two of these commonly used methods include the False Evidence Ploy (FEP), and its less deceptive variant, the bluff tactic. While the bluff may appear to be less deceptive than the FEP, it also influences innocent suspects to confess. In the present study, participants were shown 1 of 6 possible videos that varied the type of deception (control/FEP/bluff) along with interrogation length (1/12 hours). Participants rated how deceptive and coercive the interrogation was and rendered guilt judgments. The results indicates that participants failed to distinguish between the FEP and the bluff in terms of either deceptiveness or coercion, and neither deception type nor interrogation length impacted guilt judgments. Judgments of deception, coercion, and guilt intercorrelated, but participants did not distinguish between the deception tactics in any of these measures. The implications for juror evaluations of videotaped confessions during trial and the potential for wrongful convictions are discussed.

How do participants feel about the ethics of rich false memory studies?

ABSTRACT Deception is often a necessity in rich false memory studies, but is this deception acceptable to participants? In the current study, we followed up with 175 participants who had taken part in a replication of the Lost in the Mall childhood false memory study (Loftus & Pickrell, 1995), as either a research subject or a familial informant. We found that both participants and informants were generally very positive about their experience, did not regret taking part and found the deceptive methods acceptable. Importantly, the vast majority reported that they would still have taken part had they known the true objectives from the beginning. Participants also reported learning something interesting about memory and enjoying the nostalgia and family discussions that were prompted by the study. We would encourage other researchers to assess the ethical implications of false memory research paradigms and to incorporate the valuable feedback from participants and informants.

Маніпулятивні повідомлення в заголовках новинних інтернет-ресурсів

In this article, the author analyzes headlines on digital platforms of media sources, paying special attention to the manipulative content of the delivered messages. The research is based on the material of three websites, the authors of which manipulate the emotions of the recipients while providing information – “Tarasova Pravda“, “Naspravdi.Today”, and “Politeka”. The basic aspects of suggestive influence and manipulation of information during the speech act are defined and explained. In today’s rapidly evolving digital landscape, understanding the linguistic mechanisms of manipulation and suggestion in written texts is becoming increasingly crucial. This study not only sheds light on the deceptive methods employed in the headlines of Ukrainian-language online media resources but also highlights the importance of fostering media literacy among consumers. By enhancing awareness of manipulative tactics and promoting critical thinking skills, individuals will be better equipped to discern fact from fiction, ultimately contributing to a more well-informed society. The advancement and proliferation of cutting-edge information and communication technologies have led to an escalation in dissemination of manipulative texts. It can be posited that suggestion operates as a crucial instrument of verbal manipulation in written materials. Consequently, our investigation primarily focuses on examining manipulative texts (specifically, headlines within the context of this article) concerning their suggestive component. The research vectors for this issue encompass the linguistic analysis of manipulative texts for categorization purposes, recognition of suggestion components, exploration of linguistic methods employed for disinformation, and countermeasures against verbal manipulation. The objective of this article is to scrutinize the headlines of news articles featured on Ukrainian-language online media resources (based on materials from three websites: “Tarasova Pravda”, “Naspravdi.Today”, and “Politeka”), as well as to detect, classify, and interpret linguistic techniques of manipulation and suggestion used in messages, excluding the proportion of manipulative content. The conducted linguistic analysis based on the 100 headlines gives reason to claim that 66.67 % of the article headlines in the three news web portals we have investigated contain messages aimed at manipulating emotions – “Tarasova Pravda” (33.33 %), “Naspravdi.Today” (76.67 %), and “Politeka” (90 %).

Digital Authoritarianism in the Middle East: Deception, Disinformation and Social Media

Digital Authoritarianism in the Middle East: Deception, Disinformation and Social Media

Undercover Investigation, Liquor Laws, and “Disreputable” Detectives in Late Nineteenth-Century Canada

Undercover police operations have long been recognized as legally and ethically problematic, leading to court appeals and discourse about the appropriate uses of deception and manipulation by the state. The issue was debated as early as 1886, when a jury acquitted a defendant because the evidence was gathered using deceptive methods. In Sarnia, Ontario, a tavern owner named Charles Hand was accused of orchestrating the bombing of temperance leaders’ homes. The Crown’s case was based on the testimony of an undercover detective who had befriended Hand and his family. With the support of the trial judge, the defense discredited the testimony on the basis that detectives were thought to be disreputable people, a fact confirmed by their surreptitious investigations under the Canada Temperance Act. At issue was the question of how anyone could accept the testimony of a detective who had used lies and deception against reputable people. While, ostensibly, the Hand case was a victory of the “wets” over the “drys,” it was also a conflict over police modernization. In this case study, I argue that the employment of this novel investigative tactic challenged traditional, anti-modern, views about the appropriate limits of police behaviour, deception, and state authority.

Deceiving Research Participants: Is It Inconsistent With Valid Consent?

It is widely assumed that the use of deception in research is always inconsistent with obtaining valid consent. In addition, guidelines and regulations permit research without valid consent only when it poses no greater than minimal risk. Current practice thus prohibits studies that use deception and pose greater than minimal risk, including studies that rely on deceptive methods to evaluate experimental treatments. To assess whether these prohibitions are justified, the present paper evaluates five arguments that might be thought to support the assumption that deception is always inconsistent with valid consent. Analysis of these arguments reveals that deception is frequently, but not always, inconsistent with obtaining valid consent for research. This conclusion suggests that, in order to avoid unnecessarily blocking valuable research, current policies and practice should be revised to recognize the conditions under which the use of deception can be consistent with obtaining research participants' valid consent.

Thermal tolerance of two Diptera that pollinate thermogenic plants

Pollinating insects can be exposed to temperatures far from ambient air when visiting flowers, reducing their warming tolerance. Typically, such scenario occurs when flowers are exposed to solar radiation. The case of thermogenic flowers is particular because they warm up even when they are not exposed to solar energy. The flowers of Arum attract their pollinators with a deceptive method and trap them for a whole day, thereby imposing elevated temperature to visiting insects. Therefore, we predict a relatively high basal thermal tolerance in those insects. The aim of this study was to assess the thermal tolerance and warming tolerance of females of two fly species (genus Psychoda) pollinating Arum sp. (thermogenic plant). We measured their critical temperature (CTmax) and its response to rate of temperature increase as well as acclimation period to moderate temperature of 25 °C. We found relatively low CTmax (33.7 °C on average) for both species, and a weak response to acclimation period and ramping rate. In general, the thermal tolerance increased with a rapid ramping in temperature. To evaluate the warming tolerance, we compared thermal tolerance limits to flower temperatures measured in the field. We highlighted that the temperature of the thermogenic floral organ could reach values close to the thermal tolerance threshold of pollinators. This discovery raises questions about the sustainability of the interaction between these thermogenic plants and their pollinators.

Experiencing misinformation: The effect of pre-exposure warnings and debunking on psychic beliefs.

Misinformation can have a detrimental impact on our beliefs, and it is therefore necessary to understand the cognitive mechanism by which false information is integrated or can be changed. In two experiments, we worked with fake psychic demonstrations, because observers easily adopt the experience as reflecting a "true" psychic event. We manipulated the availability of alternative explanations by providing a general warning that the performer is a magician with no psychic abilities (Experiment 1) or disclosing afterwards how the fake demonstration had been staged (Experiment 2). In Experiment 1, witnessing the psychic demonstration significantly increased participants' psychic beliefs, even though they had been warned. However, providing the alternative explanation about the deceptive method mitigated this effect. In Experiment 2, the realisation of deception significantly reduced participants' psychic beliefs directly after the performance and remained reduced 1 week later.

An Attribute Extraction for Automated Malware Attack Classification and Detection Using Soft Computing Techniques.

Malware has grown in popularity as a method of conducting cyber assaults in former decades as a result of numerous new deception methods employed by malware. To preserve networks, information, and intelligence, malware must be detected as soon as feasible. This article compares various attribute extraction techniques with distinct machine learning algorithms for static malware classification and detection. The findings indicated that merging PCA attribute extraction and SVM classifier results in the highest correct rate with the fewest possible attributes, and this paper discusses sophisticated malware, their detection techniques, and how and where to defend systems and data from malware attacks. Overall, 96% the proposed method determines the malware more accurately than the existing methods.

Integrating Cyber Deception Into Attribute-Based Access Control (ABAC) for Insider Threat Detection

Insider threat is an ever-present challenge to corporate security. The availability of knowledge and privileges to insiders makes it extremely difficult to prevent, detect or deter malicious insider activities. In the literature, several studies have proposed deception-based approaches to mitigate insider threats through different layers of corporate systems. However, the integration of access control and cyber deception methods has not been adequately discussed. In this paper, we integrate Attribute-based Access Control (ABAC) with honey-based deception techniques to effectively track insiders, particularly in the context of a dynamic work environment. To the best of our knowledge, this is the first study to design, implement and evaluate this integration. Our evaluation results show that the proposed framework reliably identifies sensitive attributes in the system and generates indistinguishable honey values to protect them with an average similarity score of 0.90 to the truth.