Search Results (14)

This paper proposes a hybrid model that combines convolutional neural networks with natural language processing techniques for least significant bit-based steganography detection in grayscale digital images. The proposed approach identifies hidden messages by analyzing subtle alterations in the least significant bits and validates the linguistic coherence of the extracted content using a semantic filter implemented with spaCy. The system is trained and evaluated on datasets ranging from 5000 to 12,500 images per class, consistently using an 80% training and 20% validation partition. As a result, the model achieves a maximum accuracy and precision of 99.96%, outperforming recognized architectures such as Xu-Net, Yedroudj-Net, and SRNet. Unlike traditional methods, the model reduces false positives by discarding statistically suspicious but semantically incoherent outputs, which is essential in forensic contexts. Full article

This research examined the domain of intelligent transportation systems (ITS) by analyzing the impact of scholarly work and thematic prevalence, as well as focusing attention on vehicles, their technologies, cybersecurity, and related scholarly technologies. This was performed by examining the scientific literature indexed in the Scopus database. This study analysed 2919 documents published between 2018 and 2025. The findings indicated that the highest and most significant journal was derived from IEEE Transactions on Vehicular Technology, with significant standing to the growth of communication and computing on vehicles with edge computing and AI optimization of vehicular systems. In addition, important PST research conferences highlighted the growing interest in academic research in cybersecurity for vehicle networks. Sensor networks, pose forensics, and privacy-preserving communication frameworks were some of the significant contributing fields marking the significance of the interdisciplinary nature of this research. Employing bibliometric analysis, the literature illustrated the multiple channels integrating knowledge creation and innovation in ITS through citation analysis. The outcome suggested an increasingly sophisticated research area, weighing technical progress and increasing concern about security and privacy measures. Further studies must investigate edge computing integrated with AI, advanced privacy-preserving linguistic protocols, and new vehicular network intrusion detection systems. Full article

Mental reconstruction (MRC) and Free Recall (FR) have been recognized for enhancing the quality of witness statements. However, the mechanisms underlying this association remain insufficiently understood. This study explores how the time allocated to MRC and FR and variations in educational level influence the quality of eyewitness testimonies. Testimony quality is evaluated based on manually annotated content information provided by experts in testimony assessment, which measures adherence to the events. This is further complemented by fine-grained linguistic features, automatically extracted using linguistic analysis tools, to capture stylistic aspects. As a proof of concept, the analysis is performed on a corpus of 96 testimonies in Spanish describing two robbery cases. The results suggest that both mental reconstruction and narration times positively impact the accuracy of testimonies, as inaccuracies predominantly involve peripheral details. Furthermore, while the study confirms that educational level affects testimony quality, no significant differences were observed in the frequency of erroneous reports. This study contributes to the understanding of the relationship between cognitive strategies and the accuracy of witness statements, proposing an analysis approach applicable to forensic psychology for witness assessment. Full article

This paper investigates the multimodal manifestations of denial in US legal contexts, in the English language, by analyzing police interviews and cross-examinations. The research uses a 13-h corpus of video recordings portraying five male suspects, aged 20–44, eventually charged and convicted of femicide. We deploy techniques from conversation analysis, multimodal analysis, and speech processing, using tools like ELAN, Praat, WebMAUS, and Python libraries to transcribe, annotate, and analyze audio–video data. This exploratory study identifies several recurring patterns in prosodic and gestural cues associated with denial. In particular, our results indicate a prototypical multimodal denial characterized by a predominant gestural component: head positioning (neutral or lowered) and head shaking. This gestural expression is frequently repeated and can also function independently as a nonverbal marker of denial. Denial is also often accompanied by open-hand gestures, sitting upright posture, and a certain degree of vagueness in speech. Furthermore, our findings suggest that the expression of denial often involves a reduction in pitch and intensity following the confession or indictment. The analysis of pauses before denial instances reveals that a greater number of pauses typically occurs after incrimination. Overall, this study shows that there is an interesting interplay between verbal and nonverbal features of denial in legal interactions, underscoring the need for further analysis. Full article

This applied linguistics study on the lay discourse about legal language analyses online public reactions to a court decision in the Sarah Halimi case, a French Jewish woman killed by her neighbour in Paris in 2017. This study draws on discourse analysis with a focus on semantics analysis and dialogism theory to delve into how legal discourse is disseminated in forums and undergoes semantic redefinition through users’ language practices of legal notion in their own discourses. Thus, the aim of this study is not to develop linguistics theories but to use linguistics to explore the relationship between (1) the public representation and perception of this murder case in three forums and (2) the politico-legal response to decisions about a lack of criminal responsibility. The latter remains a sensitive topic in several countries, and several criminal justice reforms are revised or implemented with close observation of public reaction. This analysis highlights the linguistic markers revealing emotional discourse and a polymorphous expression of a lack of confidence in the justice system and legal actors, emphasising issues in comprehending justice and the work of psychiatrists and highlighting a gap between expectations and the actual delivery of justice. This study also shows that the linguistic strategies of non-experts are similar to those of legal experts. Full article

As digital crime continues to rise, the preservation of digital evidence has become a critical phase in digital forensic investigations. This phase focuses on securing and maintaining the integrity of evidence for legal proceedings. Existing solutions for evidence preservation, such as centralized storage systems and cloud frameworks, present challenges related to security and collaboration. In this paper, we propose a novel framework that addresses these challenges in the preservation phase of forensics. Our framework employs a combination of advanced technologies, including the following: (1) Segmenting evidence into smaller components for improved security and manageability, (2) Utilizing steganography for covert evidence preservation, and (3) Implementing blockchain to ensure the integrity and immutability of evidence. Additionally, we incorporate Long Short-Term Memory (LSTM) networks to enhance steganography in the evidence preservation process. This approach aims to provide a secure, scalable, and reliable solution for preserving digital evidence, contributing to the effectiveness of digital forensic investigations. An experiment using linguistic steganography showed that the LSTM autoencoder effectively generates coherent text from bit streams, with low perplexity and high accuracy. Our solution outperforms existing methods across multiple datasets, providing a secure and scalable approach for digital evidence preservation. Full article

Misgendering is a form of microaggression that reinforces gender binarism and involves the use of incorrect pronouns, names or gendered language when referring to a transgender and gender non-conforming (TGNC) individual. Despite growing awareness, it remains a persistent form of discrimination, and it is crucial not only to understand and address misgendering but also to analyse its impact within online discourse towards the TGNC community. The present study examines misgendering directed at the TGNC community present on platform X. To achieve this, a representative sample of 400 tweets targeting two TGNC individuals is compiled, applying an annotation scheme to manually classify the polarity of each tweet and instances of misgendering, and then comparing the manual annotations with those of an automatic sentiment detection system. The analysis focuses on the context and frequency of intentional misgendering, using word lists to examine the data. The results confirm that misgendering perpetuates discrimination, tends to co-occur with other forms of aggression, and is not effectively identified by automatic sentiment detection systems. Finally, the study highlights the need for improved automatic detection systems to better identify and address misgendering in online discourse and provides potentially useful tools for future research. Full article

Fraud exists on both legitimate e-commerce platforms and illicit dark web marketplaces, impacting both environments. Detecting fraudulent vendors proves challenging, despite clients’ reporting scams to platform administrators and specialised forums. This study introduces a method to differentiate sellers reported as scammers from others by analysing linguistic patterns in their textual traces collected from three distinct cryptomarkets (White House Market, DarkMarket, and Empire Market). It distinguished between potential scammers and reputable sellers based on claims made by Dread forum users. Vendor profiles and product descriptions were then subjected to textometric analysis for raw text and N-gram analysis for pre-processed text. Textual statistics showed no significant differences between profile descriptions and ads, which suggests the need to combine language traces with transactional traces. Textometric indicators, however, were useful in identifying unique ads in which potential scammers used longer, detailed descriptions, including purchase rules and refund policies, to build trust. These indicators aided in choosing relevant documents for qualitative analysis. A pronounced, albeit modest, emphasis on language related to ‘Quality and Price’, ‘Problem Resolution, Communicationand Trust’, and ‘Shipping’ was observed. This supports the hypothesis that scammers may more frequently provide details about transactions and delivery issues. Selective scamming and exit scams may explain the results. Consequently, an analysis of the temporal trajectory of vendors that sheds light on the developmental patterns of their profiles up until their recognition as scammers can be envisaged. Full article

This study aims to establish a methodological framework for investigating deception in both spoken and written language production. A foundational premise is that the production of deceitful narratives induces a heightened cognitive load that has a discernable influence on linguistic processes during real-time language production. This study includes meticulous analysis of spoken and written data from two participants who told truthful and deceitful narratives. Spoken processes were captured through audio recordings and subsequently transcribed, while written processes were recorded using keystroke logging, resulting in final texts and corresponding linear representations of the writing activity. By grounding our study in a linguistic approach for understanding cognitive load indicators in language production, we demonstrate how linguistic processes, such as text length, pauses, fluency, revisions, repetitions, and reformulations can be used to capture instances of deception in both speaking and writing. Additionally, our findings underscore that markers of cognitive load are likely to be more discernible and more automatically measured in the written modality. This suggests that the collection and examination of writing processes have substantial potential for forensic applications. By highlighting the efficacy of analyzing both spoken and written modalities, this study provides a versatile methodological framework for studying deception during language production, which significantly enriches the existing forensic toolkit. Full article

Despite the increasing visibility of transgender and gender diverse (TGD) people in U.S. society, current linguistic practices within forensic anthropology and death investigation in general are not TGD-inclusive. This lack of consideration for TGD decedents can cause unnecessary delays in the identification and disposition of their remains; moreover, failing to recognize their true identities is a form of forced post-mortem detransition. Using De León’s concept of necroviolence as a framework, we argue that language can also harm the dead and that the (mis)use of language within medicolegal death investigation reflects and reinforces structural violence against TGD people. Examples drawn from a qualitative review of public details for 87 cases are used to demonstrate how language and language-enforced bureaucratic structures can harm TGD decedents, their loved ones, the broader TGD community, and the process of medicolegal death resolution itself. We then suggest steps that anthropologists, death investigators, and their affiliated partners can take to reduce the systemic necropolitical violence faced by the TGD community. While TGD-inclusive methods will take time to implement at the institutional level, individual practitioners can enact significant change within the system by upholding core standards that recognize and respect the personhood and lived experiences of TGD decedents. Full article

The act of lying and its detection have raised interest in many fields, from the legal system to our daily lives. Considering that testimonies are commonly based on linguistic parameters, natural language processing, a research field concerned with programming computers to process and analyse natural language texts or speech, is a topic of interest on this front. This study aimed to examine the linguistic styles of simulated deception and true testimonies collected with the aim of studying witness memory. Study participants were asked to act as a witness of a crime by retelling the story they had just read. Cognitive interviewing techniques were used to collect testimony under two conditions: truth and simulated deception. A sample of 48 participants volunteered to participate in the study. Analyses of the linguistic indicators and content were carried out. Specifically, we performed a comparison of testimonies of the same participant by condition to analyse the variation between (i) lexical and (ii) linguistic features and (iii) content and speech characteristics (disfluencies) depending on the narrative condition. Concerning lexical properties, adjectives were the most-varying grammatical category between truthful and deceptive testimonies. Furthermore, in the linguistic analysis, we observed that truthful testimonies were generally longer than deceptive ones in terms of the number of words and sentences and also characterised by more articulated sentence structures, and these differences were also statistically significant. Regarding the analysis of the content, cognitive criteria (details) and admitting lack of memory were more present in truthful statements. By providing an objective measure, these results are of interest in developing NLP tools for assessing the credibility of testimonies in forensics. Full article

Forensic authorship profiling aims to extract socio-demographic information about the authors of anonymous texts based on linguistic features found in their written texts. One aspect of such a profile that is not usually considered is an analysis of the author’s personality traits. The present study intends to provide a starting point for research into this area. This will be achieved through the investigation of linguistic features reflective of the conditions of psychopathy and malignant narcissism in 14 personal letters of the killer Jack Unterweger. Previous research in the field of psychology has largely examined “psychopathic” and “narcissistic” language in spoken conversations rather than written ones, or in texts produced for the respective study rather than in naturally occurring texts. The findings of the present study thus diverge from previous findings in some aspects (e.g., the use of first-person pronouns), while they provide support for others (e.g., the incoherence of thoughts and changes in topics). Full article

Mexico is a rich source for anthropological and population genetic studies with high diversity in ethnic and linguistic groups. The country witnessed the rise and fall of major civilizations, including the Maya and Aztec, but resulting from European colonization, the population landscape has dramatically changed. Today, the majority of Mexicans do not identify themselves as Indigenous but as admixed, and appear to have very little in common with their pre-Columbian predecessors. However, when the maternally inherited mitochondrial (mt)DNA is investigated in the modern Mexican population, this is not the case. Control region sequences of 2021 samples deriving from all over the country revealed an overwhelming Indigenous American legacy, with almost 90% of mtDNAs belonging to the four major pan-American haplogroups A2, B2, C1, and D1. This finding supports a very low European contribution to the Mexican gene pool by female colonizers and confirms the effectiveness of employing uniparental markers as a tool to reconstruct a country’s history. In addition, the distinct frequency and dispersal patterns of Indigenous American and West Eurasian clades highlight the benefit such large and country-wide databases provide for studying the impact of colonialism from a female perspective and population stratification. The importance of geographical database subsets not only for forensic application is clearly demonstrated. Full article

Symptom exaggeration and feigned cognitive impairment occur commonly in forensic and medicolegal evaluations. As a result, methods to detect feigned cognitive impairment are an indispensable component of neuropsychological assessments. This study reports the results of two neurophysiological experiments using a forced-choice recognition task built from the stimuli of the Word Memory Test and Medical Symptom Validity Test as well as a new linguistically informed stimulus set. Participant volunteers were instructed either to do their best or to feign cognitive impairment consistent with a mild traumatic brain injury while their brain activity was monitored using event-related potentials (ERP). Experiment 1 varied instructions across individuals, whereas Experiment 2 varied instructions within individuals. The target brain component was a positive deflection indicating stimulus recognition that occurs approximately 300 ms after exposure to a stimulus (i.e., the P300). Multimodal comparison (P300 amplitude to behavioral accuracy) allowed the detection of feigned cognitive impairment. Results indicate that, for correct responses, P300s were equivalent for the simulated malingering and good effort conditions. However, for incorrect responses, feigned impairment produced reliable but significantly reduced P300 amplitudes. Although the P300 is an automatic index of recognition—even when knowledge is hidden—its amplitude appears capable of modulation by feigning strategies. Implications of this finding are discussed for research and clinical applications. Full article