publications

2026

1. 

   SynthForensics: A Multi-Generator Benchmark for Detecting Synthetic Video Deepfakes

   Roberto Leotta, Salvatore Alfio Sambataro, Claudio Vittorio Ragaglia, and 5 more authors

   2026

   Abs arXiv

   The landscape of synthetic media has been irrevocably altered by text-to-video (T2V) models, whose outputs are rapidly approaching indistinguishability from reality. Critically, this technology is no longer confined to large-scale labs; the proliferation of efficient, open-source generators is democratizing the ability to create high-fidelity synthetic content on consumer-grade hardware. This makes existing face-centric and manipulation-based benchmarks obsolete. To address this urgent threat, we introduce SynthForensics, to the best of our knowledge the first human-centric benchmark for detecting purely synthetic video deepfakes. The benchmark comprises 6,815 unique videos from five architecturally distinct, state-of-the-art open-source T2V models. Its construction was underpinned by a meticulous two-stage, human-in-the-loop validation to ensure high semantic and visual quality. Each video is provided in four versions (raw, lossless, light, and heavy compression) to enable real-world robustness testing. Experiments demonstrate that state-of-the-art detectors are both fragile and exhibit limited generalization when evaluated on this new domain: we observe a mean performance drop of 29.19% AUC, with some methods performing worse than random chance, and top models losing over 30 points under heavy compression. The paper further investigates the efficacy of training on SynthForensics as a means to mitigate these observed performance gaps, achieving robust generalization to unseen generators (93.81% AUC), though at the cost of reduced backward compatibility with traditional manipulation-based deepfakes. The complete dataset and all generation metadata, including the specific prompts and inference parameters for every video, will be made publicly available at [link anonymized for review].

2. 

   Digital technologies and artificial intelligence in eating disorders: A scoping review of prevention, screening, and treatment approaches

   Mirko Casu, Lucrezia Marletta, Claudio Vittorio Ragaglia, and 2 more authors

   Computers in Human Behavior Reports, 2026

   Abs DOI

   Eating disorders (EDs) are prevalent and often underdiagnosed, and digital interventions may reduce barriers to prevention, early detection, and care. This scoping review mapped research published in 2015–2025 on digital health and artificial intelligence (AI) applications for ED prevention, screening/assessment, prediction/monitoring, and treatment. Searches were run on 29 December 2025 via the University of Catania Primo VE discovery service, covering collections including Scopus, PubMed, PsycArticles, and others. Records were deduplicated and screened using a human-in-the-loop workflow with Large Language Model prioritization used only to order citations for manual screening; study data were charted with a standardized form and synthesized descriptively. The search retrieved 9390 records; 1845 duplicates were removed, leaving 7545 unique records. Screening focused on the top 500 prioritized citations; 191 full texts were assessed and 40 studies were included. Included evidence split evenly between digital technologies (n = 20; mainly web/app cognitive-behavioral programs, screening/triage platforms, and virtual reality interventions) and AI (n = 20; mainly machine learning and natural language processing approaches for risk detection and prediction, plus emerging chatbot-based supports). Evidence was heterogeneous and often early-stage, with limited external validation and uneven reporting of safety and implementation. Future work should emphasize workflow-integrated evaluations, engagement and equity, and governance frameworks for automated tools.

2025

1. 

   Causal Prototype Attention Classifier: An Interpretable Model for Credit Card Fraud Detection Under Extreme Class Imbalance

   Claudio Giusti, Mirko Casu, Luca Guarnera, and 1 more author

   In Ital-IA, 2025

   Workshop 6: AI for Cybersecurity

   Abs

   Detecting fraud in real-world financial systems presents unique challenges due to the extreme imbalance between legitimate and fraudulent transactions. This imbalance not only hampers the performance of standard classifiers but also calls for models that are both effective and interpretable. In this work, we propose the Causal Prototype Attention Classifier (CPAC), an interpretable model tailored for binary classification under severe class imbalance. CPAC learns two class prototypes in latent space and uses a per-feature attention mechanism to compute weighted distances from each input to the prototypes. This architecture enables interpretable, prototype-based reasoning instead of relying on opaque decision boundaries. We evaluate CPAC on a credit card fraud detection task, comparing it to standard classifiers, Logistic Regression, Random Forest, and XGBoost, under two widely adopted oversampling strategies: SMOTE and a VAE–GAN-based generator. Results show that CPAC achieves performance comparable to black-box models while offering greater transparency and stability, particularly when trained on VAE–GAN augmented data. These findings support the adoption of CPAC in applications where both performance and interpretability are essential.

2. 

   Smoking Detection and Cessation: An Updated Scoping Review of Digital and Mobile Health Technologies

   Mirko Casu, Francesco Guarnera, Giusy Rita Maria La Rosa, and 4 more authors

   IEEE Journal of Biomedical and Health Informatics, 2025

   Abs DOI

   Digital and mobile health technologies offer promising solutions for smoking detection and cessation. This scoping review examines the current state of research and development in this field, encompassing smartphone applications, wearable devices, and sensor-based systems. We analyzed 49 studies published between 2019 and 2023 from PubMed and ACM Digital Library, focusing on technology features, outcomes, and evaluation methods. Wearable sensors and smartphone apps show potential in combating smoking addiction and improving quit rates. Motion sensors for hand-to-mouth gesture detection achieve high accuracy in controlled settings but face challenges in real-world applications. Machine learning models and wireless signal detection techniques yield encouraging results but require further refinement. Smartphone apps provide personalized plans and progress tracking, though most rely on manual logging and lack rigorous scientific evaluation. Our findings suggest that digital health technologies could significantly enhance smoking cessation efforts. However, more robust evaluation methods and integration of sensor data with machine learning are needed to improve usability and effectiveness. Continued research and innovation in this field are crucial for developing reliable, practical solutions and integrating these technologies into clinical programs.

3. 

   Fraud is Not Just Rarity: A Causal Prototype Attention Approach to Realistic Synthetic Oversampling

   Claudio Giusti, Luca Guarnera, Mirko Casu, and 1 more author

   2025

   Abs arXiv

   Detecting fraudulent credit card transactions remains a significant challenge, due to the extreme class imbalance in real-world data and the often subtle patterns that separate fraud from legitimate activity. Existing research commonly attempts to address this by generating synthetic samples for the minority class using approaches such as GANs, VAEs, or hybrid generative models. However, these techniques, particularly when applied only to minority-class data, tend to result in overconfident classifiers and poor latent cluster separation, ultimately limiting real-world detection performance. In this study, we propose the Causal Prototype Attention Classifier (CPAC), an interpretable architecture that promotes class-aware clustering and improved latent space structure through prototype-based attention mechanisms and we will couple it with the encoder in a VAE-GAN allowing it to offer a better cluster separation moving beyond post-hoc sample augmentation. We compared CPAC-augmented models to traditional oversamplers, such as SMOTE, as well as to state-of-the-art generative models, both with and without CPAC-based latent classifiers. Our results show that classifier-guided latent shaping with CPAC delivers superior performance, achieving an F1-score of 93.14% percent and recall of 90.18%, along with improved latent cluster separation. Further ablation studies and visualizations provide deeper insight into the benefits and limitations of classifier-driven representation learning for fraud detection. The codebase for this work will be available at final submission.

4. 

   A (Mid)journey Through Reality: Assessing Accuracy, Impostor Bias, and Automation Bias in Human Detection of AI-Generated Images

   Mirko Casu, Luca Guarnera, Ignazio Zangara, and 2 more authors

   Human Behavior and Emerging Technologies, 2025

   Abs DOI Code

   While the challenge of distinguishing AI-generated from real images is widely acknowledged, the specific cognitive biases that systematically shape human judgment in this domain remain poorly understood. It is particularly unclear how a general awareness of AI capabilities fosters novel biases, like a pervasive skepticism (“impostor bias”), and how this interacts with established phenomena like “automation bias”. This study addresses this gap by providing the first quantitative analysis of how these two biases operate across five distinct experimental variants designed to test the context-dependency of human perception. Through a mixed-methods study with 746 participants, we demonstrate that human authentication accuracy hovered around chance levels (ranging from 47.0% to 55.5%). However, our analysis provides robust evidence for the systematic operation of cognitive biases. We validate the presence of “impostor bias” through a consistent pattern of higher doubt for AI-generated images and confirm “automation bias” through significant opinion changes following algorithmic suggestions. Our findings reveal that these biases are not uniform across populations: gender was a consistent predictor of automation bias, with males in all five variants showing a significantly stronger and more consistent tendency (Cohen’s d = 0.254–0.683) to be influenced by algorithmic suggestions. In contrast, age and academic background had minimal and highly localized effects. Furthermore, we identified a significant interaction between experimental stimuli and performance over time, isolating a pronounced fatigue effect to a single questionnaire variant where accuracy progressively declined (by approximately 1.7% per trial). By integrating human feedback with Grad-CAM visualizations, we confirm a divergence between human holistic evaluation and the localized focus of machine learning models. These findings carry direct implications for policy, as discussed within the context of the European AI Act, and inform the design of human–AI systems and media literacy programs aimed at mitigating these critical cognitive vulnerabilities.

5. 

   Deepfake Forensic Analysis: Source Dataset Attribution and Legal Implications of Synthetic Media Manipulation

   Massimiliano Cassia, Luca Guarnera, Mirko Casu, and 2 more authors

   In MetroXRAINE, 2025

   Abs DOI

   Synthetic media generated by Generative Adversarial Networks (GANs) pose significant challenges in verifying authenticity and tracing dataset origins, raising critical concerns in copyright enforcement, privacy protection, and legal compliance. This paper introduces a novel forensic framework for identifying the training dataset (e.g., CelebA or FFHQ) of GAN-generated images through interpretable feature analysis. By integrating spectral transforms (Fourier/DCT), color distribution metrics, and local feature descriptors (SIFT), our pipeline extracts discriminative statistical signatures embedded in synthetic outputs. Supervised classifiers (Random Forest, SVM, XGBoost) achieve 98-99% accuracy in binary classification (real vs. synthetic) and multi-class dataset attribution across diverse GAN architectures (StyleGAN, AttGAN, GDWCT, StarGAN, and StyleGAN2). Experimental results highlight the dominance of frequency-domain features (DCT/FFT) in capturing dataset-specific artifacts, such as upsampling patterns and spectral irregularities, while color histograms reveal implicit regularization strategies in GAN training. We further examine legal and ethical implications, showing how dataset attribution can address copyright infringement, unauthorized use of personal data, and regulatory compliance under frameworks like GDPR and California’s AB 602. Our framework advances accountability and governance in generative modeling, with applications in digital forensics, content moderation, and intellectual property litigation.

6. 

   (DFF ’25) 1st Deepfake Forensics Workshop: Detection, Attribution, Recognition, and Adversarial Challenges in the Era of AI-Generated Media

   Sebastiano Battiato, Mirko Casu, Francesco Guarnera, and 5 more authors

   In ACM Multimedia, 2025

   Abs DOI

   The proliferation of generative models, particularly Generative Adversarial Networks (GANs) and Diffusion Models, has reshaped multimedia content creation. Alongside creative and commercial opportunities, they have introduced unprecedented risks through the production of highly realistic synthetic content, or deepfakes. These artifacts challenge visual and auditory trust, with major implications for media, security, politics, and law. This workshop provides a forum to examine deepfake technology from forensic, technical, legal, and social perspectives. It will bring together experts to advance robust and explainable detection methods, define benchmarking practices, and address ethical and regulatory frameworks. Topics include detection and attribution, adversarial countermeasures, multimodal analysis, model traceability, legal admissibility of synthetic content, as well as real-world deployment challenges and dataset creation. Further information about the workshop is available at https://iplab.dmi.unict.it/mfs/acm-dff-ws-2025/

7. 

   Adversarial Attacks on Deepfake Detectors: A Challenge in the Era of AI-Generated Media (AADD-2025)

   Sebastiano Battiato, Mirko Casu, Francesco Guarnera, and 5 more authors

   In ACM Multimedia, 2025

   Abs DOI Code

   The rapid proliferation of AI-generated media, particularly hyper-realistic deepfakes, has underscored the critical need for robust detection systems to mitigate risks such as misinformation and identity theft. However, state-of-the-art deepfake detectors remain vulnerable to adversarial attacks-subtle perturbations designed to evade classification. To address this gap, we organized the Adversarial Attacks on Deepfake Detectors (AADD-2025) challenge, a competitive evaluation aimed at advancing methodologies to expose and strengthen weaknesses in deepfake detection models. The challenge tasked participants with generating adversarial examples capable of evading four diverse classifiers (including ResNet, DenseNet, and two blind models) while preserving structural similarity to original deepfakes. A dataset comprising 16 subsets of high- and low-quality deepfake images generated by GAN-based and diffusion models (e.g., StableDiffusion, StyleGAN3) was provided. Participants were evaluated using a weighted combination of Structural Similarity Index (SSIM) and attack success rates across all classifiers. Thirteen teams proposed innovative solutions leveraging techniques such as latent-space manipulation, ensemble gradient optimization, surrogate modeling, and frequency-domain perturbation. Top-performing approaches, including MR-CAS (1st place), Safe AI (2nd place), and RoMa (3rd place), achieved high SSIM scores (0.74-0.93) while successfully misleading classifiers. Notably, MR-CAS’s latent diffusion model inversion strategy and Safe AI’s consensus-orthogonal gradient weighting framework demonstrated superior transferability across architectures, including Vision Transformers. The challenge revealed critical insights: latent-space attacks outperformed pixel-level methods, ensemble-based strategies enhanced cross-model robustness, and adversarial perturbations optimized for both CNNs and transformers proved most effective. However, gaps persist in generalizing attacks across heterogeneous models and maintaining perceptual fidelity, highlighting the urgency of developing adaptive defenses and hybrid detection mechanisms. By fostering collaboration and innovation, AADD-2025 provides a benchmark for evaluating adversarial robustness in deepfake detection and underscores the need for resilient systems in the era of AI-generated media.

8. 

   WILD: a new in-the-Wild Image Linkage Dataset for synthetic image attribution

   Pietro Bongini, Sara Mandelli, Andrea Montibeller, and 14 more authors

   In IJCNN, 2025

   Abs DOI Code

   Synthetic image source attribution is an open challenge, with an increasing number of image generators being released yearly. The complexity and the sheer number of available generative techniques, as well as the scarcity of high-quality open source datasets of diverse nature for this task, make training and benchmarking synthetic image source attribution models very challenging. WILD is a new in-the-Wild Image Linkage Dataset designed to provide a powerful training and benchmarking tool for synthetic image attribution models. The dataset is built out of a closed set of 10 popular commercial generators, which constitutes the training base of attribution models, and an open set of 10 additional generators, simulating a real-world in-the-wild scenario. Each generator is represented by 1,000 images, for a total of 10,000 images in the closed set and 10,000 images in the open set. Half of the images are post-processed with a wide range of operators. WILD allows benchmarking attribution models in a wide range of tasks, including closed and open set identification and verification, and robust attribution with respect to post-processing and adversarial attacks. Models trained on WILD are expected to benefit from the challenging scenario represented by the dataset itself. Moreover, an assessment of seven baseline methodologies on closed and open set attribution is presented, including robustness tests with respect to post-processing.

2024

1. 

   Breaking the Silence: Exploring Peritraumatic Distress and Negative Emotions in Male and Female Physical Domestic Violence Victims

   Pasquale Caponnetto, Vittorio Lenzo, Alberto Sardella, and 3 more authors

   Health Psychology Research, 2024

   Abs DOI

   Background. Domestic violence is a widespread problem affecting individuals, families, and communities worldwide. Peritraumatic distress is associated with post-traumatic stress disorder and other mental health problems among victims, but research on men’s experiences is limited. Objective. We analyzed data from 48 physical domestic violence victims (16 males and 32 females) to compare their levels of peritraumatic distress and negative emotions, and to examine the types of aggressors they faced. Methods. We used descriptives to summarize sample characteristics and Peritraumatic Distress Inventory scores and used statistical tests such as Mann-Whitney U, Shapiro-Wilk, Levene’s test, contingency tables, and chi-square to investigate differences and associations between variables. Results. Female victims of domestic violence had significantly higher scores on the Peritraumatic Distress Inventory than male victims and experienced significantly higher levels of negative emotions than male victims, including impotence/inability to react, sadness, anger/frustration, loss of control, fear, guilt, and shame. Husbands and domestic partners were the most frequent aggressors against female victims, while wives and ex-wives were the most frequent aggressors against male victims. In addition, partners were found to be the most frequent type of aggressor in the sample. Descriptive statistics, box plots, and scatter plots were used to provide a clear picture of the sample characteristics. Conclusion. Female victims of domestic violence reported higher levels of peritraumatic distress and negative emotions compared to men victims. Partners were the most frequent type of aggressor in the sample.

2. 

   Breathing tech: digital health innovations for managing asthma-related psychological dimensions

   Mirko Casu, and Pasquale Caponnetto

   Exploration of Digital Health Technologies, 2024

   Abs DOI

   The paper aimed to provide a comprehensive overview of the use of digital health technologies in the assessment, treatment, and self-management of psychological and psychopathological factors associated with asthma. A collection of research articles and systematic reviews related to asthma, including topics such as outdoor air pollution, early life wheezing illnesses, atopic dermatitis, digital interventions for asthma self-management, psychiatric disorders and asthma, family influences on pediatric asthma, and the use of mobile health (mHealth) applications for asthma management, were analyzed. Eight selected studies were reviewed to assess the potential of digital health technologies in improving asthma psychological-related factors management and treatment outcomes. The reviewed studies suggest that electronic health (eHealth) interventions, mixed reality tools, mHealth technology-enhanced nurse-guided interventions, and smartphone applications integrating Bluetooth-enabled sensors for asthma inhalers can significantly improve symptom self-management, quality of life, and mental health outcomes, especially in children and adolescents with asthma. However, further research is needed to determine their effectiveness and feasibility in different populations and settings. Tailored interventions that address the specific needs and preferences of patients with asthma and associated psychological factors are crucial for ensuring sustained and equitable use of these technologies. The manuscript emphasizes the importance of addressing psychological factors in the management and treatment of asthma and call for continued research and development in this area.

3. 

   A Multi-Level Analysis of Biological, Social, and Psychological Determinants of Substance Use Disorder and Co-Occurring Mental Health Outcomes

   Cecilia Ilaria Belfiore, Valeria Galofaro, Deborah Cotroneo, and 4 more authors

   Psychoactives, 2024

   Abs DOI

   We explored the intricate interplay of biological, social, and psychological factors contributing to substance use disorder (SUD) and co-occurring psychiatric symptoms. Drug misuse is a global concern, with increasing prevalence rates affecting mental well-being and safety. The spectrum of SUD includes polysubstance users, posing challenges for treatment and associated health outcomes. Various psychoactive substances like cannabis, hallucinogens, opioids, and stimulants impact addiction vulnerability, with marijuana being widely used globally. Gender differences in SUD prevalence have narrowed, with women escalating drug consumption rapidly once initiated. Age disparities in substance use highlight regional variations among adolescents. Comorbidities with psychiatric symptoms are common, with mood and anxiety disorders frequently observed. This study aimed to analyze factors influencing SUD development and maintenance to inform prevention strategies and treatment recommendations. By conducting a systematic search of databases, sixty articles were reviewed, revealing diverse methodologies and geographic locations. Biological factors, including neurotransmitter systems like endocannabinoid and dopaminergic systems, play a significant role in addiction. Genetic and neurobiological factors contribute to cannabis addiction susceptibility. Social factors such as childhood experiences and parenting styles influence substance use behaviors. Psychological factors like personality traits and mental health conditions interact with SUD development. Understanding these multifaceted interactions is crucial for designing effective interventions to address the complexities of SUD and co-occurring psychiatric symptoms.

4. 

   Psychological Factors, Digital Health Technologies, and Best Asthma Management as Three Fundamental Components in Modern Care: A Narrative Review

   Pasquale Caponnetto, Graziella Chiara Prezzavento, Mirko Casu, and 2 more authors

   Applied Sciences, 2024

   Abs DOI

   New digital interventions have shown potential in managing asthma and improving patients’ quality of life compared with conventional interventions. Our objective was to conduct an exhaustive survey of the application of digital health technologies in evaluating, treating, and self-managing psychological and psychopathological elements linked to asthma. We analyzed a compendium of research papers pertaining to asthma, encompassing themes such as outdoor air pollution, early life wheezing disorders, atopic dermatitis, digital strategies for asthma self-management, psychiatric conditions and asthma, familial impacts on pediatric asthma, and the utilization of mobile health apps for managing asthma. We scrutinized six chosen studies to evaluate the capacity of digital health technologies to enhance the management and treatment outcomes of psychological factors related to asthma. The studies under review indicate that eHealth interventions, mixed reality instruments, mHealth technology-augmented nurse-led interventions, and smartphone apps incorporating Bluetooth-enabled sensors for asthma inhalers can markedly enhance self-management of symptoms, quality of life, and mental health outcomes, particularly in children and adolescents with asthma. Nonetheless, additional research is required to ascertain their efficacy and practicability across diverse populations and environments. Customized interventions that cater to the unique needs and preferences of asthma patients and associated psychological elements are vital to ensure the persistent and equitable adoption of these technologies. We underscore the significance of addressing psychological elements in the management and treatment of asthma and advocate for ongoing research and development in this domain.

5. 

   Behavioral Therapy for People With Diabetes Who Smoke: A Scoping Review

   Roberta Sammut, Joseph Grech, Riccardo Polosa, and 14 more authors

   Journal of Primary Care & Community Health, 2024

   Abs DOI

   Background: Tobacco smoking exacerbates diabetes-related complications; its prevalence is notwithstanding substantial. Persons with diabetes face a number of barriers and challenges to quitting such as multiple lifestyle restrictions; tailored interventions are required for smoking cessation.Objective:To identify research on behavioral interventions for smoking cessation in diabetes.Methods:Studies had to be randomized controlled trials, quasiexperimental or systematic reviews. The behavioral interventions included were: the 5As, Cognitive-Behavioral Therapy, Motivational Interviewing, Contingency Management, Health Coaching and Counselling, as compared to standard care. The outcomes were self-reported and/or biochemically verified smoking cessation. CINAHL Complete, MEDLINE Complete, the Cochrane databases of systematic reviews and randomized controlled trials, PsychInfo and PubMed Central were searched until July, 2023. Keywords used included diabetes, smoking cessation and each of the behavioral interventions included.Results:1615 papers were identified. Three studies on the 5As/brief advice, 4 on Motivational Intervention and 1 on counseling were retained. The results on the 5As and Motivational Interviewing were conflicting. More intensive interventions appear to be more successful in achieving smoking cessation in smokers with diabetes.Conclusions:Future research should focus on the continued development and evaluation of structured smoking cessation interventions based on the 5As, Motivational interviewing and Cognitive Behavioral Therapy.

6. 

   Exploring the Therapeutic Potential of Virtual Reality: A Review on the Simulation of Psychedelic Effects for Treating Psychological Disorders

   Mirko Casu, Chiara Farrauto, Giorgia Farruggio, and 3 more authors

   Psychology International, 2024

   Abs DOI

   Psychedelic therapy is increasingly acknowledged as a transformative approach to mental health care, much like how Virtual Reality (VR) technology has emerged as a potent tool in the realm of mental health. Hence, there is potential for integrating the benefits derived from both. This review aims to assess the current state of the art concerning the utilization of VR and psychedelic simulations for treating psychological disorders. The findings clarify the potential of an emerging treatment: the simulation of psychedelic states through Virtual Reality. This treatment has been shown to improve cognitive flexibility and executive functions and, as a result, could be used to prevent conditions such as mild cognitive impairment and dementia. Furthermore, this treatment facilitates the activation of other constructs in the subject, such as creativity, joy, pleasure, and relaxation, which can act as mediators in the treatment of various psychopathological disorders. This review attempts to broaden knowledge regarding the simulation of psychedelic states through Virtual Reality, exposing the results in a clinical setting and highlighting the need for further studies.

7. 

   Night Eating Syndrome: A Review of Etiology, Assessment, and Suggestions for Clinical Treatment

   Miria Bargagna, and Mirko Casu

   Psychiatry International, 2024

   Abs DOI

   Night Eating Syndrome (NES) is a distinct eating disorder characterized by recurrent episodes of night eating, either through excessive food consumption after the evening meal or eating after awakening from sleep. Despite its recognition, there remains a dearth of research on NES, limiting our understanding of its etiology, prevalence, diagnosis, and treatment. This paper conducts a narrative review spanning a wide spectrum of studies focusing on the etiology, assessment, and clinical treatment strategies of NES. This review traces the historical evolution of NES definitions, distinguishes NES from obesity-related eating behaviors and other eating disorders, explores the psychological determinants of NES, and discusses existing therapeutic options. This review highlights the complex etiology of NES, influenced by circadian rhythms, hormonal changes, psychological distress, and personality traits. It emphasizes the need for reliable assessment tools and a holistic approach to treatment, considering the high comorbidity of NES with other psychiatric and medical conditions. Current treatment options, such as cognitive behavioral therapy and pharmacotherapy, show promise but require further research for refinement. NES remains underdiagnosed and undertreated, with challenges including unclear diagnostic criteria, comorbidities, and lack of evidence-based treatments. Future research should focus on developing reliable assessment tools, exploring etiology, comparing treatment approaches, and considering prevention strategies, utilizing diverse and representative samples to advance our understanding and improve clinical care.

8. 

   GenAI mirage: The impostor bias and the deepfake detection challenge in the era of artificial illusions

   Mirko Casu, Luca Guarnera, Pasquale Caponnetto, and 1 more author

   Forensic Science International: Digital Investigation, 2024

   Abs DOI

   This paper examines the impact of cognitive biases on decision-making in forensics and digital forensics, exploring biases such as confirmation bias, anchoring bias, and hindsight bias. It assesses existing methods to mitigate biases and improve decision-making, introducing the novel “Impostor Bias”, which arises as a systematic tendency to question the authenticity of multimedia content, such as audio, images, and videos, often assuming they are generated by AI tools. This bias goes beyond evaluators’ knowledge levels, as it can lead to erroneous judgments and false accusations, undermining the reliability and credibility of forensic evidence. Impostor Bias stems from an a priori assumption rather than an objective content assessment, and its impact is expected to grow with the increasing realism of AI-generated multimedia products. The paper discusses the potential causes and consequences of Impostor Bias, suggesting strategies for prevention and counteraction. By addressing these topics, this paper aims to provide valuable insights, enhance the objectivity and validity of forensic investigations, and offer recommendations for future research and practical applications to ensure the integrity and reliability of forensic practices.

9. 

   AI Chatbots for Mental Health: A Scoping Review of Effectiveness, Feasibility, and Applications

   Mirko Casu, Sergio Triscari, Sebastiano Battiato, and 2 more authors

   Applied Sciences, 2024

   🌟 Cited in the UK Parliament POST

   Abs DOI

   Mental health disorders are a leading cause of disability worldwide, and there is a global shortage of mental health professionals. AI chatbots have emerged as a potential solution, offering accessible and scalable mental health interventions. This study aimed to conduct a scoping review to evaluate the effectiveness and feasibility of AI chatbots in treating mental health conditions. A literature search was conducted across multiple databases, including MEDLINE, Scopus, and PsycNet, as well as using AI-powered tools like Microsoft Copilot and Consensus. Relevant studies on AI chatbot interventions for mental health were selected based on predefined inclusion and exclusion criteria. Data extraction and quality assessment were performed independently by multiple reviewers. The search yielded 15 eligible studies covering various application areas, such as mental health support during COVID-19, interventions for specific conditions (e.g., depression, anxiety, substance use disorders), preventive care, health promotion, and usability assessments. AI chatbots demonstrated potential benefits in improving mental and emotional well-being, addressing specific mental health conditions, and facilitating behavior change. However, challenges related to usability, engagement, and integration with existing healthcare systems were identified. AI chatbots hold promise for mental health interventions, but widespread adoption hinges on improving usability, engagement, and integration with healthcare systems. Enhancing personalization and context-specific adaptation is key. Future research should focus on large-scale trials, optimal human–AI integration, and addressing ethical and social implications.

10. 

    Virtual Reality Neurofeedback Training combined with Trauma-Informed Motivational Interviewing for the Treatment of Post-Traumatic Stress Disorder

    Sergio Triscari, Mirko Casu, Eleonora Uccelli, and 4 more authors

    Preprints, 2024

    DOI

11. 

    A Case Study on the Use of Hypnosis Combined with Virtual Reality in Psychological Partner Violence

    Mirko Casu, Salvatore Bellissima, Giorgia Farruggio, and 2 more authors

    In MetroXRAINE, 2024

    Abs DOI

    This case study investigated the application of hypnosis and Virtual Reality (VR) using the Meta Quest headset, aimed at alleviating psychological partner violence mental health consequences. The subject of the study was a 56-year-old woman, originally from Mauritius, who has been residing in Italy for the past 33 years. The study employed a series of hypnosis and VR sessions, with the subject demonstrating a high susceptibility to hypnotic induction and a positive response to VR. Despite occasional distractions, she maintained a state of relaxation throughout the sessions. The VR applications used were “Nature Trecks”, “Liminal”, and “Cosmic Flow”. The use of the Meta Quest headset provided an immersive and realistic experience for the patient, showcasing the potential of health informatics in therapeutic contexts. However, the study found that the subject’s condition did not significantly improve. She continues to grapple with anxiety, disappointment, and sleep disorders, and her family situation remains challenging. Despite these hurdles, the subject consistently expressed a strong desire to use VR, which provided her with moments of tranquility. This suggests that hypnosis and VR could offer some degree of emotional stress relief, even if they cannot resolve the underlying issues. In conclusion, this case study offers valuable insights into the potential of hypnosis and VR as therapeutic tools for domestic discomfort and mental health issues. It underscores the need for further research to fully explore the long-term potential of these approaches.

2023

1. 

   Rolling the Dice: A Comprehensive Review of the New Forms of Gambling and Psychological Clinical Recommendations

   Mirko Casu, Cecilia Ilaria Belfiore, and Pasquale Caponnetto

   Psychiatry International, 2023

   Abs DOI

   This study aims to analyze the main and most recent forms of gambling and related psychopathological disorders, also proposing psychological clinical recommendations. From November 2022 to February 2023, we searched the databases of PubMed, Web of Science, Health & Medical Collection, Elsevier Journal, and Springer for relevant studies performing different searches through different search strings. New forms of gambling are mostly related to new technological tools, such as the Internet, smartphones, social media, or electronic machines. The prevalence of online gambling affects all demographic groups, although 35–44-year-olds appear to have the largest share. Online gambling can lead to addiction, financial hardship, and mental health problems. It has also been statistically significantly associated with high levels of Gambling disorder, high levels of depression and anxiety, poor overall mental health, and alcohol use. Furthermore, it has been noted that online gamblers are more likely to engage in high-risk gambling behaviors and have a higher prevalence of comorbid mental disorders. The review highlights the need for continued research on the impact of new forms of gambling and the development of effective prevention and treatment strategies. Further research is needed to better understand the complex relationship between new forms of gambling and the development of gambling disorders.

2. 

   User evaluation and feasibility test of an app designed for smoking cessation in Italian people who smoke: preliminary findings from an uncontrolled pre-test post-test open study

   Pasquale Caponnetto, Mirko Casu, David Crane, and 3 more authors

   BMC Psychology, 2023

   Abs DOI

   Background: mHealth is a public health practice that exploits the use of mobile devices, including smartphone applications. We will describe an uncontrolled pre-test post-test open pilot study concerning the feasibility evaluation of a smartphone App designed to help in smoking cessation. The aim of this study is to evaluate the feasibility of a smartphone app as a tool for smoking cessation. This study is necessary to the literature because smoking is a major public health concern and has been linked to various health issues such as cardiovascular disease, respiratory disease, and cancer. While there are several smoking cessation interventions available, the use of mobile devices to aid in smoking cessation is a relatively new and innovative approach that requires further investigation. Methods: The App “Smoke Free” was configured on the devices of N = 30 participants who smoked combustible cigarette, 13 males and 17 females aged 18 to 55 years, with the indications to use it for 90 days, describe their experience, suggest new features, and report any critical aspect. The study consisted of an initial screening visit to select participants that reflected the inclusion criteria and 4 study visits: a baseline visit, two follow-up visits, and one final visit. We used descriptive stats to summarize results. Repeated measures ANOVA and Wilcoxon test were used to test differences in smoking consumption, self-reported craving, and measured eCO level. Statistical software Jamovi was used for analysis. Interviews were conducted via phone or in-person and analyzed using qualitative description principles. Results: Participants evaluated the app as having good aesthetic appeal and user-friendliness but being moderately useful, despite some quitting or reducing their smoking behavior. To improve it, participants have proposed features such as more notifications, social network integration, and damage caused by smoking to the body over time for future app updates. Conclusions: The application was moderately useful with good feasibility, with several suggestions for future updates that could improve its effectiveness.

2022

1. 

   Update on cyber health psychology: Virtual reality and mobile health tools in psychotherapy, clinical rehabilitation, and addiction treatment

   Pasquale Caponnetto, and Mirko Casu

   International Journal of Environmental Research and Public Health, 2022

   Abs DOI

   (1) Background: we investigated and analyzed the most recent implementations of technology in the fields of psychotherapy, clinical rehabilitation, and addiction treatment. (2) Methods: from December 2021 to January 2022, we conducted a review aimed at identifying the recent implementations of technology in cyber health psychology, with particular reference to Virtual Reality in psychotherapy, mHealth tools in clinical rehabilitation, and smartphone applications in the treatment of addiction to substances of abuse, searching for relevant studies in the databases PubMed, Web of Science, Google Scholar, Health & Medical Collection, and APA PsycArticles. (3) Results: the tools analyzed are in constant development and are increasingly used, with good results, and further technological progress could lead to even better treatment outcomes; as far as mHealth tools and smartphone applications are concerned, anti-smoking Apps are the most widespread, followed by those for the treatment of alcohol use disorder, and there is no presence of Apps for the treatment of heroin, cocaine, or crack addiction. (4) Conclusions: the results of the review indicate that these technological tools are increasingly used and are, in principle, effective and have numerous advantages, including low cost and versatility.

2. 

   The “Slotstreams” Phenomenon on Twitch. Tv: Can it Lead to Online Gambling?

   Mirko Casu, and Cecilia Ilaria Belfiore

   2022

   Abs DOI

   (1) Background: Twitch. tv is a live video content website. As of 2022, Twitch users are generally adolescents and young adults, with estimates of the percentage of users aged 16-24 between 22.3% and 41%, predominantly males with estimates ranging from 65% to 78.36%. In recent years,“slotstream” content has become increasingly popular, where streamers gamble online while users watch them.(2) Methods: from July 2022 to November 2022, we researched articles related to Twitch, live streaming, gambling, casino, slot, gambling online, modeling, social media influencers, conditioning, and celebrities, searching for relevant studies in the databases LexisNexis Academic, Business Source Complete, PubMed, Web of Science, Freedom Collection, Health & Medical Collection, Elsevier Journal, Springer, APA PsycARTICLES, Wiley, and other single journals.(3) Results: we took into consideration Gambling Disorder, Online Gambling Disorder, and Internet Gaming Disorder on the one hand, and consumer components such as persuasive communication and influencer-audience relationship on the other; we then hypothesized that the presence of these variables within the slotstream format is positively correlated with the development of pathological gambling, especially in an adolescent audience.(4) Conclusion: we assume that there is a positive correlation between watching slotstream content and the probability of developing both pathological and non-pathological gambling behavior.

3. 

   New Perspectives in Psychopathology and Psychological Well-being by Using Forest Therapy: A Systematic Review

   Pasquale Caponnetto, Lucio Inguscio, Sergio Triscari, and 4 more authors

   The Open Psychology Journal, 2022

   Abs DOI

   Background: Forest therapy is the clinical application of Shinrin-Yoku, also known as forest bathing. Specifically, the term Shinrin-Yoku refers to what is known as “a bath in the air of the forest”, which is carried out by walking and observing the landscapes of nature. In recent years, ecopsychology has become increasingly interested in the use of forest therapy as a credible, eco-sustainable, and easily adaptable treatment. The idea is to borrow the typical techniques of mindfulness and apply them to environmental contexts to verify if this leads to a better physical and mental well-being of the individual. Given the few contexts in which forest therapy is applied, this article aims to investigate, through a review of available literature, the applications of forest therapy existing in various severe or mild disorders in order to understand if this technique can represent a credible alternative to traditional treatments, and consequently arouse the interest of future experimental research in different contexts. Methods: A systematic review was conducted using the PRISMA 2020 guidelines that allowed us to identify n = 7 unique papers to be included in our analysis. Results: Forest therapy has been shown to be an effective treatment for improving a range of psychological and physiological parameters in college students, the elderly, women with postmenopausal insomnia, patients with alcoholism syndrome, patients with post-traumatic stress disorder (PTSD), patients with chronic widespread pain, and psychiatric patients. Conclusion: The analyzed studies are not yet able to tell us if forest therapy is better than traditional treatments. However, the effectiveness demonstrated in the studies provides empirical evidence of the credibility of forest therapy and leads to new scenarios for more eco-sustainable, versatile, affordable, and adaptable therapies according to the needs of the patients.

2021

1. 

   The effects of physical exercise on mental health: From cognitive improvements to risk of addiction

   Pasquale Caponnetto, Mirko Casu, Miriam Amato, and 8 more authors

   International Journal of Environmental Research and Public Health, 2021

   Abs DOI

   (1) Background: we aimed to investigate the effects of physical activity on cognitive functions and deficits of healthy population and other needy groups. Secondly, we investigated the relation between healthy habits and psychopathological risks. Finally, we investigated the impact of COVID-19 pandemic on exercise addiction and possible associated disorders. (2) Methods: From April 2021 to October 2021, we conducted a review aimed at identifying the effects of physical exercise on mental health, from cognitive improvements to risk of addiction; we searched for relevant studies on PubMed, Web of Science, EMBASE, PsycINFO and CINHAL. (3) Results: For the first purpose, results indicated multiple effects such as better precision and response speed in information processing tasks on healthy populations; improvement of executive functions, cognitive flexibility and school performance in children; improvement of attention and executive functions and less hyperactivity and impulsiveness on children with attention deficit hyperactivity disorder (ADHD); improvement of executive and global functions on adults; improvement of overall cognitive functioning on patients with schizophrenic spectrum disorder or bipolar disorder. Data also demonstrated that exercise addiction seems to be related to low levels of education, low self-esteem, eating disorders and body dysmorphisms. Eventually, it was found that people with lower traits and intolerance of uncertainty show a strong association between COVID-19 anxiety and compulsive exercise and eating disorder. (4) Conclusions: these findings underline on one side the beneficial effects of physical activity on cognitive function in healthy individuals in a preventive and curative key, while on the other side the importance of an adequate evaluation of psychological distress and personality characteristics associated with exercise addiction.