How does AI supercharge scams and how can people protect themselves?

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1. 1

   Don't fall for this - how to protect yourself from AI scams

   BBC NewsPublished Oct 18, 2026Checked Oct 19, 2026

   “Trading standards officers say AI is "super-charging" existing scams through clones and fake websites.”

2. 2

   Framing digital inauthenticity: Comparing user detection of AI-generated faces to messaged-based scam methods.

   Acta psychologica (Sarno et al.)Published Dec 4, 2025Checked Oct 19, 2026

   “For the message-based tasks, participants classified real and phishing emails/text messages. For the image-based task, participants classified real and AI-generated faces. Participants' cognitive reasoning styles, digital and risk literacy, and demographics were also assessed. Our findings suggest that users may be less likely to detect image-based digital inauthenticity, like AI-generated faces, compared to message-based scams, like phishing attacks. Additionally, our results indicate that while participants made poorer and riskier classifications with the faces, user characteristics such as risk literacy, cognitive reflection, and gender may be linked to the successful identification of image-based inauthenticity. Shared vulnerability across methods may depend on similarities in content, such as incorporating images/text. As online actors leverage genAI-tools to help develop more elaborate methods of digital inauthenticity, users will likely continue to struggle to identify digital inauthenticity, emphasizing the need for better technical safeguards and user interventions.”

3. 3

   Evaluating spam filters and Stylometric Detection of AI-generated phishing emails

   Expert Systems with Applications (Opara et al.)Published Mar 10, 2025Checked Oct 19, 2026

   “As AI-generated phishing emails become increasingly sophisticated, a critical question arises: How effectively can current email systems and detection mechanisms identify these threats? This study addresses this issue by analysing 63 AI-generated phishing emails created using GPT-4o. It evaluates the effectiveness of major email services, Gmail, Outlook, and Yahoo, in filtering these malicious communications. The findings reveal that Gmail and Outlook allowed more AI-generated phishing emails to bypass their filters compared to Yahoo, highlighting vulnerabilities in existing email filtering systems. To mitigate these challenges, we applied 60 stylometric features across four machine learning models: Logistic Regression, Support Vector Machine, Random Forest, and XGBoost. Among these, XGBoost demonstrated superior performance, achieving 96% accuracy and an AUC score of 99%. Key features such as imperative verb count, clause density, and first-person pronoun usage were instrumental to the model’s success. The dataset of AI-generated phishing emails is publicly available on Kaggle to foster further research.”

4. 4

   Multilayered Epistemic Disruption in AI-Driven Health Misinformation: Conceptual Framework and Viewpoint.

   JMIR infodemiology (Malkoç)Published Aug 20, 2026Checked Oct 19, 2026

   “Perceptual studies document that listeners correctly identify AI-generated voice clones only about 60% of the time and perceive a cloned voice as identical to its real counterpart in approximately 80% of trials. Existing defenses-including Content Provenance and Authenticity standards, automated deepfake detection (showing area under the curve drops of up to 50% under real-world conditions), and prebunking interventions-are shown to address only subsets of the proposed cascade, leaving temporal and systemic layers substantially unmitigated. Four testable hypotheses are advanced for empirical validation. Addressing AI-driven health misinformation requires moving beyond individual-level i-frame interventions toward structural, s-frame policy responses calibrated to each layer of the MEDF cascade. Policymakers and platforms must implement source identity verification, clinician biometric protection protocols, cross-platform ecosystem governance, and proactive trust infrastructure, with particular urgency in lower- and middle-income country contexts where regulatory capacity and platform oversight are most limited.”

5. 5

   Deepfake Technologies in Financial Fraud: Generation, Detection, and Mitigation Strategies

   Research paper (Shetye et al.)Published May 9, 2025Checked Oct 19, 2026

   “We categorize deepfake generation methods, including voice synthesis, facial reenactment, and synthetic media creation, detailing the underlying algorithms and their potential exploitation in schemes like identity theft, phishing, and unauthorized transactions. In addition, we analyze various detection strategies that leverage machine learning, signal processing, and forensic analysis to identify deepfake content. The survey highlights the challenges faced in detecting increasingly sophisticated deepfakes, such as data scarcity, the evolving nature of fraud tactics, and the necessity for real-time detection capabilities. We also discuss regulatory and ethical implications, emphasizing the importance of developing robust frameworks for accountability and transparency. By presenting a holistic view of the current state of financial fraud related to deepfake technologies, this survey aims to inform stakeholders and stimulate further research in prevention and detection methodologies, ultimately contributing to a safer financial ecosystem.”

6. 6

   The use of IT-safety and coping measures against cybercrimes among older adults in Hong Kong: an application of cyber routine activity theory.

   Frontiers in sociology (Lam et al.)Published Jun 3, 2026Checked Oct 19, 2026

   “Twenty-three focus groups involving 142 adults aged 55 or older were conducted in Hong Kong between May and August 2023. Data was analyzed using deductive and inductive thematic analysis with NVivo 15.FindingsFindings indicate that impersonation of close relatives, information leakage, and "guess who I am" scams are the most common cybercrimes faced by older adults. Target suitability is shaped by daily smartphone routines and socio-emotional and relational expectations, while motivated offenders employ adaptive, low-cost tactics. Participants relied on both physical and social forms of guardianship, including peer support from individuals with higher digital literacy and the use of social knowledge to reduce scam risks.DiscussionThe study extends CRAT by highlighting the role of socio-emotional commitments rooted in Confucian collectivist culture and by conceptualizing multi-layered and distributed social guardianship. Policy implications at individual, family, and societal levels are discussed.”