How do AI voice clones and deepfakes power 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 18, 2026

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

2. 2

   Unmasking digital deceptions: An integrative review of deepfake detection, multimedia forensics, and cybersecurity challenges.

   MethodsX (Singh & Dhumane)Published Sep 18, 2025Checked Oct 18, 2026

   “Deepfakes, which are driven by developments in generative AI, seriously jeopardize public trust, cybersecurity, and the veracity of information. This study offers a comprehensive analysis of the most recent methods for creating and detecting deepfakes in image, video, and audio modalities. With a focus on their advantages and disadvantages in cross-dataset and real-world scenarios, we compile the latest developments in transformer-based detection models, multimodal biometric defenses, and Generative Adversarial Networks (GANs). We provide implementation-level information such as pseudocode workflows, hyperparameter settings, and preprocessing pipelines for popular detection frameworks to improve reproducibility. We also examine the implications of cybersecurity, including identity theft and biometric spoofing, as well as policy-oriented solutions that incorporate federated learning, explainable AI, and ethical protections. By enriching technical insights with interdisciplinary perspectives, this review charts a roadmap for building robust, scalable, and trustworthy deepfake detection systems.”

3. 3

   Deepfake Media Forensics: Status and Future Challenges.

   Journal of imaging (Amerini et al.)Published Feb 28, 2025Checked Oct 18, 2026

   “Using advanced frameworks such as Generative Adversarial Networks (GANs) and Diffusion Models (DMs), deepfakes are capable of producing highly realistic yet fabricated content, while these advancements enable creative and innovative applications, they also pose severe ethical, social, and security risks due to their potential misuse. The proliferation of deepfakes has triggered phenomena like "Impostor Bias", a growing skepticism toward the authenticity of multimedia content, further complicating trust in digital interactions. This paper is mainly based on the description of a research project called FF4ALL (FF4ALL-Detection of Deep Fake Media and Life-Long Media Authentication) for the detection and authentication of deepfakes, focusing on areas such as forensic attribution, passive and active authentication, and detection in real-world scenarios. By exploring both the strengths and limitations of current methodologies, we highlight critical research gaps and propose directions for future advancements to ensure media integrity and trustworthiness in an era increasingly dominated by synthetic media.”

4. 4

   Love as Bait: A Scoping Review and Crime Script Analysis of Online Romance Scams.

   Trauma, violence & abuse (Schokkenbroek & Snaphaan)Published Sep 9, 2025Checked Oct 18, 2026

   “In total, 50 empirical studies were included based on their descriptions of scammer behaviors. Data were analyzed by coding relevant passages on scammer actions and process models, which were then categorized into scenes and actions to construct a crime script. The resulting script identifies nine major scenes in the scam process: (1) preparation (the Setup); (2) target selection (the Hunt); (3) initial contact (the Hook);(4) transition to private communication (the Shift); (5) grooming; (6) the Sting; (7) financial transaction (the Payout); (8) the Squeeze (e.g., sextortion); and (9) the Aftermath (e.g., revictimization). Each scene includes multiple possible actions and variations, demonstrating the flexibility and adaptability of scammers. The review underscores gaps in previous process models by highlighting non-linearity, scammer adaptability, and revictimization in the online romance scam process. This study contributes to both theory and practice by offering a detailed framework for understanding and reducing (the harm following) online romance scams.”

5. 5

   Deepfake (Wikipedia)

   WikipediaPublished Oct 18, 2026Checked Oct 18, 2026

   “Deepfakes (a portmanteau of 'deep learning' and 'fake') are images, videos, or audio that have been edited or generated using artificial intelligence, AI-based tools or audio-video editing software. They may depict real or fictional people and are considered a form of synthetic media, that is media that is usually created by artificial intelligence systems by combining various media elements into a new media artifact. While the act of creating fake content is not new, deepfakes uniquely leverage machine learning and artificial intelligence techniques, including facial recognition algorithms and artificial neural networks such as variational autoencoders and generative adversarial networks (GANs). In turn, the field of image forensics has worked to develop techniques to detect manipulated images. Deepfakes have garnered widespread attention for their potential use in creating child sexual abuse material, celebrity pornographic videos, revenge porn, fake news, hoaxes, bullying, and financial fraud. Academics have raised concerns about the potential for deepfakes to promote disinformation and hate speech, as well as interfere with elections.”