Hear Us, then Protect Us: Navigating Deepfake Scams and Safeguard Interventions with Older Adults through Participatory Design

Research Background and Issues

• Issues or Challenges
  The authors point out that with the rapid development of artificial intelligence technologies, Deepfake technology has become a critical tool for generating realistic personal images and voices used in new forms of online fraud. Elderly individuals are particularly vulnerable to Deepfake scams, yet existing protective measures often lack designs tailored to their needs, treating them as passive recipients rather than active participants.

• Importance
  The elderly population is increasingly engaging with digital technologies, especially in the post-pandemic era, where they frequently use social media for communication and shopping. This makes them prime targets for fraudsters. When online scams incorporate Deepfake technology, the realistic forged content is especially likely to gain the trust of older adults. Therefore, designing targeted protective measures is crucial to reducing financial losses and psychological trauma for this demographic.

• Research Motivation and Related Work
  The authors examined technical defenses against Deepfake (e.g., machine detection) and educational approaches (e.g., improving digital literacy) but found these measures to be insufficient in addressing the specific needs of elderly individuals. Furthermore, as human-computer interaction (HCI) increasingly intersects with cybersecurity, user-centered designs that emphasize participation and address user actions and needs are becoming more critical. Based on this, the authors propose using participatory design to transform elderly individuals from passive recipients to active contributors of design information, better addressing the threats posed by Deepfake scams.

Solution

• Proposed Solution
  The authors employed a participatory design approach to create a series of interactive workshops focused on Deepfake scams and protective measures. They invited 10 elderly participants from China to analyze simulated Deepfake scam cases and evaluate provocative protective concepts.

• Innovative Points

  1. The authors utilized participatory design to make elderly individuals active participants rather than passive recipients, respecting their role as autonomous decision-makers.
  2. They adopted provocative design concepts to stimulate critical thinking about current protective measures among the elderly.
  3. Recognizing the diversity of elderly individuals, they explored their complex psychological dynamics regarding scam recognition through case analysis and interactive exploration.

• Implementation Steps and Key Techniques

  1. Case Analysis: Simulated Deepfake scam cases were constructed, involving various roles (family members, celebrities, etc.) and scam types (real-time and pre-recorded). Short videos guided participants in discussing key details and vulnerabilities of these cases.
  2. Evaluation of Provocative Concepts: Multiple protective device and measure concepts (e.g., Deepfake detection glasses, fingerprint devices) were designed, and participants were invited to evaluate and improve them.
  3. Workshop Process: Conducted in phases, including guiding elderly participants to share their experiences and feedback, while collecting data through interviews and observations.
  4. Integration of Technology and Participation: Combining artificial intelligence and user experience design, the workshops explored ways to enhance digital literacy among the elderly, enabling them to protect themselves more effectively.

Research Outcomes

• Specific Outcomes
  The authors revealed the complex perceptions and psychological needs of elderly individuals regarding Deepfake scams and proposed security design principles that respect their autonomy and lifestyle habits. They also emphasized the importance of experiential learning to improve digital literacy, alongside collective social responsibility to combat technology misuse.

• Advantages Compared to Existing Solutions

  1. Shifted the portrayal of elderly individuals in protective measures from "technologically lagging users" to "active participants," granting them greater autonomy.
  2. Customized designs: Provided differentiated protective measures based on user backgrounds (e.g., urban vs. rural), rather than generic solutions.
  3. Broke the monotony of protective measures by fostering interaction and critical thinking, offering long-term strategies for design practices.

• Experimental or Evaluation Results
  The study found that through participatory design, elderly individuals transitioned from initial skepticism to understanding and empathy in recognizing Deepfake scams. It also uncovered deeper social, cultural, and psychological reasons behind scam victimization. Their evaluations of the design measures indicated that protective measures must strike a balance between security and convenience.

• Limitations and Future Directions

  1. The participant sample was primarily composed of smartphone users from East Asia; future research should expand to groups with diverse cultural backgrounds and varying levels of technological familiarity.
  2. The current study focused more on understanding the needs of elderly individuals; future research should delve into practical design and technological implementation.
  3. Explore ways to lower psychological barriers to digital learning, such as designing immersive or task-integrated learning modules.

This study not only contributes to better serving the elderly population in combating Deepfake scams but also highlights the importance of prioritizing autonomy and dignity in design practices. Additionally, it underscores the need to incorporate broad social responsibility and ethical considerations into cybersecurity design.