Examining Human Perception of Generative Content Replacement in Image Privacy Protection

Document Title

Examining Human Perception of Generative Content Replacement in Image Privacy Protection

Document Information

• Subject Area: Image Privacy Protection, Human-Computer Interaction, Generative Artificial Intelligence
• Keywords: Image Privacy Protection, Usable Security, Generative AI, Diffusion Models, Narrative Consistency, Visual Integration

Research Background and Issues

• Problems and Challenges:

  • With the widespread use of image capturing and online sharing, privacy leakage issues are becoming increasingly prominent.
  • Common image privacy protection techniques (e.g., blurring) often struggle to balance privacy protection and image usability, compromising the practical utility and visual experience of images.
  • Unconventional privacy protection methods (e.g., image replacement or cartoonization) may negatively impact user experience due to visual unnaturalness.

• Significance:

  • Seamless image privacy protection can effectively prevent privacy threats while preserving the narrative of the image.
  • Exploring user perceptions of generative AI applied to privacy protection in the fields of human-computer interaction and machine learning can facilitate better interface design.

• Motivation and Related Work:

  • Computer vision research has applied generative AI to replace privacy-threatening content in images (e.g., license plates, faces).
  • This study is the first to evaluate a generative content replacement method from the perspective of user perception, considering the applicability and user experience of such technologies.

Solution

• Method and Solution:

  • This paper proposes a Generative Content Replacement (GCR) method using the latest image description model (BLIP-2) and Stable Diffusion v2.1.
  • GCR replaces privacy-threatening content in images with similar and realistic alternative content, ensuring seamless integration within the image.

• Innovations:

  • Compared to traditional methods, GCR protects privacy while achieving low detectability, making editing traces difficult to perceive.
  • GCR is applicable to various privacy threat scenarios and maintains the narrative and visual harmony of the image.

• Implementation Steps:

  1. Use BLIP-2 to automatically generate descriptions of privacy-threatening content and the overall narrative of the image.
  2. Combine textual prompts with diffusion models to remove original content and generate replacement content.
  3. Automatically identify privacy-threatening parts of the image and complete replacement through iterative denoising.

Research Findings

• Specific Results:

  • GCR was shown to be undetectable in 60% of test images.
  • It outperformed methods such as blurring, color filling, and removal in maintaining image narrative and visual harmony.
  • User satisfaction with GCR-edited images was significantly higher than with other methods.

• Advantages Comparison:

  • Compared to Existing Methods: Among four common image privacy protection methods (blurring, cartoonization, color filling, removal), GCR performed best in terms of detectability and narrative preservation.
  • Visual and Narrative Effects: GCR maintained visual harmony, effectively hiding privacy content without reducing the image's communicative value.

• Experimental Data and Evaluation Results:

  • Experimental Design: Collected 270 privacy-threatening images, applied five privacy protection techniques (including GCR), and tested user perceptions (e.g., editing detectability, narrative consistency, visual harmony).
  • Statistical Analysis:
    • GCR scored highest in editing detection difficulty.
    • GCR-generated replacement content led in visual integration and user satisfaction ratings.
    • Users expressed clear preferences for low detectability and narrative preservation.

• Limitations and Future Directions:

  • Limitations:
    • The dataset primarily consisted of public images, lacking real-world testing on users' personal images.
    • GCR occasionally exhibited unnatural results in handling complex generation tasks (e.g., replacing body parts or textual content).
    • Respondents were predominantly from Western countries, potentially limiting cultural adaptability for diverse users.
  • Future Directions:
    • Explore more advanced generative models to optimize GCR performance in complex privacy scenarios.
    • Conduct cross-cultural studies on user perceptions of privacy protection methods.
    • Perform experiments in real-world usage scenarios to analyze user acceptance and preferences for GCR.

Conclusion

The proposed GCR method demonstrates superior privacy protection capabilities, low editing detectability, and high user satisfaction, offering an innovative solution in the field of image privacy protection. Future research should focus on deeper human-computer interaction studies to enable broader applications and optimize user experience with GCR.