Regressor-Guided Image Editing Regulates Emotional Response to Reduce Online Engagement
Journal:
arXiv
Published Date:
Jan 21, 2025
Abstract
Emotions are known to mediate the relationship between users' content
consumption and their online engagement, with heightened emotional intensity
leading to increased engagement. Building on this insight, we propose three
regressor-guided image editing approaches aimed at diminishing the emotional
impact of images. These include (i) a parameter optimization approach based on
global image transformations known to influence emotions, (ii) an optimization
approach targeting the style latent space of a generative adversarial network,
and (iii) a diffusion-based approach employing classifier guidance and
classifier-free guidance. Our findings demonstrate that approaches can
effectively alter the emotional properties of images while maintaining high
visual quality. Optimization-based methods primarily adjust low-level
properties like color hues and brightness, whereas the diffusion-based approach
introduces semantic changes, such as altering appearance or facial expressions.
Notably, results from a behavioral study reveal that only the diffusion-based
approach successfully elicits changes in viewers' emotional responses while
preserving high perceived image quality. In future work, we will investigate
the impact of these image adaptations on internet user behavior.
