My Body My Choice: Human-Centric Full-Body Anonymization
This work addresses privacy concerns for individuals in online media by enabling customizable anonymization, though it is incremental as it builds on existing methods like diffusion models and GANs.
The paper tackles the problem of full-body anonymization in online content by proposing a human-guided approach that adapts to various contexts like cultural norms and personal preferences, achieving state-of-the-art results in anonymization tasks as evaluated on seven datasets with image, adversarial, and generative metrics.
In an era of increasing privacy concerns for our online presence, we propose that the decision to appear in a piece of content should only belong to the owner of the body. Although some automatic approaches for full-body anonymization have been proposed, human-guided anonymization can adapt to various contexts, such as cultural norms, personal relations, esthetic concerns, and security issues. ''My Body My Choice'' (MBMC) enables physical and adversarial anonymization by removal and swapping approaches aimed for four tasks, designed by single or multi, ControlNet or GAN modules, combining several diffusion models. We evaluate anonymization on seven datasets; compare with SOTA inpainting and anonymization methods; evaluate by image, adversarial, and generative metrics; and conduct reidentification experiments.