Could It Be Generated? Towards Practical Analysis of Memorization in Text-To-Image Diffusion Models
<|reference_start|>Could It Be Generated? Towards Practical Analysis of Memorization in Text-To-Image Diffusion Models: The past few years have witnessed substantial advancement in text-guided image generation powered by diffusion models. However, it was shown that text-to-image diffusion models are vulnerable to training image memorization, raising concerns on copyright infringement and privacy invasion. In this work, we perform practical analysis of memorization in text-to-image diffusion models. Targeting a set of images to protect, we conduct quantitive analysis on them without need to collect any prompts. Specifically, we first formally define the memorization of image and identify three necessary conditions of memorization, respectively similarity, existence and probability. We then reveal the correlation between the model's prediction error and image replication. Based on the correlation, we propose to utilize inversion techniques to verify the safety of target images against memorization and measure the extent to which they are memorized. Model developers can utilize our analysis method to discover memorized images or reliably claim safety against memorization. Extensive experiments on the Stable Diffusion, a popular open-source text-to-image diffusion model, demonstrate the effectiveness of our analysis method.<|reference_end|>
@article{ma2024could,
    title={Could It Be Generated? Towards Practical Analysis of Memorization in
  Text-To-Image Diffusion Models},
    author={Zhe Ma, Xuhong Zhang, Qingming Li, Tianyu Du, Wenzhi Chen, Zonghui
  Wang, Shouling Ji},
    journal={arXiv preprint arXiv:2405.05846},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05846},
    primaryClass={cs.CR cs.CV}
}