Leaky Diffusion: Attribute Leakage in Text-Guided Image Generation

Anastasios Lepipas, Marios Charalambides, Jiani Liu, Yiying Guan, Dominika Woszczyk, Mansi, Thanh Hai Le, Soteris Demetriou

Abstract:

Text–guided diffusion models can be used to generate photorealistic images conditioned on natural language instructions. Due to
their ease of use, millions of users already leverage them to generate and populate images online. In this work, we reveal the risk
of attribute (authorship and dementia) leakage from such models.
Existing authorship and dementia inferences rely primarily on text.
We show that instructions are a new form of text that can reveal
these attributes. More surprisingly, and in contrast to prior work,
we show that those attributes can be transferred and leaked from
images generated with diffusion models. In particular, we construct
image and multi–modal adversarial models which leverage image
data augmentation and text–image embedding models to achieve
state of the art performance in spear authorship inference (up to
0.877% Top–5 accuracy for 100 authors), while dementia inference
is possible even from the output images alone (0.75% accuracy on
the ADReSS dataset). Our rigorous evaluation shows that such inferences remain robust using different training sets, and when trained
in classifier-independent ways, and against SOTA mitigations such
paraphrasing Transformer models and LLMs.