Silent Branding Attack: Trigger-free Data Poisoning Attack on
  Text-to-Image Diffusion Models
Text-to-image diffusion models have achieved remarkable success in generating
high-quality contents from text prompts. However, their reliance on publicly
available data and the growing trend of data sharing for fine-tuning make these
models particularly vulnerable to data poisoning attacks. In this work, we
introduce the Silent Branding Attack, a novel data poisoning method that
manipulates text-to-image diffusion models to generate images containing
specific brand logos or symbols without any text triggers. We find that when
certain visual patterns are repeatedly in the training data, the model learns
to reproduce them naturally in its outputs, even without prompt mentions.
Leveraging this, we develop an automated data poisoning algorithm that
unobtrusively injects logos into original images, ensuring they blend naturally
and remain undetected. Models trained on this poisoned dataset generate images
containing logos without degrading image quality or text alignment. We
experimentally validate our silent branding attack across two realistic
settings on large-scale high-quality image datasets and style personalization
datasets, achieving high success rates even without a specific text trigger.
Human evaluation and quantitative metrics including logo detection show that
our method can stealthily embed logos.
67d37754e07f664c7325f236
Sangwon Jang
claimed_verified
2025-03-14T08:56:49.038Z
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/avatars/cd038f11dc1007b1267324b34c165dda.svg
Sangwon
user
agwmon
67d37754e07f664c7325f237
June Suk Choi
admin_assigned
2025-03-14T09:05:59.636Z
66c6edcc91dced946471bc13
/avatars/55cc8593da6540e1566e1de9d7133f9f.svg
June Suk Choi
user
wchoi403
67d37754e07f664c7325f238
Jaehyeong Jo
admin_assigned
2025-03-14T09:06:19.773Z
65e5bd4568234ef5d6decadc
/avatars/c41095a946c0176b949c0b3566136c05.svg
Jaehyeong Jo
user
harryjo97
67d37754e07f664c7325f239
Kimin Lee
admin_assigned
2025-03-14T09:06:26.649Z
635097ec59bfa9a85d4207b2
/avatars/787085894e9e6538b6b3e3051efe9eea.svg
Kimin Lee
user
kiminle2
67d37754e07f664c7325f23a
Sung Ju Hwang
Silent Branding Attack: Trigger-free Data Poisoning Attack on
  Text-to-Image Diffusion Models
Text-to-image diffusion models have achieved remarkable success in generating
high-quality contents from text prompts. However, their reliance on publicly
available data and the growing trend of data sharing for fine-tuning make these
models particularly vulnerable to data poisoning attacks. In this work, we
introduce the Silent Branding Attack, a novel data poisoning method that
manipulates text-to-image diffusion models to generate images containing
specific brand logos or symbols without any text triggers. We find that when
certain visual patterns are repeatedly in the training data, the model learns
to reproduce them naturally in its outputs, even without prompt mentions.
Leveraging this, we develop an automated data poisoning algorithm that
unobtrusively injects logos into original images, ensuring they blend naturally
and remain undetected. Models trained on this poisoned dataset generate images
containing logos without degrading image quality or text alignment. We
experimentally validate our silent branding attack across two realistic
settings on large-scale high-quality image datasets and style personalization
datasets, achieving high success rates even without a specific text trigger.
Human evaluation and quantitative metrics including logo detection show that
our method can stealthily embed logos.
The Silent Branding Attack manipulates text-to-image diffusion models to generate images with specific logos without text triggers, using data poisoning techniques.
text-to-image diffusion models data poisoning Silent Branding Attack visual patterns automated data poisoning algorithm logo detection style personalization datasets