Invisible Brand Messaging: Data Poisoning in Text-to-Image Diffusion Models

Text-to-image diffusion models have made impressive progress in recent years, enabling the creation of high-quality images from simple text descriptions. These models are based on massive datasets, often publicly accessible and used for fine-tuning new models. However, this openness also carries risks, particularly the vulnerability to data poisoning attacks.

A new attack, called the "Silent Branding Attack," demonstrates how manipulated data can be used to insert specific brand logos or symbols into generated images without needing to mention them in the text prompt. The attack exploits the models' ability to learn and reproduce visual patterns when they appear repeatedly in the training dataset. By inconspicuously inserting logos into the training images, the model learns to integrate these logos into the generated images even without an explicit request.

How the Silent Branding Attack Works

The Silent Branding Attack is based on an automated algorithm that subtly inserts logos into the original images of the training dataset. Care is taken to ensure that the logos appear natural and are not recognizable as manipulation. The model trained with this poisoned dataset subsequently generates images containing the embedded logos without affecting image quality or adherence to the text prompt.

The effectiveness of the attack has been tested in various scenarios, including large datasets with high-quality images and datasets for style personalization. The results show that the attack achieves a high success rate even without explicit text triggers. Both human evaluations and quantitative metrics, such as logo detection, confirm the attack's ability to embed logos inconspicuously.

Implications and Future Research

The Silent Branding Attack highlights the security risks associated with using publicly available data for training AI models. The possibility of manipulating models to generate unwanted content poses a serious threat, especially regarding copyright infringement, the spread of propaganda, or the manipulation of public opinion.

Future research should focus on developing more robust training methods that are less susceptible to data poisoning attacks. This could be achieved, for example, by implementing mechanisms for detecting and removing manipulated data or by developing models explicitly trained to detect unwanted patterns.

The increasing prevalence of AI models in everyday life requires increased awareness of the associated security risks. Only through continuous research and the development of effective protective measures can we ensure that these technologies are used responsibly and safely.

Bibliography: Jang, S., Choi, J. S., Jo, J., Lee, K., & Hwang, S. J. (2025). Silent Branding Attack: Trigger-free Data Poisoning Attack on Text-to-Image Diffusion Models. *CVPR 2025*. Further Sources: - https://arxiv.org/abs/2305.04175 - https://x.com/MLAI_KAIST/status/1896753677681225859 - https://harryjo97.github.io/ - https://arxiv.org/html/2310.13828v2 - https://x.com/mlai_kaist?lang=de - https://arteesetica.org/wp-content/uploads/2023/12/Nightshade.pdf - https://paperswithcode.com/task/data-poisoning/latest?page=6&q= - https://alinlab.kaist.ac.kr/publications.html - https://www.mlai-kaist.com/publication - https://people.cs.uchicago.edu/~ravenben/publications/pdf/nightshade-oakland24.pdf