9 accepted papers
Erasing harmful or proprietary concepts from powerful text‑to‑image generators is an emerging safety requirement, yet current ``concept erasure'' techniques either collapse image quality, rely on brittle adversarial losses, or demand prohibitive retraining cycles. We trace these limitations to a myo…
Transformer-based large-scale pre-trained models achieve great success. Fine-tuning is the standard practice for leveraging these models in downstream tasks. Among the fine-tuning methods, adapter-tuning provides a parameter-efficient fine-tuning by introducing lightweight trainable modules while ke…
Text-to-image (T2I) diffusion models notably the unCLIP models (e.g. DALL-E-2) achieve state-of-the-art (SOTA) performance on various compositional T2I benchmarks at the cost of significant computational resources. The unCLIP stack comprises T2I prior and diffusion image decoder. The T2I prior model…
My doctoral research delves into the realm of generative model fingerprinting, aiming to assign responsibility for the generated images. I introduce frameworks that modify generative models to incorporate each user's distinct digital fingerprint. This ensures that every piece of generated content ca…
Contrastive Language-Image Pretraining (CLIP) models maximize the mutual information between text and visual modalities to learn representations. This makes the nature of the training data a significant factor in the efficacy of CLIP for downstream tasks. However, the lack of compositional diversity…
The rapid advancement of generative models facilitating the creation of hyper-realistic images from textual descriptions has concurrently escalated critical societal concerns such as misinformation. Although providing some mitigation traditional fingerprinting mechanisms fall short in attributing re…
Generative models have enabled the creation of contents that are indistinguishable from those taken from nature. Open-source development of such models raised concerns about the risks of their misuse for malicious purposes. One potential risk mitigation strategy is to attribute generative models via…
Generative models are now capable of synthesizing images, speeches, and videos that are hardly distinguishable from authentic contents. Such capabilities cause concerns such as malicious impersonation and IP theft. This paper investigates a solution for model attribution, i.e., the classification of…
Growing applications of generative models have led to new threats such as malicious personation and digital copyright infringement. One solution to these threats is model attribution, i.e., the identification of user-end models where the contents under question are generated. Existing studies showe…