Unmasking the Veil: An Investigation into Concept Ablation for Privacy and Copyright Protection in Images
Abstract: In this paper, we extend the study of concept ablation within pre-trained models as introduced in 'Ablating Concepts in Text-to-Image Diffusion Models' by $\citep{Kumari2022}$. Our work focuses on reproducing the results achieved by the different variants of concept ablation proposed through predefined metrics. We also introduce a novel variant of concept ablation—trademark ablation. This variant combines the principles of memorization and instance ablation to tackle the nuanced influence of proprietary or branded elements in model outputs. Further, our research contributions include an observational analysis of the model's limitations. Moreover, we investigate the model's behavior in response to ablation leakage-inducing prompts, which aim to indirectly ablate concepts, revealing insights into the model's resilience and adaptability. We also observe the model's performance degradation on images generated by concepts far from its target ablation concept, which is documented in the appendix.
Submission Length: Regular submission (no more than 12 pages of main content)
Changes Since Last Submission: * Modified image of graph to cover a smaller step size in section 4.1.3
* Added notations for the equations covered in section 2.1
* Added a Discussion section to explore methods to correct current limitations.
* Added a user study to validate our claims in the Appendix
* Fixed minor typos.
Assigned Action Editor: ~Jonathan_Ullman1
Submission Number: 2262
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