Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models
- URL: http://arxiv.org/abs/2406.14855v1
- Date: Fri, 21 Jun 2024 03:58:44 GMT
- Title: Six-CD: Benchmarking Concept Removals for Benign Text-to-image Diffusion Models
- Authors: Jie Ren, Kangrui Chen, Yingqian Cui, Shenglai Zeng, Hui Liu, Yue Xing, Jiliang Tang, Lingjuan Lyu,
- Abstract summary: Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts.
The models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts.
concept removal methods have been proposed to modify diffusion models to prevent the generation of malicious and unwanted concepts.
- Score: 58.74606272936636
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Text-to-image (T2I) diffusion models have shown exceptional capabilities in generating images that closely correspond to textual prompts. However, the advancement of T2I diffusion models presents significant risks, as the models could be exploited for malicious purposes, such as generating images with violence or nudity, or creating unauthorized portraits of public figures in inappropriate contexts. To mitigate these risks, concept removal methods have been proposed. These methods aim to modify diffusion models to prevent the generation of malicious and unwanted concepts. Despite these efforts, existing research faces several challenges: (1) a lack of consistent comparisons on a comprehensive dataset, (2) ineffective prompts in harmful and nudity concepts, (3) overlooked evaluation of the ability to generate the benign part within prompts containing malicious concepts. To address these gaps, we propose to benchmark the concept removal methods by introducing a new dataset, Six-CD, along with a novel evaluation metric. In this benchmark, we conduct a thorough evaluation of concept removals, with the experimental observations and discussions offering valuable insights in the field.
Related papers
- Unstable Unlearning: The Hidden Risk of Concept Resurgence in Diffusion Models [13.479224197351673]
We show that fine-tuning a text-to-image diffusion model on seemingly unrelated images can cause it to "relearn" concepts that were previously "unlearned"
Our findings underscore the fragility of composing incremental model updates.
arXiv Detail & Related papers (2024-10-10T16:10:27Z) - Safeguard Text-to-Image Diffusion Models with Human Feedback Inversion [51.931083971448885]
We propose a framework named Human Feedback Inversion (HFI), where human feedback on model-generated images is condensed into textual tokens guiding the mitigation or removal of problematic images.
Our experimental results demonstrate our framework significantly reduces objectionable content generation while preserving image quality, contributing to the ethical deployment of AI in the public sphere.
arXiv Detail & Related papers (2024-07-17T05:21:41Z) - Unlearning Concepts in Diffusion Model via Concept Domain Correction and Concept Preserving Gradient [20.091446060893638]
This paper proposes a concept domain correction framework for unlearning concepts in diffusion models.
By aligning the output domains of sensitive concepts and anchor concepts through adversarial training, we enhance the generalizability of the unlearning results.
arXiv Detail & Related papers (2024-05-24T07:47:36Z) - Concept Arithmetics for Circumventing Concept Inhibition in Diffusion Models [58.065255696601604]
We use compositional property of diffusion models, which allows to leverage multiple prompts in a single image generation.
We argue that it is essential to consider all possible approaches to image generation with diffusion models that can be employed by an adversary.
arXiv Detail & Related papers (2024-04-21T16:35:16Z) - Implicit Concept Removal of Diffusion Models [92.55152501707995]
Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images.
We present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.
arXiv Detail & Related papers (2023-10-09T17:13:10Z) - Towards Safe Self-Distillation of Internet-Scale Text-to-Image Diffusion
Models [63.20512617502273]
We propose a method called SDD to prevent problematic content generation in text-to-image diffusion models.
Our method eliminates a much greater proportion of harmful content from the generated images without degrading the overall image quality.
arXiv Detail & Related papers (2023-07-12T07:48:29Z) - Ablating Concepts in Text-to-Image Diffusion Models [57.9371041022838]
Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability.
These models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos.
We propose an efficient method of ablating concepts in the pretrained model, preventing the generation of a target concept.
arXiv Detail & Related papers (2023-03-23T17:59:42Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.