Memory Triggers: Unveiling Memorization in Text-To-Image Generative Models through Word-Level Duplication

• URL: http://arxiv.org/abs/2312.03692v1
• Date: Wed, 6 Dec 2023 18:54:44 GMT
• Title: Memory Triggers: Unveiling Memorization in Text-To-Image Generative Models through Word-Level Duplication
• Authors: Ali Naseh, Jaechul Roh, Amir Houmansadr
• Abstract summary: Diffusion-based models have revolutionized text-to-image synthesis with their ability to produce high-quality, high-resolution images. These models also raise concerns due to their tendency to replicate exact training samples, posing privacy risks and enabling adversarial attacks. This paper focuses on two distinct and underexplored types of duplication that lead to replication during inference in diffusion-based models.
• Score: 16.447035745151428
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion-based models, such as the Stable Diffusion model, have revolutionized text-to-image synthesis with their ability to produce high-quality, high-resolution images. These advancements have prompted significant progress in image generation and editing tasks. However, these models also raise concerns due to their tendency to memorize and potentially replicate exact training samples, posing privacy risks and enabling adversarial attacks. Duplication in training datasets is recognized as a major factor contributing to memorization, and various forms of memorization have been studied so far. This paper focuses on two distinct and underexplored types of duplication that lead to replication during inference in diffusion-based models, particularly in the Stable Diffusion model. We delve into these lesser-studied duplication phenomena and their implications through two case studies, aiming to contribute to the safer and more responsible use of generative models in various applications.

Related papers

• Reinforcing Pre-trained Models Using Counterfactual Images [54.26310919385808]
  This paper proposes a novel framework to reinforce classification models using language-guided generated counterfactual images. We identify model weaknesses by testing the model using the counterfactual image dataset. We employ the counterfactual images as an augmented dataset to fine-tune and reinforce the classification model.
  arXiv  Detail & Related papers  (2024-06-19T08:07:14Z)
• Memorized Images in Diffusion Models share a Subspace that can be Located and Deleted [15.162296378581853]
  Large-scale text-to-image diffusion models excel in generating high-quality images from textual inputs. Concerns arise as research indicates their tendency to memorize and replicate training data. Efforts within the text-to-image community to address memorization explore causes such as data duplication, replicated captions, or trigger tokens.
  arXiv  Detail & Related papers  (2024-06-01T15:47:13Z)
• Frame by Familiar Frame: Understanding Replication in Video Diffusion Models [28.360705633967353]
  Video generation poses greater challenges due to its higher-dimensional nature, the scarcity of training data, and the complex relationships involved. Video diffusion models, which operate with even more constrained datasets, may be more prone to replicating samples from their training sets. We present a systematic investigation into the phenomenon of sample replication in video diffusion models.
  arXiv  Detail & Related papers  (2024-03-28T17:15:23Z)
• Unveiling and Mitigating Memorization in Text-to-image Diffusion Models through Cross Attention [62.671435607043875]
  Research indicates that text-to-image diffusion models replicate images from their training data, raising tremendous concerns about potential copyright infringement and privacy risks. We reveal that during memorization, the cross-attention tends to focus disproportionately on the embeddings of specific tokens. We introduce an innovative approach to detect and mitigate memorization in diffusion models.
  arXiv  Detail & Related papers  (2024-03-17T01:27:00Z)
• Mitigate Replication and Copying in Diffusion Models with Generalized Caption and Dual Fusion Enhancement [7.9911486976035215]
  We introduce a generality score that measures the caption generality and employ large language model (LLM) to generalize training captions. We leverage generalized captions and propose a novel dual fusion enhancement approach to mitigate the replication of diffusion models.
  arXiv  Detail & Related papers  (2023-09-13T18:43:13Z)
• DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability [75.9781362556431]
  We propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. We show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks.
  arXiv  Detail & Related papers  (2023-08-18T05:03:48Z)
• Understanding and Mitigating Copying in Diffusion Models [53.03978584040557]
  Images generated by diffusion models like Stable Diffusion are increasingly widespread. Recent works and even lawsuits have shown that these models are prone to replicating their training data, unbeknownst to the user.
  arXiv  Detail & Related papers  (2023-05-31T17:58:02Z)
• Diffusion Art or Digital Forgery? Investigating Data Replication in Diffusion Models [53.03978584040557]
  We study image retrieval frameworks that enable us to compare generated images with training samples and detect when content has been replicated. Applying our frameworks to diffusion models trained on multiple datasets including Oxford flowers, Celeb-A, ImageNet, and LAION, we discuss how factors such as training set size impact rates of content replication.
  arXiv  Detail & Related papers  (2022-12-07T18:58:02Z)
• SinDiffusion: Learning a Diffusion Model from a Single Natural Image [159.4285444680301]
  We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image. It is based on two core designs. First, SinDiffusion is trained with a single model at a single scale instead of multiple models with progressive growing of scales. Second, we identify that a patch-level receptive field of the diffusion network is crucial and effective for capturing the image's patch statistics.
  arXiv  Detail & Related papers  (2022-11-22T18:00:03Z)

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.