Targeted Data Protection for Diffusion Model by Matching Training Trajectory

• URL: http://arxiv.org/abs/2512.10433v1
• Date: Thu, 11 Dec 2025 08:47:41 GMT
• Title: Targeted Data Protection for Diffusion Model by Matching Training Trajectory
• Authors: Hojun Lee, Mijin Koo, Yeji Song, Nojun Kwak,
• Abstract summary: We introduce TAFAP (Trajectory Alignment via Fine-tuning with Adrial Perturbations), the first method to successfully achieve effective Targeted Data Protection (TDP)<n>Unlike snapshot-based methods whose protective influence is easily diluted as training progresses, TAFAP employs trajectory-matching inspired by dataset distillation to enforce persistent, verifiable transformations throughout fine-tuning.<n> TAFAP significantly outperforms existing TDP attempts, achieving robust redirection toward target concepts while maintaining high image quality.
• Score: 27.34374942680402
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advancements in diffusion models have made fine-tuning text-to-image models for personalization increasingly accessible, but have also raised significant concerns regarding unauthorized data usage and privacy infringement. Current protection methods are limited to passively degrading image quality, failing to achieve stable control. While Targeted Data Protection (TDP) offers a promising paradigm for active redirection toward user-specified target concepts, existing TDP attempts suffer from poor controllability due to snapshot-matching approaches that fail to account for complete learning dynamics. We introduce TAFAP (Trajectory Alignment via Fine-tuning with Adversarial Perturbations), the first method to successfully achieve effective TDP by controlling the entire training trajectory. Unlike snapshot-based methods whose protective influence is easily diluted as training progresses, TAFAP employs trajectory-matching inspired by dataset distillation to enforce persistent, verifiable transformations throughout fine-tuning. We validate our method through extensive experiments, demonstrating the first successful targeted transformation in diffusion models with simultaneous control over both identity and visual patterns. TAFAP significantly outperforms existing TDP attempts, achieving robust redirection toward target concepts while maintaining high image quality. This work enables verifiable safeguards and provides a new framework for controlling and tracing alterations in diffusion model outputs.

Related papers

• Deep Leakage with Generative Flow Matching Denoiser [54.05993847488204]
  We introduce a new deep leakage (DL) attack that integrates a generative Flow Matching (FM) prior into the reconstruction process.<n>Our approach consistently outperforms state-of-the-art attacks across pixel-level, perceptual, and feature-based similarity metrics.
  arXiv  Detail & Related papers  (2026-01-21T14:51:01Z)
• DLADiff: A Dual-Layer Defense Framework against Fine-Tuning and Zero-Shot Customization of Diffusion Models [74.9510349783152]
  Malicious actors can exploit diffusion model customization with just a few or even one image of a person to create synthetic identities nearly identical to the original identity.<n>This paper proposes Dual-Layer Anti-Diffusion (DLADiff) to defend both fine-tuning methods and zero-shot methods.
  arXiv  Detail & Related papers  (2025-11-25T04:35:55Z)
• Perturb a Model, Not an Image: Towards Robust Privacy Protection via Anti-Personalized Diffusion Models [32.903448192224644]
  Recent advances in diffusion models have enabled high-quality synthesis of specific subjects, such as identities or objects.<n>Personalization techniques can be misused by malicious users to generate unauthorized content.<n>We introduce Direct Protective Optimization (DPO), a novel loss function that effectively disrupts subject personalization in the target model without compromising generative quality.
  arXiv  Detail & Related papers  (2025-11-03T07:42:05Z)
• Boosting Fidelity for Pre-Trained-Diffusion-Based Low-Light Image Enhancement via Condition Refinement [63.54516423266521]
  Pre-Trained Diffusion-Based (PTDB) methods often sacrifice content fidelity to attain higher perceptual realism.<n>We propose a novel optimization strategy for conditioning in pre-trained diffusion models, enhancing fidelity while preserving realism and aesthetics.<n>Our approach is plug-and-play, seamlessly integrating into existing diffusion networks to provide more effective control.
  arXiv  Detail & Related papers  (2025-10-20T02:40:06Z)
• ScaleWeaver: Weaving Efficient Controllable T2I Generation with Multi-Scale Reference Attention [86.93601565563954]
  ScaleWeaver is a framework designed to achieve high-fidelity, controllable generation upon advanced visual autoregressive( VAR) models.<n>The proposed Reference Attention module discards the unnecessary attention from image$rightarrow$condition, reducing computational cost.<n>Experiments show that ScaleWeaver delivers high-quality generation and precise control while attaining superior efficiency over diffusion-based methods.
  arXiv  Detail & Related papers  (2025-10-16T17:00:59Z)
• Latent Diffusion Unlearning: Protecting Against Unauthorized Personalization Through Trajectory Shifted Perturbations [18.024767641200064]
  We propose a model-based perturbation strategy that operates within the latent space of diffusion models.<n>Our method alternates between denoising and inversion while modifying the starting point of the denoising trajectory: of diffusion models.<n>We validate our approach on four benchmark datasets to demonstrate robustness against state-of-the-art inversion attacks.
  arXiv  Detail & Related papers  (2025-10-03T15:18:45Z)
• Concept Unlearning by Modeling Key Steps of Diffusion Process [27.387108656476375]
  Concept unlearning has been used to prevent text-to-image diffusion models from being misused to generate undesirable visual content.<n>We propose Key Step Concept Unlearning (KSCU), which selectively fine-tunes the model at key steps to the target concept.<n>For example, on the I2P dataset, KSCU outperforms ESD by 8.3% in nudity unlearning accuracy while improving FID by 8.4%, and achieves a high overall score of 0.92, substantially surpassing all other SOTA methods.
  arXiv  Detail & Related papers  (2025-07-09T03:55:58Z)
• Orthogonal Projection Subspace to Aggregate Online Prior-knowledge for Continual Test-time Adaptation [67.80294336559574]
  Continual Test Time Adaptation (CTTA) is a task that requires a source pre-trained model to continually adapt to new scenarios.<n>We propose a novel pipeline, Orthogonal Projection Subspace to aggregate online Prior-knowledge, dubbed OoPk.
  arXiv  Detail & Related papers  (2025-06-23T18:17:39Z)
• ADT: Tuning Diffusion Models with Adversarial Supervision [16.974169058917443]
  Diffusion models have achieved outstanding image generation by reversing a forward noising process to approximate true data distributions.<n>We propose Adrial Diffusion Tuning (ADT) to stimulate the inference process during optimization and align the final outputs with training data.<n>ADT features a siamese-network discriminator with a fixed pre-trained backbone and lightweight trainable parameters.
  arXiv  Detail & Related papers  (2025-04-15T17:37:50Z)
• Adversarial Robustification via Text-to-Image Diffusion Models [56.37291240867549]
  Adrial robustness has been conventionally believed as a challenging property to encode for neural networks. We develop a scalable and model-agnostic solution to achieve adversarial robustness without using any data.
  arXiv  Detail & Related papers  (2024-07-26T10:49:14Z)
• Adaptive Bidirectional Displacement for Semi-Supervised Medical Image Segmentation [11.195959019678314]
  Consistency learning is a central strategy to tackle unlabeled data in semi-supervised medical image segmentation. In this paper, we propose an Adaptive Bidirectional Displacement approach to solve the above challenge.
  arXiv  Detail & Related papers  (2024-05-01T08:17:43Z)
• Learn from the Past: A Proxy Guided Adversarial Defense Framework with Self Distillation Regularization [53.04697800214848]
  Adversarial Training (AT) is pivotal in fortifying the robustness of deep learning models. AT methods, relying on direct iterative updates for target model's defense, frequently encounter obstacles such as unstable training and catastrophic overfitting. We present a general proxy guided defense framework, LAST' (bf Learn from the Pbf ast)
  arXiv  Detail & Related papers  (2023-10-19T13:13:41Z)

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.