Disrupting Diffusion-based Inpainters with Semantic Digression

• URL: http://arxiv.org/abs/2407.10277v1
• Date: Sun, 14 Jul 2024 17:21:19 GMT
• Title: Disrupting Diffusion-based Inpainters with Semantic Digression
• Authors: Geonho Son, Juhun Lee, Simon S. Woo,
• Abstract summary: fabrication of visual misinformation on the web and social media has increased exponentially with the advent of text-to-image diffusion models. Namely, Stable Diffusion inpainters allow the synthesis of maliciously inpainted images of personal and private figures, and copyrighted contents, also known as deepfakes. To combat such generations, a disruption framework, namely Photoguard, has been proposed, where it adds adversarial noise to the context image to disrupt their inpainting synthesis. While their framework suggested a diffusion-friendly approach, the disruption is not sufficiently strong and it requires a significant amount of GPU and time to immunize the context image
• Score: 20.10876695099345
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The fabrication of visual misinformation on the web and social media has increased exponentially with the advent of foundational text-to-image diffusion models. Namely, Stable Diffusion inpainters allow the synthesis of maliciously inpainted images of personal and private figures, and copyrighted contents, also known as deepfakes. To combat such generations, a disruption framework, namely Photoguard, has been proposed, where it adds adversarial noise to the context image to disrupt their inpainting synthesis. While their framework suggested a diffusion-friendly approach, the disruption is not sufficiently strong and it requires a significant amount of GPU and time to immunize the context image. In our work, we re-examine both the minimal and favorable conditions for a successful inpainting disruption, proposing DDD, a "Digression guided Diffusion Disruption" framework. First, we identify the most adversarially vulnerable diffusion timestep range with respect to the hidden space. Within this scope of noised manifold, we pose the problem as a semantic digression optimization. We maximize the distance between the inpainting instance's hidden states and a semantic-aware hidden state centroid, calibrated both by Monte Carlo sampling of hidden states and a discretely projected optimization in the token space. Effectively, our approach achieves stronger disruption and a higher success rate than Photoguard while lowering the GPU memory requirement, and speeding the optimization up to three times faster.

Related papers

• Sparse Repellency for Shielded Generation in Text-to-image Diffusion Models [29.083402085790016]
  We propose a method that coaxes the sampled trajectories of pretrained diffusion models to land on images that fall outside of a reference set. We achieve this by adding repellency terms to the diffusion SDE throughout the generation trajectory. We show that adding SPELL to popular diffusion models improves their diversity while impacting their FID only marginally, and performs comparatively better than other recent training-free diversity methods.
  arXiv  Detail & Related papers  (2024-10-08T13:26:32Z)
• DiffusionGuard: A Robust Defense Against Malicious Diffusion-based Image Editing [93.45507533317405]
  DiffusionGuard is a robust and effective defense method against unauthorized edits by diffusion-based image editing models. We introduce a novel objective that generates adversarial noise targeting the early stage of the diffusion process. We also introduce a mask-augmentation technique to enhance robustness against various masks during test time.
  arXiv  Detail & Related papers  (2024-10-08T05:19:19Z)
• Coherent and Multi-modality Image Inpainting via Latent Space Optimization [61.99406669027195]
  PILOT (intextbfPainting vtextbfIa textbfLatent textbfOptextbfTimization) is an optimization approach grounded on a novel textitsemantic centralization and textitbackground preservation loss. Our method searches latent spaces capable of generating inpainted regions that exhibit high fidelity to user-provided prompts while maintaining coherence with the background.
  arXiv  Detail & Related papers  (2024-07-10T19:58:04Z)
• Disrupting Diffusion: Token-Level Attention Erasure Attack against Diffusion-based Customization [19.635385099376066]
  malicious users have misused diffusion-based customization methods like DreamBooth to create fake images. In this paper, we propose DisDiff, a novel adversarial attack method to disrupt the diffusion model outputs.
  arXiv  Detail & Related papers  (2024-05-31T02:45:31Z)
• Privacy-Preserving Diffusion Model Using Homomorphic Encryption [5.282062491549009]
  We introduce a privacy-preserving stable diffusion framework leveraging homomorphic encryption, called HE-Diffusion. We propose a novel min-distortion method that enables efficient partial image encryption. We successfully implement HE-based privacy-preserving stable diffusion inference.
  arXiv  Detail & Related papers  (2024-03-09T04:56:57Z)
• Adv-Diffusion: Imperceptible Adversarial Face Identity Attack via Latent Diffusion Model [61.53213964333474]
  We propose a unified framework Adv-Diffusion that can generate imperceptible adversarial identity perturbations in the latent space but not the raw pixel space. Specifically, we propose the identity-sensitive conditioned diffusion generative model to generate semantic perturbations in the surroundings. The designed adaptive strength-based adversarial perturbation algorithm can ensure both attack transferability and stealthiness.
  arXiv  Detail & Related papers  (2023-12-18T15:25:23Z)
• IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks [16.577595936609665]
  We introduce a novel approach to counter adversarial attacks, namely, image resampling. Image resampling transforms a discrete image into a new one, simulating the process of scene recapturing or rerendering as specified by a geometrical transformation. We show that our method significantly enhances the adversarial robustness of diverse deep models against various attacks while maintaining high accuracy on clean images.
  arXiv  Detail & Related papers  (2023-10-18T11:19:32Z)
• MaskDiffusion: Boosting Text-to-Image Consistency with Conditional Mask [84.84034179136458]
  A crucial factor leading to the text-image mismatch issue is the inadequate cross-modality relation learning. We propose an adaptive mask, which is conditioned on the attention maps and the prompt embeddings, to dynamically adjust the contribution of each text token to the image features. Our method, termed MaskDiffusion, is training-free and hot-pluggable for popular pre-trained diffusion models.
  arXiv  Detail & Related papers  (2023-09-08T15:53:37Z)
• DiffProtect: Generate Adversarial Examples with Diffusion Models for Facial Privacy Protection [64.77548539959501]
  DiffProtect produces more natural-looking encrypted images than state-of-the-art methods. It achieves significantly higher attack success rates, e.g., 24.5% and 25.1% absolute improvements on the CelebA-HQ and FFHQ datasets.
  arXiv  Detail & Related papers  (2023-05-23T02:45:49Z)
• Towards Prompt-robust Face Privacy Protection via Adversarial Decoupling Augmentation Framework [20.652130361862053]
  We propose the Adversarial Decoupling Augmentation Framework (ADAF) to enhance the defensive performance of facial privacy protection algorithms. ADAF introduces multi-level text-related augmentations for defense stability against various attacker prompts.
  arXiv  Detail & Related papers  (2023-05-06T09:00:50Z)
• SDM: Spatial Diffusion Model for Large Hole Image Inpainting [106.90795513361498]
  We present a novel spatial diffusion model (SDM) that uses a few iterations to gradually deliver informative pixels to the entire image. Also, thanks to the proposed decoupled probabilistic modeling and spatial diffusion scheme, our method achieves high-quality large-hole completion.
  arXiv  Detail & Related papers  (2022-12-06T13:30:18Z)

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.