StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model

• URL: http://arxiv.org/abs/2408.05669v1
• Date: Sun, 11 Aug 2024 01:22:29 GMT
• Title: StealthDiffusion: Towards Evading Diffusion Forensic Detection through Diffusion Model
• Authors: Ziyin Zhou, Ke Sun, Zhongxi Chen, Huafeng Kuang, Xiaoshuai Sun, Rongrong Ji,
• Abstract summary: StealthDiffusion is a framework that modifies AI-generated images into high-quality, imperceptible adversarial examples. It is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries.
• Score: 62.25424831998405
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress in generative models has given rise to the critical task of AI-Generated Content Stealth (AIGC-S), which aims to create AI-generated images that can evade both forensic detectors and human inspection. This task is crucial for understanding the vulnerabilities of existing detection methods and developing more robust techniques. However, current adversarial attacks often introduce visible noise, have poor transferability, and fail to address spectral differences between AI-generated and genuine images. To address this, we propose StealthDiffusion, a framework based on stable diffusion that modifies AI-generated images into high-quality, imperceptible adversarial examples capable of evading state-of-the-art forensic detectors. StealthDiffusion comprises two main components: Latent Adversarial Optimization, which generates adversarial perturbations in the latent space of stable diffusion, and Control-VAE, a module that reduces spectral differences between the generated adversarial images and genuine images without affecting the original diffusion model's generation process. Extensive experiments show that StealthDiffusion is effective in both white-box and black-box settings, transforming AI-generated images into high-quality adversarial forgeries with frequency spectra similar to genuine images. These forgeries are classified as genuine by advanced forensic classifiers and are difficult for humans to distinguish.

Related papers

• Vulnerabilities in AI-generated Image Detection: The Challenge of Adversarial Attacks [17.87119255294563]
  We investigate the vulnerability of state-of-the-art AIGI detectors against adversarial attack under white-box and black-box settings. We propose a new attack containing two main parts. First, inspired by the obvious difference between real images and fake images in the frequency domain, we add perturbations under the frequency domain to push the image away from its original frequency distribution. We show that adversarial attack is truly a real threat to AIGI detectors, because FPBA can deliver successful black-box attacks across models, generators, defense methods, and even evade cross-generator detection.
  arXiv  Detail & Related papers  (2024-07-30T14:07:17Z)
• RIGID: A Training-free and Model-Agnostic Framework for Robust AI-Generated Image Detection [60.960988614701414]
  RIGID is a training-free and model-agnostic method for robust AI-generated image detection. RIGID significantly outperforms existing trainingbased and training-free detectors.
  arXiv  Detail & Related papers  (2024-05-30T14:49:54Z)
• Diffusion Facial Forgery Detection [56.69763252655695]
  This paper introduces DiFF, a comprehensive dataset dedicated to face-focused diffusion-generated images. We conduct extensive experiments on the DiFF dataset via a human test and several representative forgery detection methods. The results demonstrate that the binary detection accuracy of both human observers and automated detectors often falls below 30%.
  arXiv  Detail & Related papers  (2024-01-29T03:20:19Z)
• MAEDiff: Masked Autoencoder-enhanced Diffusion Models for Unsupervised Anomaly Detection in Brain Images [40.89943932086941]
  We propose a novel Masked Autoencoder-enhanced Diffusion Model (MAEDiff) for unsupervised anomaly detection in brain images. The MAEDiff involves a hierarchical patch partition. It generates healthy images by overlapping upper-level patches and implements a mechanism based on the masked autoencoders operating on the sub-level patches to enhance the condition on the unnoised regions.
  arXiv  Detail & Related papers  (2024-01-19T08:54:54Z)
• 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)
• Exposing the Fake: Effective Diffusion-Generated Images Detection [14.646957596560076]
  This paper proposes a novel detection method called Stepwise Error for Diffusion-generated Image Detection (SeDID) SeDID exploits the unique attributes of diffusion models, namely deterministic reverse and deterministic denoising errors. Our work makes a pivotal contribution to distinguishing diffusion model-generated images, marking a significant step in the domain of artificial intelligence security.
  arXiv  Detail & Related papers  (2023-07-12T16:16:37Z)
• DIRE for Diffusion-Generated Image Detection [128.95822613047298]
  We propose a novel representation called DIffusion Reconstruction Error (DIRE) DIRE measures the error between an input image and its reconstruction counterpart by a pre-trained diffusion model. It provides a hint that DIRE can serve as a bridge to distinguish generated and real images.
  arXiv  Detail & Related papers  (2023-03-16T13:15:03Z)
• Adversarially-Aware Robust Object Detector [85.10894272034135]
  We propose a Robust Detector (RobustDet) based on adversarially-aware convolution to disentangle gradients for model learning on clean and adversarial images. Our model effectively disentangles gradients and significantly enhances the detection robustness with maintaining the detection ability on clean images.
  arXiv  Detail & Related papers  (2022-07-13T13:59:59Z)
• Fast Unsupervised Brain Anomaly Detection and Segmentation with Diffusion Models [1.6352599467675781]
  We propose a method based on diffusion models to detect and segment anomalies in brain imaging. Our diffusion models achieve competitive performance compared with autoregressive approaches across a series of experiments with 2D CT and MRI data.
  arXiv  Detail & Related papers  (2022-06-07T17:30:43Z)

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.