MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks

• URL: http://arxiv.org/abs/2410.14089v1
• Date: Thu, 17 Oct 2024 23:52:39 GMT
• Title: MMAD-Purify: A Precision-Optimized Framework for Efficient and Scalable Multi-Modal Attacks
• Authors: Xinxin Liu, Zhongliang Guo, Siyuan Huang, Chun Pong Lau,
• Abstract summary: We introduce an innovative framework that incorporates a precision-optimized noise predictor to enhance the effectiveness of our attack framework. Our framework provides a cutting-edge solution for multi-modal adversarial attacks, ensuring reduced latency. We demonstrate that our framework achieves outstanding transferability and robustness against purification defenses.
• Score: 21.227398434694724
• License:
• Abstract: Neural networks have achieved remarkable performance across a wide range of tasks, yet they remain susceptible to adversarial perturbations, which pose significant risks in safety-critical applications. With the rise of multimodality, diffusion models have emerged as powerful tools not only for generative tasks but also for various applications such as image editing, inpainting, and super-resolution. However, these models still lack robustness due to limited research on attacking them to enhance their resilience. Traditional attack techniques, such as gradient-based adversarial attacks and diffusion model-based methods, are hindered by computational inefficiencies and scalability issues due to their iterative nature. To address these challenges, we introduce an innovative framework that leverages the distilled backbone of diffusion models and incorporates a precision-optimized noise predictor to enhance the effectiveness of our attack framework. This approach not only enhances the attack's potency but also significantly reduces computational costs. Our framework provides a cutting-edge solution for multi-modal adversarial attacks, ensuring reduced latency and the generation of high-fidelity adversarial examples with superior success rates. Furthermore, we demonstrate that our framework achieves outstanding transferability and robustness against purification defenses, outperforming existing gradient-based attack models in both effectiveness and efficiency.

Related papers

• Improving Transferable Targeted Attacks with Feature Tuning Mixup [12.707753562907534]
  Deep neural networks exhibit vulnerability to examples that can transfer across different models. We propose Feature Tuning Mixup (FTM) to enhance targeted attack transferability. Our method achieves significant improvements over state-of-the-art methods while maintaining low computational cost.
  arXiv  Detail & Related papers  (2024-11-23T13:18:25Z)
• Pixel Is Not A Barrier: An Effective Evasion Attack for Pixel-Domain Diffusion Models [9.905296922309157]
  Diffusion Models have emerged as powerful generative models for high-quality image synthesis, with many subsequent image editing techniques based on them. Previous works have attempted to safeguard images from diffusion-based editing by adding imperceptible perturbations. Our work proposes a novel attacking framework with a feature representation attack loss that exploits vulnerabilities in denoising UNets and a latent optimization strategy to enhance the naturalness of protected images.
  arXiv  Detail & Related papers  (2024-08-21T17:56:34Z)
• Multi-granular Adversarial Attacks against Black-box Neural Ranking Models [111.58315434849047]
  We create high-quality adversarial examples by incorporating multi-granular perturbations. We transform the multi-granular attack into a sequential decision-making process. Our attack method surpasses prevailing baselines in both attack effectiveness and imperceptibility.
  arXiv  Detail & Related papers  (2024-04-02T02:08:29Z)
• RigorLLM: Resilient Guardrails for Large Language Models against Undesired Content [62.685566387625975]
  Current mitigation strategies, while effective, are not resilient under adversarial attacks. This paper introduces Resilient Guardrails for Large Language Models (RigorLLM), a novel framework designed to efficiently moderate harmful and unsafe inputs.
  arXiv  Detail & Related papers  (2024-03-19T07:25:02Z)
• Mutual-modality Adversarial Attack with Semantic Perturbation [81.66172089175346]
  We propose a novel approach that generates adversarial attacks in a mutual-modality optimization scheme. Our approach outperforms state-of-the-art attack methods and can be readily deployed as a plug-and-play solution.
  arXiv  Detail & Related papers  (2023-12-20T05:06:01Z)
• Introducing Foundation Models as Surrogate Models: Advancing Towards More Practical Adversarial Attacks [15.882687207499373]
  No-box adversarial attacks are becoming more practical and challenging for AI systems. This paper recasts adversarial attack as a downstream task by introducing foundational models as surrogate models.
  arXiv  Detail & Related papers  (2023-07-13T08:10:48Z)
• LEAT: Towards Robust Deepfake Disruption in Real-World Scenarios via Latent Ensemble Attack [11.764601181046496]
  Deepfakes, malicious visual contents created by generative models, pose an increasingly harmful threat to society. To proactively mitigate deepfake damages, recent studies have employed adversarial perturbation to disrupt deepfake model outputs. We propose a simple yet effective disruption method called Latent Ensemble ATtack (LEAT), which attacks the independent latent encoding process.
  arXiv  Detail & Related papers  (2023-07-04T07:00:37Z)
• Model-Agnostic Meta-Attack: Towards Reliable Evaluation of Adversarial Robustness [53.094682754683255]
  We propose a Model-Agnostic Meta-Attack (MAMA) approach to discover stronger attack algorithms automatically. Our method learns the in adversarial attacks parameterized by a recurrent neural network. We develop a model-agnostic training algorithm to improve the ability of the learned when attacking unseen defenses.
  arXiv  Detail & Related papers  (2021-10-13T13:54:24Z)
• Adaptive Feature Alignment for Adversarial Training [56.17654691470554]
  CNNs are typically vulnerable to adversarial attacks, which pose a threat to security-sensitive applications. We propose the adaptive feature alignment (AFA) to generate features of arbitrary attacking strengths. Our method is trained to automatically align features of arbitrary attacking strength.
  arXiv  Detail & Related papers  (2021-05-31T17:01:05Z)
• A Perceptual Distortion Reduction Framework for Adversarial Perturbation Generation [58.6157191438473]
  We propose a perceptual distortion reduction framework to tackle this problem from two perspectives. We propose a perceptual distortion constraint and add it into the objective function of adversarial attack to jointly optimize the perceptual distortions and attack success rate.
  arXiv  Detail & Related papers  (2021-05-01T15:08:10Z)
• Adversarial example generation with AdaBelief Optimizer and Crop Invariance [8.404340557720436]
  Adversarial attacks can be an important method to evaluate and select robust models in safety-critical applications. We propose AdaBelief Iterative Fast Gradient Method (ABI-FGM) and Crop-Invariant attack Method (CIM) to improve the transferability of adversarial examples. Our method has higher success rates than state-of-the-art gradient-based attack methods.
  arXiv  Detail & Related papers  (2021-02-07T06:00:36Z)

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.