Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness

• URL: http://arxiv.org/abs/2408.08502v1
• Date: Fri, 16 Aug 2024 03:01:07 GMT
• Title: Efficient Image-to-Image Diffusion Classifier for Adversarial Robustness
• Authors: Hefei Mei, Minjing Dong, Chang Xu,
• Abstract summary: Diffusion models (DMs) have demonstrated great potential in the field of adversarial robustness. DMs require huge computational costs due to the usage of large-scale pre-trained DMs. We introduce an efficient Image-to-Image diffusion classifier with a pruned U-Net structure and reduced diffusion timesteps. Our method achieves better adversarial robustness with fewer computational costs than DM-based and CNN-based methods.
• Score: 24.465567005078135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Diffusion models (DMs) have demonstrated great potential in the field of adversarial robustness, where DM-based defense methods can achieve superior defense capability without adversarial training. However, they all require huge computational costs due to the usage of large-scale pre-trained DMs, making it difficult to conduct full evaluation under strong attacks and compare with traditional CNN-based methods. Simply reducing the network size and timesteps in DMs could significantly harm the image generation quality, which invalidates previous frameworks. To alleviate this issue, we redesign the diffusion framework from generating high-quality images to predicting distinguishable image labels. Specifically, we employ an image translation framework to learn many-to-one mapping from input samples to designed orthogonal image labels. Based on this framework, we introduce an efficient Image-to-Image diffusion classifier with a pruned U-Net structure and reduced diffusion timesteps. Besides the framework, we redesign the optimization objective of DMs to fit the target of image classification, where a new classification loss is incorporated in the DM-based image translation framework to distinguish the generated label from those of other classes. We conduct sufficient evaluations of the proposed classifier under various attacks on popular benchmarks. Extensive experiments show that our method achieves better adversarial robustness with fewer computational costs than DM-based and CNN-based methods. The code is available at https://github.com/hfmei/IDC.

Related papers

• Counterfactual Image Generation for adversarially robust and interpretable Classifiers [1.3859669037499769]
  We propose a unified framework leveraging image-to-image translation Generative Adrial Networks (GANs) to produce counterfactual samples. This is achieved by combining the classifier and discriminator into a single model that attributes real images to their respective classes and flags generated images as "fake" We show how the model exhibits improved robustness to adversarial attacks, and we show how the discriminator's "fakeness" value serves as an uncertainty measure of the predictions.
  arXiv  Detail & Related papers  (2023-10-01T18:50:29Z)
• Fine-grained Recognition with Learnable Semantic Data Augmentation [68.48892326854494]
  Fine-grained image recognition is a longstanding computer vision challenge. We propose diversifying the training data at the feature-level to alleviate the discriminative region loss problem. Our method significantly improves the generalization performance on several popular classification networks.
  arXiv  Detail & Related papers  (2023-09-01T11:15:50Z)
• Traditional Classification Neural Networks are Good Generators: They are Competitive with DDPMs and GANs [104.72108627191041]
  We show that conventional neural network classifiers can generate high-quality images comparable to state-of-the-art generative models. We propose a mask-based reconstruction module to make semantic gradients-aware to synthesize plausible images. We show that our method is also applicable to text-to-image generation by regarding image-text foundation models.
  arXiv  Detail & Related papers  (2022-11-27T11:25:35Z)
• Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks [154.31827097264264]
  Adversarial training is a popular defense strategy against attack threat models with bounded Lp norms. We propose Dual Manifold Adversarial Training (DMAT) where adversarial perturbations in both latent and image spaces are used in robustifying the model. Our DMAT improves performance on normal images, and achieves comparable robustness to the standard adversarial training against Lp attacks.
  arXiv  Detail & Related papers  (2020-09-05T06:00:28Z)
• Defending Adversarial Examples via DNN Bottleneck Reinforcement [20.08619981108837]
  This paper presents a reinforcement scheme to alleviate the vulnerability of Deep Neural Networks (DNN) against adversarial attacks. By reinforcing the former while maintaining the latter, any redundant information, be it adversarial or not, should be removed from the latent representation. In order to reinforce the information bottleneck, we introduce the multi-scale low-pass objective and multi-scale high-frequency communication for better frequency steering in the network.
  arXiv  Detail & Related papers  (2020-08-12T11:02:01Z)
• DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning [122.51237307910878]
  We develop methods for few-shot image classification from a new perspective of optimal matching between image regions. We employ the Earth Mover's Distance (EMD) as a metric to compute a structural distance between dense image representations. To generate the important weights of elements in the formulation, we design a cross-reference mechanism.
  arXiv  Detail & Related papers  (2020-03-15T08:13:16Z)
• I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively [135.7695909882746]
  We name the MAximum Discrepancy (MAD) competition. We adaptively sample a small test set from an arbitrarily large corpus of unlabeled images. Human labeling on the resulting model-dependent image sets reveals the relative performance of the competing classifiers.
  arXiv  Detail & Related papers  (2020-02-25T03:32:29Z)
• Image Fine-grained Inpainting [89.17316318927621]
  We present a one-stage model that utilizes dense combinations of dilated convolutions to obtain larger and more effective receptive fields. To better train this efficient generator, except for frequently-used VGG feature matching loss, we design a novel self-guided regression loss. We also employ a discriminator with local and global branches to ensure local-global contents consistency.
  arXiv  Detail & Related papers  (2020-02-07T03:45:25Z)

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.