Class-Conditioned Transformation for Enhanced Robust Image Classification

• URL: http://arxiv.org/abs/2303.15409v2
• Date: Mon, 04 Nov 2024 19:05:51 GMT
• Title: Class-Conditioned Transformation for Enhanced Robust Image Classification
• Authors: Tsachi Blau, Roy Ganz, Chaim Baskin, Michael Elad, Alex M. Bronstein,
• Abstract summary: We propose a novel test-time threat model algorithm that enhances Adrial-versa-Trained (AT) models. Our method operates through COnditional image transformation and DIstance-based Prediction (CODIP) The proposed method achieves state-of-the-art results demonstrated through extensive experiments on various models, AT methods, datasets, and attack types.
• Score: 19.738635819545554
• License:
• Abstract: Robust classification methods predominantly concentrate on algorithms that address a specific threat model, resulting in ineffective defenses against other threat models. Real-world applications are exposed to this vulnerability, as malicious attackers might exploit alternative threat models. In this work, we propose a novel test-time threat model agnostic algorithm that enhances Adversarial-Trained (AT) models. Our method operates through COnditional image transformation and DIstance-based Prediction (CODIP) and includes two main steps: First, we transform the input image into each dataset class, where the input image might be either clean or attacked. Next, we make a prediction based on the shortest transformed distance. The conditional transformation utilizes the perceptually aligned gradients property possessed by AT models and, as a result, eliminates the need for additional models or additional training. Moreover, it allows users to choose the desired balance between clean and robust accuracy without training. The proposed method achieves state-of-the-art results demonstrated through extensive experiments on various models, AT methods, datasets, and attack types. Notably, applying CODIP leads to substantial robust accuracy improvement of up to $+23\%$, $+20\%$, $+26\%$, and $+22\%$ on CIFAR10, CIFAR100, ImageNet and Flowers datasets, respectively.

Related papers

• 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)
• Model Inversion Attacks Through Target-Specific Conditional Diffusion Models [54.69008212790426]
  Model attacks (MIAs) aim to reconstruct private images from a target classifier's training set, thereby raising privacy concerns in AI applications. Previous GAN-based MIAs tend to suffer from inferior generative fidelity due to GAN's inherent flaws and biased optimization within latent space. We propose Diffusion-based Model Inversion (Diff-MI) attacks to alleviate these issues.
  arXiv  Detail & Related papers  (2024-07-16T06:38:49Z)
• FACTUAL: A Novel Framework for Contrastive Learning Based Robust SAR Image Classification [10.911464455072391]
  FACTUAL is a Contrastive Learning framework for Adversarial Training and robust SAR classification. Our model achieves 99.7% accuracy on clean samples, and 89.6% on perturbed samples, both outperforming previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-04-04T06:20:22Z)
• Enhancing Targeted Attack Transferability via Diversified Weight Pruning [0.3222802562733786]
  Malicious attackers can generate targeted adversarial examples by imposing human-imperceptible noise on images. With cross-model transferable adversarial examples, the vulnerability of neural networks remains even if the model information is kept secret from the attacker. Recent studies have shown the effectiveness of ensemble-based methods in generating transferable adversarial examples.
  arXiv  Detail & Related papers  (2022-08-18T07:25:48Z)
• Attackar: Attack of the Evolutionary Adversary [0.0]
  This paper introduces textitAttackar, an evolutionary, score-based, black-box attack. Attackar is based on a novel objective function that can be used in gradient-free optimization problems. Our results demonstrate the superior performance of Attackar, both in terms of accuracy score and query efficiency.
  arXiv  Detail & Related papers  (2022-08-17T13:57:23Z)
• Threat Model-Agnostic Adversarial Defense using Diffusion Models [14.603209216642034]
  Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks. Deep Neural Networks (DNNs) are highly sensitive to imperceptible malicious perturbations, known as adversarial attacks.
  arXiv  Detail & Related papers  (2022-07-17T06:50:48Z)
• Towards Alternative Techniques for Improving Adversarial Robustness: Analysis of Adversarial Training at a Spectrum of Perturbations [5.18694590238069]
  Adversarial training (AT) and its variants have spearheaded progress in improving neural network robustness to adversarial perturbations. We focus on models, trained on a spectrum of $epsilon$ values. We identify alternative improvements to AT that otherwise wouldn't have been apparent at a single $epsilon$.
  arXiv  Detail & Related papers  (2022-06-13T22:01:21Z)
• Distributed Adversarial Training to Robustify Deep Neural Networks at Scale [100.19539096465101]
  Current deep neural networks (DNNs) are vulnerable to adversarial attacks, where adversarial perturbations to the inputs can change or manipulate classification. To defend against such attacks, an effective approach, known as adversarial training (AT), has been shown to mitigate robust training. We propose a large-batch adversarial training framework implemented over multiple machines.
  arXiv  Detail & Related papers  (2022-06-13T15:39:43Z)
• Robust Binary Models by Pruning Randomly-initialized Networks [57.03100916030444]
  We propose ways to obtain robust models against adversarial attacks from randomly-d binary networks. We learn the structure of the robust model by pruning a randomly-d binary network. Our method confirms the strong lottery ticket hypothesis in the presence of adversarial attacks.
  arXiv  Detail & Related papers  (2022-02-03T00:05:08Z)
• 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)
• RAIN: A Simple Approach for Robust and Accurate Image Classification Networks [156.09526491791772]
  It has been shown that the majority of existing adversarial defense methods achieve robustness at the cost of sacrificing prediction accuracy. This paper proposes a novel preprocessing framework, which we term Robust and Accurate Image classificatioN(RAIN) RAIN applies randomization over inputs to break the ties between the model forward prediction path and the backward gradient path, thus improving the model robustness. We conduct extensive experiments on the STL10 and ImageNet datasets to verify the effectiveness of RAIN against various types of adversarial attacks.
  arXiv  Detail & Related papers  (2020-04-24T02:03:56Z)

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.