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Guided Diffusion Model for Adversarial Purification
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With wider application of deep neural networks (DNNs) in various algorithms and frameworks, security threats have become one of the concerns. Adversarial attacks disturb DNN-based image classifiers, in which attackers can intentionally add imperceptible adversarial perturbations on input images to fool the classifiers. In this paper, we propose a novel purification approach, referred to as guided diffusion model for purification (GDMP), to help protect classifiers from adversarial attacks. The core of our approach is to embed purification into the diffusion denoising process of a Denoised Diffusion Probabilistic Model (DDPM), so that its diffusion process could submerge the adversarial perturbations with gradually added Gaussian noises, and both of these noises can be simultaneously removed following a guided denoising process. On our comprehensive experiments across various datasets, the proposed GDMP is shown to reduce the perturbations raised by adversarial attacks to a shallow range, thereby significantly improving the correctness of classification. GDMP improves the robust accuracy by 5%, obtaining 90.1% under PGD attack on the CIFAR10 dataset. Moreover, GDMP achieves 70.94% robustness on the challenging ImageNet dataset.
Forward citations
Cited by 14 Pith papers
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Demystifying Adversarial Robustness in Diffusion Models: Compression, Randomness, and Geometry
Diffusion models improve adversarial robustness mainly by compressing the input space, while the large gains reported earlier mostly come from evaluation randomness.
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VideoPure is a diffusion-based adversarial purification framework that combines temporal DDIM inversion, spatial-temporal optimization, and multi-step voting to defend video recognition models.
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A diffusion model's conditional and unconditional scores can be combined to generate transferable adversarial perturbations for segmentation without a segmentation victim model.
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IDATA: Scalable Invertible Diffusion for Unrestricted Adversarial Transfer Attack
IDATA combines an EDICT-style invertible diffusion path with wavelet low-frequency latent constraints to generate unrestricted transferable adversarial examples with reduced GPU memory.
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Enhancing Adversarial Robustness with Signed Distance Fields for Harmonizing Geometric Invariance and Texture
A classifier trained with SDF-based shape guidance and stochastic appearance debiasing is claimed to reach 81.64% robust accuracy under AutoAttack on ImageNet, but the evaluation protocol inflates the result.
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NAPPure: Adversarial Purification for Robust Image Classification under Non-Additive Perturbations
By modeling the attack as a known transformation with unknown parameters, NAPPure jointly recovers the clean image and the perturbation through likelihood maximization, beating additive-only purification baselines on ...
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IAP: Invisible Adversarial Patch Attack through Perceptibility-Aware Localization and Perturbation Optimization
A perceptibility-aware placement step plus a color-preserving perturbation update produces targeted adversarial patches that evade both human observers and six published patch defenses while keeping attack success rates high.
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Towards Effective and Efficient Adversarial Defense with Diffusion Models for Robust Visual Tracking
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Adversarially Robust AI-Generated Image Detection for Free: An Information Theoretic Perspective
A training-free detector-side defense, TRIM, flips predictions flagged by entropy and KL-divergence thresholds, reporting large robustness gains on ProGAN, GenImage, and SDv1.4.
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Random Sampling for Diffusion-based Adversarial Purification
A maximally random variant of DDIM sampling, combined with guidance applied to the predicted clean image, yields a diffusion purification defense (DiffAP) that outperforms prior methods on CIFAR-10.
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A diffusion-based framework reconstructs attacked RSSI feature spectra in a simulated low-altitude ISAC scenario, reporting up to 87% relative SSIM improvement and a 44% average.
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