Full spectrum Unlearnable Examples via Spectral Equalization

• URL: http://arxiv.org/abs/2606.26719v1
• Date: Thu, 25 Jun 2026 07:53:53 GMT
• Title: Full spectrum Unlearnable Examples via Spectral Equalization
• Abstract summary: We propose Full-spectrum Unlearnable examples via Spectral Equalization (FUSE)<n>FUSE generates spectrum-agnostic perturbations by equalizing the contributions from different bands and enforcing cross-band consistency.<n>Experiments across multiple datasets, architectures, and spectral filtering demonstrate the strong protection achieved by FUSE.
• Score: 29.22443621401928
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unlearnable examples (UEs) protect training data by injecting imperceptible perturbations so that models fail to extract exploitable representations. In this paper, we reveal that existing UEs exhibit a critical failure once low-pass filtering is applied, indicating that the effective perturbation signals for unlearnability concentrate predominantly in high frequencies. Hence, we argue that reliable UEs should remain effective across the full spectrum. To this end, we propose Full-spectrum Unlearnable examples via Spectral Equalization (FUSE), which aims to generate spectrum-agnostic perturbations by equalizing the contributions from different bands and enforcing cross-band consistency. Specifically, FUSE adopts a Random Spectral Masking (RSM) strategy during generator training, which randomly removes a contiguous frequency band, forcing the remaining bands to maintain unlearnability. In addition, FUSE further integrates Cross-Band Guidance (CBG), which enforces mutual consistency between high- and low-frequency components, thereby further enhancing low-frequency unlearnability and regulating high-frequency perturbations to preserve the semantic fidelity of images. Extensive experiments across multiple datasets, architectures, and spectral filtering demonstrate the strong protection achieved by FUSE.

Related papers

• SpecGradFilter: A Spectral Gradient Filtering Framework for Taming Federated Heterogeneity [57.84199526590779]
  Federated Learning (FL) is fundamentally challenged by statistical heterogeneity, where non-identically distributed (non-IID) data induces client drift that hampers global convergence.<n>In this work, we revisit client drift from a novel frequency-domain perspective and uncover a critical Spectral Bias of Drift.<n>Motivated by this, we propose SpecGradFilter, a unified Spectral Filtering Gradient Framework that tames heterogeneity by suppressing discordant low-frequency signals.
  arXiv  Detail & Related papers  (2026-07-05T09:23:35Z)
• FedFFT: Taming Client Drift in Federated SAM via Spectral Perturbation Filtering [23.709933555412757]
  We propose Federated learning with Frequency-domain Filtering of SAM perturbations (FedFFT)<n>FedFFT is a lightweight and plug-and-play method that filters out low-frequency components of SAM perturbations without requiring additional communication.
  arXiv  Detail & Related papers  (2026-07-05T08:33:38Z)
• Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation [58.33732779555114]
  We argue that the key bottleneck is a representation-level failure caused by two coupled heterogeneities.<n>We propose Dynamic Spectral Denoising with Global-Context Attention for Multi-Behavior Recommendation (SpectraMB)<n>SpectraMB achieves the best results in most evaluation settings and exhibits improved robustness under noisy interactions.
  arXiv  Detail & Related papers  (2026-06-01T15:58:13Z)
• Frequency-Domain Regularized Adversarial Alignment for Transferable Attacks against Closed-Source MLLMs [59.973833105841685]
  A key challenge for improving adversarial transferability is to effectively capture the intrinsic visual focus shared across different models.<n>We propose FRA-Attack, which addresses both challenges from a unified frequency-domain regularization perspective.<n>For feature alignment, a high-pass DCT objective on patch features suppresses redundant global structures and concentrates the loss on the high-frequency band that carries the MLLMs' intrinsic visual focus.<n>For gradient optimization, we introduce Frequency-domain Gradient Regularization (FGR), a textitmodel-agnostic low-pass regularizer that modulates the surrogate gradient using
  arXiv  Detail & Related papers  (2026-05-20T08:15:56Z)
• A Practical Noise2Noise Denoising Pipeline for High-Throughput Raman Spectroscopy [0.0]
  The approach relies on a one-dimensional convolutional autoencoder trained using a Noise2Noise strategy.<n>From a reduced training subset composed of repeated short-exposure acquisitions, the model learns to reconstruct Raman spectra while efficiently suppressing noise.<n>Results demonstrate that integration times as short as 5 ms per spectrum, which are typically insufficient for reliable interpretation, yield denoised spectra with high fidelity to the reference data.
  arXiv  Detail & Related papers  (2026-05-18T15:03:43Z)
• Contrastive Spectral Rectification: Test-Time Defense towards Zero-shot Adversarial Robustness of CLIP [68.44229678548298]
  Contrastive Spectral Rectification (CSR) is an efficient test-time defense against adversarial examples.<n>CSR outperforms the SOTA by an average of 18.1% against strong AutoAttack.<n>CSR exhibits broad applicability across diverse visual tasks.
  arXiv  Detail & Related papers  (2026-01-27T05:24:45Z)
• Structured Spectral Reasoning for Frequency-Adaptive Multimodal Recommendation [13.886659472425393]
  Multimodal recommendation aims to integrate collaborative signals with heterogeneous content such as visual and textual information.<n>These issues are often exacerbated by naive fusion or shallow modeling strategies, leading to degraded generalization and poor robustness.<n>We propose a Structured Spectral Reasoning framework for frequency-aware multimodal recommendation.
  arXiv  Detail & Related papers  (2025-12-01T07:39:28Z)
• SpectrumFM: Redefining Spectrum Cognition via Foundation Modeling [65.65474629224558]
  We propose a spectrum foundation model, termed SpectrumFM, which provides a new paradigm for spectrum cognition.<n>An innovative spectrum encoder that exploits the convolutional neural networks is proposed to effectively capture both fine-grained local signal structures and high-level global dependencies in the spectrum data.<n>Two novel self-supervised learning tasks, namely masked reconstruction and next-slot signal prediction, are developed for pre-training SpectrumFM, enabling the model to learn rich and transferable representations.
  arXiv  Detail & Related papers  (2025-08-02T14:40:50Z)
• Robust Spectral Fuzzy Clustering of Multivariate Time Series with Applications to Electroencephalogram [6.62414474989199]
  We introduce a fuzzy clustering framework in the spectral domain to extract frequency-specific monotonic relationships across variables.<n>Our method takes advantage of dominant frequency-based cross-regional connectivity patterns to improve clustering accuracy.<n>As a flagship application, we analyze electroencephalogram recordings, where our approach uncovers frequency- and connectivity-specific markers of latent cognitive states.
  arXiv  Detail & Related papers  (2025-06-28T12:02:01Z)
• LSCD: Lomb-Scargle Conditioned Diffusion for Time series Imputation [55.800319453296886]
  Time series with missing or irregularly sampled data are a persistent challenge in machine learning.<n>We introduce a different Lombiable--Scargle layer that enables a reliable computation of the power spectrum of irregularly sampled data.
  arXiv  Detail & Related papers  (2025-06-20T14:48:42Z)
• Zero-Shot Hyperspectral Pansharpening Using Hysteresis-Based Tuning for Spectral Quality Control [5.231219025536678]
  Methods for hyperspectral pansharpening often overlook the unique challenges posed by hyperspectral data fusion.<n>A single lightweight neural network is used, with weights that adapt on the fly to each band.<n>The proposed method is fully unsupervised, with no prior training on external data, flexible, and low-complexity.
  arXiv  Detail & Related papers  (2025-05-22T13:24:24Z)

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.