# Full spectrum Unlearnable Examples via Spectral Equalization

arXiv: https://arxiv.org/abs/2606.26719 · HTML: https://mlsanity.com/paper/2606.26719/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 41 of its 67 references that are in the map.
- First posted on arXiv 25 Jun 2026.

## Builds on (most cited first, 30 of 41)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [Shortcut Learning in Deep Neural Networks](https://mlsanity.com/paper/2004.07780/) (2020)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness](https://mlsanity.com/paper/1811.12231/) (2018)
- [Unlearnable Examples: Making Personal Data Unexploitable](https://mlsanity.com/paper/2101.04898/) (2021)
- [Availability Attacks Create Shortcuts](https://mlsanity.com/paper/2111.00898/) (2021)
- [Which Shortcut Cues Will DNNs Choose? A Study from the Parameter-Space Perspective](https://mlsanity.com/paper/2110.03095/) (2021)
- [When Source-Free Domain Adaptation Meets Learning with Noisy Labels](https://mlsanity.com/paper/2301.13381/) (2023)
- [Autoregressive Perturbations for Data Poisoning](https://mlsanity.com/paper/2206.03693/) (2022)
- [Transferable Unlearnable Examples](https://mlsanity.com/paper/2210.10114/) (2022)
- [Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples](https://mlsanity.com/paper/2301.01217/) (2022)
- [Robust Unlearnable Examples: Protecting Data Against Adversarial Learning](https://mlsanity.com/paper/2203.14533/) (2022)
- [On Learning Fairness and Accuracy on Multiple Subgroups](https://mlsanity.com/paper/2210.10837/) (2022)
- [One-Pixel Shortcut: on the Learning Preference of Deep Neural Networks](https://mlsanity.com/paper/2205.12141/) (2022)
- [What Can We Learn from Unlearnable Datasets?](https://mlsanity.com/paper/2305.19254/) (2023)
- [Multimodal Unlearnable Examples: Protecting Data against Multimodal Contrastive Learning](https://mlsanity.com/paper/2407.16307/) (2024)
- [Transferable Adversarial Attacks on SAM and Its Downstream Models](https://mlsanity.com/paper/2410.20197/) (2024)
- [On the Benefits of Attribute-Driven Graph Domain Adaptation](https://mlsanity.com/paper/2502.06808/) (2025)
- [Purify Unlearnable Examples via Rate-Constrained Variational Autoencoders](https://mlsanity.com/paper/2405.01460/) (2024)
- [Provably Unlearnable Data Examples](https://mlsanity.com/paper/2405.03316/) (2024)
- [Structure-Preserving Graph Representation Learning](https://mlsanity.com/paper/2209.00793/) (2022)
- [EAGLE: Elevating Geometric Reasoning through LLM-empowered Visual Instruction Tuning](https://mlsanity.com/paper/2408.11397/) (2024)
- [Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need](https://mlsanity.com/paper/2410.03644/) (2024)
- [Detection and Defense of Unlearnable Examples](https://mlsanity.com/paper/2312.08898/) (2023)

## Built on by (most cited first, 3 of 3)

- [Unlearnable Faces: Privacy Protection Surviving Extraction Pipeline](https://mlsanity.com/paper/2607.05996/) (2026)
- [MUGEN: Generating Unlearnable Graph Examples for Multiple Learning Tasks](https://mlsanity.com/paper/2609.00696/) (2026)
- [ARM: Attention with Routed-Memory for Learnable Sparse Control](https://mlsanity.com/paper/2609.24417/) (2026)

## Abstract (arXiv)

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.